Jump to main content

Professorship Artificial Intelligence

Research Seminar

Research seminars are presently cancelled.

The seminar is intended for interested students in their Master or final stages of Bachelor studies. However, other interested listeners are cordially welcomed too! In the seminar, either the staff of the professorship Artificial Intelligence or students will present current research topics. The presentations are normally hold in English. This term, the seminar will be hold mainly on Mondays and Thursdays. The precise schedule is presented in the following table.

Information for Bachelor and Master students

Seminar presentations of each major can be hold in the frame of this event. This includes mainly the "Hauptseminar" in the major Bachelor-IF/AIF and the "Forschungsseminar" in all Master majors of Informatik as well as SEKO Bachelor and Master. Attendees acquire research-oriented knowledge by themselves and present it during a talk. The research topics will be typically from the field of Artificial Intelligence, Neurocomputing, Deep Reinforcement learning, Neurokognition, Neurorobotic and intelligent agents in virtual reality. Interested students should write an Email to Prof. Hamker, the talk itself will be scheduled after an individual consultation.

Recent Events

Auswirkungen von Distraktoren auf überwachte und unüberwachte Lernverfahren

Michael Göthel

Mon, 3. 2. 2020, Room 204

In dieser Arbeit wurde untersucht, welche Auswirkungen ein Störfaktor, der in dieser Arbeit als Distraktor bezeichnet wird, auf das Lernen zweier untersuchter Netze, ein Deep Convolutional Neuronal Network (DCNN) und ein unüberwacht lernendes Netz, hat. Bei den verwendeten Netzen handelt es sich zum einen um das von LeCun et al. (1998) vorgestellte LeNet-5 als DCNN, zum anderen wird das bereits von Kolankeh (2018) vorgestellte Netz als unüberwachtes Netz genutzt. Es wurden die Klassifikationen der Netze sowohl mittels der Layer-Wise Relevance Propagation (LRP) untersucht und außerdem die Aktivitäten der Neuronen selbst. Dabei konnten bereits frühere Resultate, beispielsweise von Lapuschkin et al. (2019), welche eine starke Anfälligkeit eines DCNN für einen solchen Distraktor gezeigt haben, nachempfunden werden. Es konnten allerdings dieselben Eigenschaften auch für das unüberwachte Netz festgestellt werden. In verschiedenen Resultaten, welche mit und ohne Hilfe des Klassifikators erstellt wurden, konnten hier Hinweise gefunden werden, welche auf eine ähnliche Beeinflussung des unüberwachten Netzes durch den Distraktor schließen lassen.

Federated Pruning of Semantic Segmentation Networks Based on Temporal Stability

Yasin Baysidi

Mon, 27. 1. 2020, Room 204

Deep Convolutional Neural Networks (DCNN) are used widely in autonomous driving applications for perceiving the environment based on camera inputs. These DCNNs are used particularly for semantic segmentation and object detection. However, the number of trainable parameters in such networks is high and can be reduced without decreasing the overall performance of the network. On the other side, the performance of these networks are always assessed with conventional assessment methods, such as mean Intersection over Union (mIoU), or loss, and not towards other requirements, like stability, robustness, etc. Based on that, we propose a novel temporal stability evaluation metric and also study the impact of removing parts of the trained network, which tend to be unstable after training. This master thesis consists of two parts: 1) a novel method to define the temporal stability of semantic segmentation methods with sequential unlabeled data named Temporal Coherence, and 2) a novel pruning method, which reduces the complexity of the networks towards temporal stability enhancement named Stability based Federated Pruning. In the coarse of our experiments, two semantic segmentation networks, Fully Convolutional Networks FCN8-VGG16 and Full Resolution Residual Network (FRRN) are trained on two data sets, Cityscapes [9] and a Volkswagen Group internal data set. Afterwards, they are pruned with two state-of-the-art pruning methods along with our proposed method, and evaluated on Intersection over Union as a supervised and our Temporal Coherence as an unsupervised evaluation metric. It is shown in the experiments that the overall performance (mIoU) and the Temporal Coherence of the networks improved after pruning up to more than 40 percent of the network parameters. Furthermore, we have shown that we could produce competitive results by our pruning metric compared to the other state-of-the-art pruning methods in all the experiments, and outperformed them in some of cases.

Cooperative Machine Learning for Autonomous Vehicles

Sebastian Neubert

Thu, 23. 1. 2020, Room 368

Automated driving has been around for more than half a century till now and the approaches vary noticeably in the car industry. While some manufacturers and research institutions rely on a combination of multiple sensors like Lidar, Radar, Sonar, GPS and Camera, Elon Musk, the CEO of Tesla, is convinced to solve fully autonomous driving by primarily solving vision, as inspired by what human beings are using to make driving decisions, i.e. vision in first place. Current state-of-the-art approaches for object detection are entirely based on machine learning techniques. These involve training very complex models on huge amounts of data centralized in large-scale datacenters. Due to the fact that in modern applications like autonomous driving and edge computing, data is usually generated in a decentralized way, a feasible consideration would be to also train the machine learning models in a decentralized manner. In this thesis we examine a distributed learning approach called Federated Learning (FL) by applying it on several scenarios with MNIST as the dataset. In these settings, multiple clients are led to personalize on a specific digit whose models are then aggregated into an average model. We have made an in-depth analysis of how this algorithm is performing on these scenarios. Additionally, we propose several ways of improving the accuracy up to 97 % on the test set as well as the consideration that the principles of FL are not limited to neural network based learning algorithms but, for instance, can also be applied to SVMs.

A neuro-computational approach for attention-guided vergence control using the iCub

Torsten Follak

Mon, 13. 1. 2020, Room 204

In this thesis a combined model for attention-guided vergence control is presented. The model consists of two prior models. One model for vergence control by Gibaldi et al. (2017), implementing a biologically inspired approach for the robotic vergence control. The second part is a model for object localization by Beuth (2017), which is inspired by the attention mechanisms in the human brain. The connection of these two models should lead to a new model with an attention guidance mechanism for the vergence control. This thesis presents first the grounding models. Further, the necessary adaptions for the model fusion are shown. Finally, the performance of the new model is tested in different settings.

Real time head pose estimation received from 3D sensor using neural networks

Dhanakumaresh Subramani

Thu, 9. 1. 2020, Room 368

Human-machine non-verbal communication can be inferred from the human head pose tracking. Therefore, human head pose estimation is very crucial in person-specific services such as automotive safety. Bayesian filters like Kalman filter is one among the efficient visual object tracking algorithm. The popularity of Kalman filter is because of its inherent feedback loop, which can predict the forthcoming measurements. Nevertheless, it cannot be used widely because of its complex design, and it has to be micro specific to the task. Recent studies in RNN (Recurrent Neural Network) prove that it could be an ideal replacement for the Bayesian filters as the temporal information in RNN has a significant influence in the field of visual object tracking. The feedback loop in RNN allows storing the temporal state information of the entire sequences of the event. Additionally, RNN can perform the functionalities of CNN (Convolutional Neural Network) and Kalman filter. Moreover, notable improvements in CNN architectures are studied, such as learning multiple related tasks (human head pose estimation, facial landmark localization and visibility estimation) improves the accuracy of the main task. Hence, in this thesis, a recurrent multi-tasking network is designed, which can estimate the head orientation along with facial landmark localization.

Modeling goal-directed navigation based on the integration of place cells in the hippocampus and action selection in the striatum

Daniel Lohmeier von Laer

Fri, 13. 12. 2019, Room 132

In my master thesis, two computational models of brain areas involved in navigation, precisely a hippocampal place-cell model and a model of the basal ganglia, were investigated, and a link between them has been established. The hippocampal formation supposedly serves as a flexible cognitive map required for orientation and navigation. Whereas, the basal ganglia presumably map stimuli to responses and are supposed to be used for action selection and suppression. Hippocampal place-cells do not only code for an animal's current position but also shift forward at choice points along possible future paths. This information alone is not enough for action selection, as it requires an interface to motor areas, which is assumed to be represented in the nuclei of the basal ganglia. For the modeled link, a mapping of the information that is relevant for navigation from the place-cell model to the basal ganglia model had to be found. Furthermore, the basal ganglia model had to learn to read sequences and classify them into discrete directional categories. There were two setups for the experiments to test the created algorithm: a T-maze and a plus-maze. Both received two possible sequences that were to be converted into a left- or right-decision.

Visual semantic planning using deep successor representations

Anh-Duc Dang

Mon, 9. 12. 2019, Room 204

In this seminar, I will present the paper 'Visual Semantic Planning Using Deep Successor Representations' by Zhu et. al (2017). Visual semantic planning (VSP) is the task of predicting a sequence of actions in a visual dynamic environment that moves an agent from a random initial state to a given goal state. Given a task like 'putting a bowl in the sink', through interaction with a visual environment, an agent needs to learn about the visual environment's object affordance and possible set of actions (as well as all their preconditions and post-effects). First, I will give a brief introduction to important reinforcement learning concepts and then explain in detail the paper's model and Zhu et. al's approach of solving the VSP problem in the challenging THOR visual environment. The main idea of the model is successor representations, which can be considered as a trade-off between model-based and model-free reinforcement learning. The idea of successor representations is not recent (Dayan, 1993) but attracted a lot of attention in the neuroscientific, machine learning or deep reinforcement learning communities in recent years.

Wireless Human Body Motion Tracking Using IMU

Keyur-Meghjibhai Hapaliya

Thu, 5. 12. 2019, Room 368

Wearable human motion capture systems are developed for continuous recording and tracking of the human body. Continuous monitoring of these people could be carried out by wearable sensor systems even though in an unknown environment. Inertial measurement unit (IMU) is very well known for human body motion tracking analysis because of compact in size and low cost. Hence, this research project represents a wireless network of the Inertial measurement unit (IMU) for human body motion tracking. Several BNO55-Adafruit sensors have been attached to the human body. Each wireless module, by 'Espressif Systems', transmits sensor's data to another module using wireless communication protocol such as User datagram protocol (UDP) and Transmission control protocol (TCP). Serial data feeds into an animation to visualise real-time motion of the human body.

Erkennung von nonverbalen Geräuschen mit Machine Learning Ansätzen in Audio-Signalen

Max Rose

Mon, 2. 12. 2019, Room 204

In dieser Bachelorarbeit untersuche ich das Erkennen von nonverbalen Geräuschen in beliebigen Audio-Signalen mittels Deep Learning, einem jungen Feld des maschinellen Lernens. Die großen Herausforderungen bestehen in der Sammlung von ausreichend Daten, der richtigen Aufbereitung dieser sowie dem Design und Training der Netze. Konkret verwende ich hierfür Convolutional-Neural-Networks, Fully-Convolutional-Neural-Networks und Convolutional-Recurrent-Neural-Networks in einem Ensemble. Um ausreichend Trainingsdaten zu generieren entwickle ich eine Methode zur Datenaugmentierung. Um das Rauschen der Ausgabe zu entfernen entwickle ich einen Algorithmus, der sich die zeitliche Abhängigkeit des Audio-Signals zunutze macht. Das Ergebnis meiner Arbeit kann für die binäre Klassifizierung zwischen 'Menschliche Stimme' und 'Nonverbales Geräusch' eines belieben Audio-Signals verwendet werden, hat eine Genauigkeit von über 95% auf den Testdatensatz und ist auf die deutsche und englische Sprache trainiert.

Processing of automotive signals for fault diagnosis using an automated probabilistic Machine Learning Framework

Nishant Gaurav

Thu, 28. 11. 2019, Room 368

The study of automotive signals for fault diagnosis can be proved substantial for securing human life. These automotive signal contains data which can be mined for information retrieval. This mining of information can provide us specifications which can be useful for fault detection and diagnosis. This thesis focuses on finding the parameters and approaches that can be applied to a data set to extract signal traces that lead to a precise event. Signal traces are extracted with the help of different processes that are in the framework of (Mrowca, 2020). Best model for every process is searched for which in turn needs the best parameters for building those models. The work starts with searching the best parameters for clustering the signals which further is used for learning a network from which paths can be determined for the causes of the event. Parameters for learning the network are estimated and the best structure is used to define the network. Paths in these networks will provide specifications and the likelihood for each path is calculated. The path that will provide the maximum likelihood is chosen as our specification. Three data set is provided and each data set contains a physical experience. Specifications for these experiences are mined out.

Spatial cognition in a virtual environment: Robustness of a large scale neuro-computational model

Micha Burkhardt

Mon, 25. 11. 2019, Room 204

Spatial cognition is a key aspect of human intelligence. It allows a person to process behaviorally relevant features in a complex environment and to update this information during processes of eye and body movements. To unfold the underlying mechanisms of spatial cognition is not only paramount in the fields of biology and medicine, but also constitutes an important framework for the development of cognitive agents, like robots working in real-world scenarios. In my thesis, a large scale neuro-computational model of spatial cognition is used on a virtual cognitive agent called Felice. The model simulates object recognition and feature-based attention in the ventral stream, supported by spatial attention in the dorsal pathway. Furthermore the visual models are complemented by a model of spatial memory and imagery of medial temporal and retrosplenial areas. Multiple novel features, which were independently developed in other works, were integrated to further improve the performance of the model. This enables the agent to reliably navigate in a familiar environment, perform object localizations and recall positions of objects in space. After an overview of the structure and functionality of the model, an insight into the ongoing evaluation of the features and robustness will be given.

Increasing the robustness of deep neural nets against the physical world adversarial perturbations

Indira Tekkali

Thu, 14. 11. 2019, Room 368

Adversarial examples denote changes to data such as images which are imperceptible (or at least innocuous) for a human but can fool a machine learning model into misclassifying the inputs. Recent work has shown that such adversarial examples can be made sufficiently robust such that they remain adversarial (fool a classifier) even when placed as artifacts in the physical world. This may pose a security threat for autonomous systems (for e.g. recognition system, detection system etc) such as autonomous cars. Many approaches for increasing robustness of models against adversarial examples have been suggested but with limited success so far. However, the space of physical-world adversarial examples is much more constrained (i.e. smaller) than the general set of adversarial examples. Thus, increasing the robustness against these physical world adversarial examples might be more viable. The goal of this thesis is to assess which of the existing methods are particularly suited for defending against physical-world adversarial examples. Firstly we have decided to use the projected gradient descent (PGD) attack along with Expectation Over Transformation (EOT) to generate the physical adversaries also we tested with other attacks such as FGSM and BIM but compare to the PGD attack FGSM and BIM are weaker so we neglected, though BIM is some what better compare to the FGSM but its not strong enough to generate the stronger perturbations moreover it require more amount of time to generate adversaries. For defending the model against physical adversaries we have selected the adversarial training. We ran our simulations on two different types of dataset such as CIFAR-10 and ImageNet dataset (200 classes with an image size of 224X224). Due to the current lack of a standardized testing method, we propose a evaluation methodology, we evaluate the efficiency of physical adversaries by simply attacking the model without EOT and we achieved 57.47% as adversarial accuracy on CIFAR-10 and 64.67% on ImageNet and when we attack the model with EOT along with the PGD we received 72.64% on CIFAR-10 and 67.84% on ImageNet.

4D object tracking using 3D and 2D camera systems

Shubham Turai

Mon, 4. 11. 2019, Room 131

4D object tracking is the tracking of objects in real-world by considering the 3D position and movement of the object in time. When using 2D camera systems, like traditional monocular cameras, it is considered that all objects are moving on the ground plane or a constant horizontal plane. The objects to be tracked are detected using You Only Look Once (YOLO) (an object detection algorithm), and these detections are used to track the object. The performance of the tracking is mostly dependent on the accuracy of the detection algorithm. Different 2D trackers are shown: Centroid Tracker, an extension of Centroid Tracker using a well known single object Kernelized Correlation Filter (KCF) tracker, an extension of Centroid Tracker using Consensus-based Matching and Tracking (CMT) Tracker, the Simple Online and Realtime Tracking (SORT) and DeepSORT (a feature-based extension of SORT). The SORT is a spatial tracking algorithm which uses Kalman filtering to make predictions and corrects the detections later. 3D versions of Centroid Tracker, KCF Centroid tracker, SORT, and DeepSORT are implemented to study how the added spatial dimension improves the reliability of the trackers. The extended 2D algorithms and all the 3D algorithms were devised by author. An algorithm to transform the 2D detections to 3D detections is explained, and also the accuracy of the resultant 3D detections is checked. In this thesis, I study the importance of 3D detections (from world coordinates) upon 2D detections (from image plane) to track objects. I also study the importance of feature and spatial tracking, upon just spatial tracking. The 2D YOLO detections are converted to 3D using Visual geometry, considering that the objects are moving on the ground plane. The 2D bounding boxes are projected to get 3D bounding boxes to track the objects in 3D space. Having known the camera position and velocity at all times by using Inertial Measurement Unit (IMU) and Gyroscope so that the system provides a complete pose (position, altitude, and time) information. The existing camera intrinsic and extrinsic transformation matrices allow transforming from image coordinates into 3D coordinates. When using 3D (stereo) camera systems the position can be calculated like above, but the assumption that the objects are on the ground (a co-planar surface) is no longer required because the stereo camera provides the missing depth information.

Simulating Tourette Syndrome. A Neurocomputational Approach to Pathophysiology.

Carolin Scholl

Wed, 30. 10. 2019, Room 132

The talk starts with an overview of the symptomatology of Tourette syndrome and current hypotheses regarding the pathophysiology of tics, focusing on potentially disturbed signaling in the cortico-basal ganglia-thalamo-cortical loops. Based on the analogy between tics and habits from both a reinforcement-learning and neurophysiological perspective, a recent behavioral finding is highlighted: unmedicated adult Tourette patients responded towards devalued outcomes at a higher rate than healthy control subjects, which was interpreted as predominant habitual behavior in the outcome devaluation paradigm (Delorme et al., 2015). This behavioral effect can be replicated using a novel neurocomputational model with multiple, hierarchically organized cortico-basal ganglia-thalamo-cortical loops. First, I will explain the task modelling and introduce the ?healthy? model that successfully reproduces the behavior of healthy control subjects in the study. Next, I will present the findings from several sets of experiments, which entailed the systematic variation of model parameters. Some of these ?pathological? models indeed show increased rates of response towards devalued outcomes, as observed experimentally. Specifically, the behavioral effect can be reproduced by decreasing striatal disinhibition or enhancing dopaminergic modulation in the model. Regarding dopaminergic modulation, both an increase in the difference between tonic and phasic dopamine levels and manipulating the gain of striatal neurons is effective. Further analyses of the computational model reveal that striatal disinhibition and enhanced dopaminergic modulation in the basal ganglia may both create an imbalance between the indirect pathway and the direct pathway, in favor of the latter.

Object classification based on high resolution LiDAR

Megha Rana

Mon, 28. 10. 2019, Room 132

Technological revolution and vigorous data growth can be considered as a backbone of future autonomous driving. To understand the surrounding scene more accurately, the vehicle has to rely upon an inevitable information source provided by sensors like the camera, LiDAR, radar, etc. Apart from this, LiDAR sensors are widely adapted and qualified to locate objects with better accuracy along with precision; a LiDAR is capable of producing a 3-dimensional point cloud from the environment to detect and classify the scene into different kinds of objects such as cars, cyclists, pedestrians, etc. Many research works have employed Velodyne LiDAR with 64 channels for the classification task. In this thesis, high-resolution LiDAR having 200-meter detection range and 300 vertical channels, is utilized to deal with point cloud sparsity challenge. Moreover, Velodyne HDL-64E LiDAR data from Kitti raw data recordings are taken as reference (to provide a basis for comparison with high-resolution LiDAR). And, the classification is implemented on a point cloud data with a proposed feature extraction based classification method instead of a deep learning approach (as it is limited to the huge amount of data to train a model). For that, traditional machine learning algorithms (using a supervised learning approach) such as Random Forest, Support Vector Machine and K-Nearest Neighbors are chosen. Considering an appropriate training workflow followed with data exploration and pre-processing, these algorithms are trained on the different data distribution and relevant features. Furthermore, offline and online (such as city and highway scene) classification results per each algorithm under different categories are evaluated on the basis of the mean accuracy evaluation metric. To discuss about results, Random Forest and Support Vector Machine achieved the best overall mean accuracy (from all 3 feature sets including 4 classes such as car, cyclist, motorbike, pedestrian) around 87 to 91% for offline as well as online highway scene while receiving 71 to 80% for the online city scene.

What determines the parallel performance of spiking neural networks?

Helge Ülo Dinkelbach

Mon, 28. 10. 2019, Room 204

The size and complexity of the neural networks investigated in computational neuroscience are increasing, leading to a need for efficient neural simulation tools to support their development. Many neural simulators are available for the development of spiking models, even though their performance can largely differ which was shown in several performance studies (e. g. Vitay et al. 2015, Dinkelbach et al. 2019, Stimberg et al. 2019). In this talk I want to go into more detail on the (parallel) implementation of core functions required by the simulation of spiking neural networks. For these functions I will demonstrate the influence of different aspects such as hardware platform, data structures and vectorization.

Scalable Deep Reinforcement Learning Algorithm for Multi-Agent Traffic Scenarios

Niyathipriya Pasupuleti

Mon, 21. 10. 2019, Room 131

Autonomous cars are soon realizable on streets of modern day world. This gives scope to many advantages as well as disadvantages on traffic of roads. The project work of this Thesis associates with analyzing the behaviour of many such agents in mixed autonomy on particular traffic scenarios by simulation. The activity includes development of a Simulation environment for particular traffic scenarios. Autonomous cars, referred as agents are trained using a Deep reinforcement learning algorithm and simulated for various traffic scenarios. The behaviour of multiple agents in mixed autonomy traffic is analyzed. The traffic flow is desired to be stabilized through inclusion of trained agents. It is also influential through different parameters and environment used. Each Reinforcement learning agent is trained with a policy and awarded with different rewards. Scalable algorithm for training the agent is determined through rewards and performance analysis of RL agent.

Active Learning for Semantic Segmentation of Automotive Images

Geethu Jacob

Thu, 17. 10. 2019, Room 131

Convolutional Neural Networks perform computer vision tasks with high accuracy. However, these networks need an immense amount of data to do so. Collection of such a high amount of data is a difficult task. Labelling the collected data is even more tedious and time-consuming. Active learning is a field in machine learning which tries to select a subset of relevant samples from a pool of unlabelled data such that, a model trained with the subset can perform equivalent to one trained with the entire dataset. Thus, active learning helps in reducing the amount of labelling done as only the selected samples to be annotated. Semantic segmentation is a scene understanding task which assigns a class to every pixel in an image. This requires per-pixel labelling for all images. Active learning heuristics could be used to limit the annotation performed for segmentation. Two active learning strategies: a core-set approach which treats active learning as a cover set problem and uncertainty sampling which selects the samples which a model is not confident about are experimented for semantic segmentation. Monte Carlo dropout is used to select the uncertain samples. Results of two active learning methods are compared to the baseline where samples are selected at random from the dataset. Further analysis of the samples selected by each strategy and possible improvements in the future are also presented.

Models for Object Recognition and Localisation with visual attention and Spike-timing-dependent plasticity

Tingyou Hao

Thu, 17. 10. 2019, Room 368

Object recognization and localization are always two important tasks in computer vision. Attention is used as a spatial pre-selection stage in the computer vision. The model, which was proposed by Beuth and Hamker, treats the view of attention as cognitive, holistic control process. The primary vision (V1) extracts the features of input image. And the higher visual areas (HVA) organize the target object, The activates cells in the prefrontal cortex (PFC) encode the target object's type. And a recurrent loop, from Higher Visual Areas to Frontal Eye Field (FEF), and afterwards back to Higher Visual Areas, enhances the position with soft-competition. The target object is localized, if the position reaches the threshold. It demonstrates that neuro-computational attention models can be applied in the realistic computer vision tasks. Based on the recent discoveries, a nuero-computational approach - Spatial updating of attention across eye movements, which is proposed by J. Bergelt and F. H. Hamker,illustrates the lingering effect from the late updating of the proprioceptive eye position signal and remapping from the early corollary discharge signal. The results provide a comprehensive framework to discuss multiple experimental observations occuring around saccades. The model, STDP-based (Spike-timing-dependent plasticity) spiking deep neural network (SDNN), consists of a temporal-coding layer followed by a cascade of consecuitive convolutional and pooling layers. In the first layer, the visual information of input image is encoded in the temporal order of the spikes. In the convolutional layers, neurons integrate input spikes and emit a spike after reaching their thresholds. The visual features are learned in these convolutional layers with STDP. Pooling layers translate invariance and compact the visual information and transfer the compacted information to next layer. At the end, a classifier with support vector machine (SVM) detects the category of the input image. The models and the approach analyze the attention in the human brain in un- and supervised learning types.

Vorhersage des Kundenverhaltens mithilfe von maschinellem Lernen

Christian Dehn

Thu, 19. 9. 2019, Room 132

Kundenanfragen bei einem Energieunternehmen, das Strom- und Gastarife anbietet, sollen in möglichst kurzer Zeit bearbeitet werden. Dadurch verringern sich die Kosten und gleichzeitig steigt die Kundenzufriedenheit. Chatbots sind fur diese Aufgabe jedoch nicht immer eine Alternative. Daher soll herausgefunden werden, ob sich mithilfe von maschinellem Lernen erkennen lässt, ob, wann, wie und warum sich ein Kunde bei dem Unternehmen meldet. Dafur wurden die Daten mit Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP) und T-distributed Stochastic Neighbor Embedding (t-SNE) untersucht. Die ersten beiden Techniken erzeugten Vorhersagen fur den Kundenkontakt. Diese waren allerdings nicht nutzbar, da bei der Validierung in keinem Fall ein korrektes Ergebnis vorlag. Daraufhin wurden die Daten mit t-SNE analysiert. Daraus entstandene Ergebnisse waren ungeordnet, da in den Datensätzen keine Strukturen erkannt wurden. Alle drei Verfahren ließen erkennen, dass die vorhandenen Kundendaten scheinbar zufällig angeordnet sind oder dass die notwendigen Informationen, zur Beantwortung der zu Grunde liegenden Fragen, nicht in den Datensätzen verfugbar waren. Daher war es mit den vorliegenden Kunden- und Kontaktdaten nicht möglich, den Kundenkontakt korrekt vorherzusagen.

Augmented Reality based Deep Learning for Pedestrian Detection

Srinivas Reddy Mudem

Wed, 18. 9. 2019, Room 132

Pedestrian detection is a challenging task in machine learning and a significant amount of research is going on this. Over the years, many researchers have proposed a wide variety of solutions to enhance the performance of pedestrian detection. But the majority of these approaches relies on a large amount of dataset. These approaches demand manually labeled training data to train a pedestrian classifier. However, collecting and labeling images from the real world is time consuming and expensive. To overcome such issues of collecting and manually labeling real image training data, an efficient approach is presented in this thesis work. This thesis investigates the possibilities to generate augmented data with automatic labeling. Instead of considering the whole environment for augmentation, this thesis work proposes an alternative paradigm which considers only an interesting object for augmentation. These augmented objects are merged with real images. Creating only interesting objects instead of the whole environment makes the generation process easy, cost-effective, and also it keeps the data more real. This work presents an efficient way of augmenting images with synthetic objects. A complete pipeline to generate automatic training data with the label will be presented. In addition, training of augmented dataset is demonstrated using state of the art deep learning networks and tested on different types of real datasets to find out the flaws of trained models. Our experiments and results illustrate that the model trained on synthetic data generalize better than real data, which is having limited annotation. Through a wide range of experiments, we conclude that the right set of parameters to generate the augmented dataset enhance the performance of the network.

Laufzeit und Präzisionsanalyse von Künstlichen Neuronalen Netzen in einer Predictive Maintenance Umgebung

Justin Colditz

Wed, 11. 9. 2019, Room 336

Das Ziel dieser Arbeit ist es, zu verstehen welchen Einfluss die verschiedenen Parameter von Künstlichen neuronalen Netzen (KNN) und deren Datengrundlage auf die Dauer des Lernens und das endgültige Ergebnis haben. Dabei wird sowohl auf die einzelnen Freiheitsgrade als auch auf deren Zusammenhang untereinander eingegangen. Die zugrunde liegenden Daten beziehen sich auf die maschinellen Ausfälle eines Reifenherstellers. Um die Parameter auf deren Einfluss zu überprüfen werden einige Methoden ausprobiert. Der Großteil der Arbeit besteht allerdings darin, verschiedene Werte zu testen und deren Ergebnisse miteinander zu vergleichen. Anhand dieses Vergleiches werden die Werte angepasst und erneut getestet ob sich das Netz durch die Anpassung verbessert hat. Die Ergebnisse zeigen, dass sich vieles bei KNN durch Tests und Ausprobieren verbessern lässt. Es gibt selten die eine Methode, welche direkt einen genauen Wert liefert. Wenn überhaupt können einige Parameter maximal auf bestimmte Bereiche eingeschränkt werden. Anhand dieser Bereiche, kann dann mithilfe von weiteren Tests herausgefunden werden, welcher Wert sich am besten eignet. Künstliche neuronale Netze sind viel zu speziell, um allgemeine Aussagen treffen zu können. Jedes Netz ist eigen und benötigt verschiedene Einstellungen. Des Weiteren haben die Daten, welche zum Trainieren verwendet werden, einen weitaus höheren Einfluss als erwartet wurde. Es ist daher empfehlenswert sich mit den Daten und dem gewünschten Aufbau des Netzes ausgiebig auseinander zu setzen bevor mit dem Training begonnen wird, um bestmögliche Ergebnisse für jedes Problem zu ermöglichen.

Precise feature gain tuning in visual search tasks - the basal ganglia makes it possible: A neuro-computational study

Oliver Maith

Mon, 26. 8. 2019, Room 132

In visual search tasks a target item should be found among several distractors. A common theory is that when looking for a known target, the visual attention is directed to the features of the target, thus enhancing the response to the target in the visual system. In a study by Navalpakkam and Itti (2007) it was shown that subjects in a visual search task, in which the target was very similar to the distractors, did not learn to direct the attention directly to the features of the target, as this would also enhance the response to the distractors. In this work, the visual search task of Navalpakkam and Itti (2007) was performed with a biologically plausible neural model. For this a model of the visual system (Beuth, 2017) was combined with a model of the basal ganglia (Villagrasa, Baladron, Hamker, 2016). In this combined model, the basal ganglia receive information about the visual input through the inferior temporal cortex (IT) and control the activity of the prefrontal cortex (PFC). Activity in the PFC causes feature-based attention in the visual model. The combined model can select certain items of the visual input through feature-based and spatial attention and learns to direct the feature-based attention to certain features due to reward. The findings of Navalpakkam and Itti (2007) could be replicated with the model. The model thus demonstrates a very interesting, so far poorly investigated function of the basal ganglia, which could also explain other findings about the effects of reward on attention.

Revealing the impairments of thalamic lesions using Bayesian adaptive direct search for a neurocomputational model of saccadic suppression of displacement

Adrian Kossmann

Mon, 26. 8. 2019, Room 132

In recent years a series of studies were published emphasizing the role of the thalamus in visual stability across saccades (Cavanaugh et al., 2016; Ostendorf et al., 2013; Tanaka, 2005). It is believed that visual stability across saccades is established by a corollary discharge signal which is present in the thalamus and a proprioceptive eye position signal (Wurtz, 2018). Using a neurocomputational model for perisaccadic vision (Ziesche & Hamker, 2011) the impact of thalamic lesions on corollary discharge and proprioception was investigated. Model parameters were tuned to psychometric functions of subjects with focal thalamic lesions that participated in saccadic suppression of displacement experiments (Ostendorf et al., 2013) using Bayesian adaptive direct search (Acerbi & Ma, 2017). By applying a cluster analysis on the fitted model parameters a single-case analysis was conducted and parameters impaired by thalamic lesions were revealed. After giving a brief background overview and introducing the used methods, results will be presented and discussed.

Introduction to selected derivative-free optimization algorithms

Sebastian Neubert

Mon, 5. 8. 2019, Room 131

Derivatives, or its adaptation called gradient descent, is all over the place in different fields of machine learning, especially in neural networks when using backpropagation to optimize a NN. In most cases the calculation of such gradients is computationally very expensive or not even possible. As an alternative to the state-of-the-art optimization algorithms based on derivatives, I would like to give a brief overview about optimization algorithms which do not depend on any form of a derivative and are therefore called derivative-free optimization algorithms. In this talk the focus will be put on evolutionary strategies, especially on NEAT (NeuroEvolution of Augmenting Topologies) with an outlook on Particle Swarm Optimization later on as a second example of a derivative-free optimization algorithm.

Role of the Recurrent Architecture of the Cerebellum: a Computational Study of Forward Models using Recurrent Dynamics and Perturbation Learning

Katharina Schmid

Wed, 31. 7. 2019, Room 132

The cerebellum is generally believed to be involved in motor learning and motor control. Many tasks in this domain require precise timing and coordinated activity of different body parts. Recent computational models have turned to reservoir computing in order to describe how the cerebellum encodes spatiotemporal information and adapts to the requirements of different tasks. The present study extends this work by using perturbation learning as a more biologically plausible learning mechanism. A network consisting of a recurrent reservoir and feedforward output layers is trained to model the forward dynamics of a 2-joint planar arm and to generate adaptively timed responses to different inputs. These simplified versions of tasks that may be attributed to the cerebellum illustrate the network?s ability to learn using biologically plausible learning mechanisms.

Acceptance of Occupational E-Mental Health - A cross-sectional study within the Third German Sociomedical Panel of Employees, GSPE-III

Julian Thukral

Tue, 9. 7. 2019, Room 131

Mental disorders have become one of the leading causes for workplace absence and long-term work incapacity. Occupational e-mental health (OEMH) tools such as internet-and mobile-based interventions (IMI) provide a promising addition to regular health care. Yet, acceptance of e-mental health is low. Goal of the study was to identify drivers and barriers of acceptance of OEMH. Cross-sectional data of N=1829 participants were collected from the first and second wave of the Third Sociomedical Panel of Employees ? GSPE-III, including a self-designed survey. Participants consisted of employees with high risk of early retirement due to health reasons. The Unified Theory of Acceptance and Use of Technology (UTAUT) was modified and extended to research drivers and barriers of acceptance. 86.3% of the participants (n=1579) indicated low to moderate acceptance of IMIs, while n=193 (10.6%) indicated high acceptance (M= 2.07, SD= 1.04, Range= 1-5). Path analysis confirmed that the UTAUT predictors performance expectancy (beta= .47, p= <.001), effort expectancy (beta= .20, p= <.001), and social influence (beta= .26, p= <.001) significantly predicted acceptance. Strongest extended predictors of acceptance identified were health related internet use (Total: beta= .29, p= <.001), previous experience with e-mental health (Total: beta= .24, p= <.001), and gender (Total: beta= -.11, p= <.001). Acceptance of OEMH in the sample can be considered as low. UTAUT predictors were found to significantly predict acceptance in the OEMH setting and new extended predictors were identified. To improve implementation of OEMH within regular health care, performance expectancy, effort expectancy, health related internet use, and experience must be facilitated.

Hierarchical representations of actions in multiple basal ganglia loops part II: Computational benefits and comparison to other models

Javier Baladron

Mon, 8. 7. 2019, Room 131

In this talk I will continue with the analysis of the hierarchical model I presented in my last seminar. I will initially remind you of the principal characteristics of the model and then move to new results which show the computational benefits of the hierarchical organization we are proposing. In our new experiments the model was able to transfer information across tasks. I will then compare our approach to other neuro-computational models which also target the organization of the multiple basal ganglia loops. Finally I will discuss how the model relates to reinforcement learning and how it may be extended.

Chaotic neural networks: FORCE learning and Lyapunov spectra

Janis Goldschmidt

Mon, 17. 6. 2019, Room 132

The FORCE learning algorithm by Sussillo and Abbott renewed interest in reservoir computing and using dynamic systems for machine learning purposes. This talk will give a short introduction to the FORCE learning algorithm, to the chaotic neural nets it is applied to, and how to probe their dynamic structure in the hopes to understand their computational ability.

Erkennung des Diskomforterlebens des Fahrers beim hochautomatisierten Fahren anhand von Blick- und Fahrdaten mit Maschinellen Lernverfahren

Roghayyeh Assarzadeh

Wed, 12. 6. 2019, Room 132

Mit dem Durchbruch der Fahrautomatisierung gewinnen Fragen der Mensch-Maschine-Interaktion (HMI), wie der Komfort beim automatisierten Fahren, zunehmend an Aufmerksamkeit. In diesem Zusammenhang zielt das Forschungsprojekt KomfoPilot an der Technischen Universität Chemnitz darauf ab, Diskomfort in einem automatisierten Fahrzeug anhand von physiologischen, Umwelt- und Fahrzeugparametern verschiedener Sensoren zu bewerten. Der Fahrsimulator erzeugt umfangreiche Datenmengen, die für die Erkennung von Diskomfort wertvoll sind. Die Suche nach einem Ansatz, der eine zuverlässige Erkennung von Diskomfort, verursacht durch ein hochautomatisiertes Fahrzeug, ermöglicht, ist sehr komplex. Fortschritte in der künstlichen Intelligenz (KI) führen zu einer schnellen Lösung von Problemen mit hoher Komplexität. KI hat Menschen bei der Berechnung komplexer Aufgaben, wie der Erkennung von Ermüdungserscheinungen eines Fahrers im Fahrzeug, der Erkennung von Diskomfort im automatisierten Fahrzeug, usw. übertroffen. Neuronale Netze bieten die Möglichkeit, diese Herausforderungen zu bewältigen und mithilfe von Fahrdaten des Simulators Diskomfort zu erkennen. Darüber hinaus werden die spezifischen Szenarien zur Erkennung von Diskomfort im Simulator berücksichtigt. Dieses Projekt hat zwei große Herausforderungen: Erstens, die besten Merkmale finden, um mithilfe neuronaler Netze Diskomfort zu erkennen. Zweitens, finden des besten Modells, das mithilfe der ermittelten Merkmale Diskomfort erkennen kann. Die Ergebnisse diese Projekts wurden in zwei Phasen bewertet: In der ersten Phase wurden MLP- und LSTM-Modelle zur Diagnose von Diskomfort vorgestellt und die Ergebnisse verglichen. In der zweiten Phase wird unter Verwendung des LSTM-Modells eine neue Architektur namens Kaskade vorgestellt. Um die Effizienz der neuen Architektur zu demonstrieren, werden zwei Untermodelle betrachtet: das erste für die Diagnose des Komforts und das zweite für die Diagnose des Diskomfort. Schließlich zeigen die Ergebnisse, dass die Kaskadenarchitektur die Erkennung von Diskomfort im Vergleich zur vorherigen Phase signifikant verbessert.

Exploring biologically-inspired mechanisms of humanoid robots to generate complex behaviors dealing with perturbations during walking

Hoa Tran Duy

Mon, 3. 6. 2019, Room 131

Humanoid robots are getting fluent in an interactive joint human-robot environment to assist people in need, e.g., home assistance, where robots will need to deal with even rougher terrain and more complicated manipulation tasks under significant uncertainty either for static or dynamically changing environments. Researches on humanoid robots have focused mainly on reliable perception, planning, and control methods for completing challenging tasks. Studies also show that, legged robots may inevitable fall over during a task in an unstructured environment, and it may end up with a serious damage for itself or the environment. While, the ideal robot, like a human, in a real-world scenario should be able to recover, stand up, and complete the task. In this project I introduce a new approach for humanoid robotics push recovery, provide robots the ability to recover from external pushes in real world scenarios. The study involves detecting falls, selecting and performing the appropriate actions to prevent a damage both on the robot and the environment around during its locomotion. Advances in studies of animal and human movements along with robotics, artificial intelligence and neural network researches are combined to develop a new paradigm in creating robust and flexible recovery behaviors. This project involves topics such as central pattern generator, robotics biped walking, unsupervised and reinforcement learning to develop a robot recovery approach inspired by biological systems.

Investigating robustness of End-to-End Learning for self-driving cars using Adversarial attacks

Vivek Bakul Maru

Mon, 27. 5. 2019, Room 131

Deep neural networks have been recently performing on high accuracy in many important tasks, most notably on image-based tasks. Current state-of-the-art image classifiers are performing at human level. Having said that, Deep learning has been successfully applied to End-to-End learning architectures for autonomous driving tasks where a deep neural network maps the pixel values coming from camera to a specific steering angle of the car. However, these networks are not robust enough to carefully perturbated image inputs known as Adversarial inputs. These inputs can severally decrease the accuracy and performance of the network which results in endangering the systems where such deep learning models are deployed. This thesis work mainly focuses on proving the vulnerability of End-to-End architectures for self-driving car towards adversarial attacks. In this work, we formulize the space of adversaries against End-to-End learning and introduce a way to generate Universal Adversarial Perturbation for these architectures. We have generated universal perturbations using distinct subsets of dataset and tested against two different models i.e. classification and regression in a White-box setting. To show the transferability and universality of these perturbations, we have tested these perturbations against a third constrained model in a Black-box setting. With the results, we showed that End-to-End architectures are highly vulnerable to perturbations and these perturbations can generate a serious threat to the system when deployed in a self-driving car. We also propose a theoretical prevention method for the End-to-End architecture against Universal perturbations to increase the security of the model.

Successor representations

Julien Vitay

Mon, 20. 5. 2019, Room 131

Successor representations (SR) are a trade-off between model-free and model-based methods in reinforcement learning. They see a revival for a couple of years through the work of Samuel Gershman and colleagues who propose a neurobiological mapping of this algorithm to the prefrontal cortex, hippocampus and basal ganglia. This seminar will explain the principle of SR, present their main neuroscientific predictions and discuss their plausibility. See

Development and analysis of active SLAM algorithms for mobile robots

Harikrishnan Vijayakumar

Mon, 13. 5. 2019, Room 375

Mobile robots are becoming an essential part of industrial and household services. In this thesis, we tackle the problem of autonomous exploration using active SLAM concepts. Autonomous exploration is the task of creating a model of the environment by the robot without human intervention. The environment model creation capabilities of the robot are highly dependent on sensor and actuator performance. We require an algorithmic approach to handle the noise from the hardware measurements. We need a combined solution for localization, mapping, and motion planning to improve autonomous exploration performance. In its classical formulation, SLAM is passive which means that the algorithm creates the map and localizes the robot passively by using received motion and sensor observations. It takes no part in deciding where to take measurements next. The quality of the map created by the SLAM depends on the correctness of position estimation of the robot while mapping. To further improve the quality of the map, it is necessary to integrate path planning with SLAM so that the algorithm can decide the future motion of the robot based on the current localization and map information for achieving better localization and a higher quality map. This thesis addresses this problem. There are many approaches that do not address the uncertainty in motion and sensor measurements. We propose a probabilistic approach that considers the current system uncertainty and plans the path of the robot that minimizes the robot?s pose and map uncertainty. This thesis first examines the background and other approaches addressing active SLAM. We implement and evaluate state of the art approaches on our own, standardized, datasets. We propose and implement a new approach based on Schirmer on path planning in belief space. Path planning in configuration space assumes accurate knowledge of the robot position, and the goal is to find a path from the position of the robot to the destination. In reality, uncertainties in movement and sensing sometimes lead to the inability to detect the accurate position of the robot. Planning in belief space maintains a probability distribution over possible states of the robot and takes these uncertainties into account. Path planning in belief space entails planning a path for the robot given the knowledge about the environment and the current belief. We compute paths in belief space by computing two representations of the environment in parallel. The first one is a classical geometric map of the environment, the other one encodes how well the robot can localize at different parts of the environment. We then apply a search algorithm to the discrete configuration space to find the best path to the target goal location with the lowest uncertainty. Finally, we define a utility function for each target goal based on its mean uncertainty and navigation cost to evaluate the best candidate for the next motion. At the end of the work, we discuss in detail the results, strengths, weaknesses, assumptions, and identify areas for future research.

Optimizing topic models on small corpora - A qualitative comparison between LDA and LDA2Vec

Anton Schädlich

Thu, 9. 5. 2019, Room 367

Topic modeling become a subfield in unsupervised learning since the invention Latent Dirichlet Allocation (LDA) algorithm. However, on smaller corpora it sometimes does not perform well in generating sufficiently coherent topics. In order to boost coherence scores, it has been extended in various researches with vector space word embeddings. The LDA2Vec algorithm is one of these symbiotic algorithms that draws context out of the word vectors and the training corpus. This presentation is about the qualitative comparison of the topics and models of optimized LDA and the LDA2Vec algorithm trained on a small corpus of 1800 German language documents with a considerably small amount of topics. The coherences were measured both on the 'combined coherence framework' and on manual observation.

Multi-GPU simulation of spiking neural networks on the neural simulator ANNarchy

Joseph Gussev

Mon, 6. 5. 2019, Room 367

Because of their highly parallel architecture and availability, GPUs have become very present in neural simulators. With the goal of outperforming CPU-based clusters with cheaper GPUs, some simulators have started to add multi-GPU support and have shown speedups compared to single-GPU implementations. The thesis behind this presentation found its goal in adding multi-GPU implementations to ANNarchy's spiking neural network simulation. After a brief background overview, the ideas behind the implemented algorithms will be presented, their performance and scalability evaluated and discussed.

A model car localization using Adaptive Monte Carlo Localization and Extended Kalman Filter

Prafull Mohite

Tue, 23. 4. 2019, Room 336

Localization works with the help of GPS. But this might not be very accurate specially in case of autonomous driving. In case of Autonomous driving, It is a key step for deciding what should be next step. As localization is a key concept to predict the next step, it should be made more accurate and reliable and this can be done using Exteroception sensors. The range of Lidar is high enough with minimum error rate, is been used for depth perception. In this thesis, a very popular technique, Simultaneous Localization and mapping (SLAM) approach is selected. Map is needed for localization and localization cannot be completed without map giving rise to chicken egg problem. Mapping the environment before localization is in common practice and we have followed that. The aim of thesis is to tryout the performance of the proposed system against the state of the art. The proposed system consist of hybrid localization consist of 3 times pose correction with non Gaussian filter and Nonparametric filters. As a result, location in Cartesian coordinate system and orientation is observed.

Investigation of Model-Based Augmentation of Model-Free Reinforcement Learning Algorithms

Oliver Lange

Mon, 15. 4. 2019, Room 131

Reinforcement learning has been successfully applied to a range of challenging problems and has recently been extended to handle large neuronal network policies and value functions. However, the sample complexity of model-free algorithms tends to limit their applicability to physical systems. The reduction of sample complexity for deep reinforcement learning algorithms is essential to make them applicable in complex environments. This presentation is about the investigation of the performance of a reinforcement learning agent which utilizes the advantages of both model-free and model-based reinforcement learning algorithms.

Interpreting deep neural network-based models for automotive diagnostics

Ria Armitha

Wed, 10. 4. 2019, Room 336

With the breakthrough of Artificial intelligence over the last few decades and extensive improvements in Deep Learning methodologies, the field of Deep Learning has gone through major changes. AI has outdone humans in computing complex tasks like object and image recognition, fault detection in vehicles, speech recognition, medical diagnosis etc. From a bird's-eye view the models are basically algorithms which try to learn concealed patterns and relationships from the data fed into it without any fixed rules or instructions. Although these models' prediction accuracies may be impressive, the system as a whole is a black-box (non-transparent). Hence, explaining the working of a model to the real world poses its own set of challenges. This work deals with interpreting vehicle fault-detection model. Current fault detection approaches rely on model-based or rule-based systems. With an increasing complexity of vehicles and their sub systems, these approaches will reach their limits in detecting fault root causes in highly connected and complex systems. Furthermore, current vehicles produce rich amounts of data valuable for fault detection which cannot be considered by current approaches. Deep Neural Networks (DNN) offer great capabilities to tackle these challenges and automatically train fault detection models using in-vehicle data. However, fault detection models based on DNNs (here, CNNs and LSTMs) are black boxes so it is nearly impossible to back-trace their outputs. Therefore, the aim of this work is to identify, implement and evaluate available approaches to interpret decisions made by DNNs applied in vehicle diagnostics. With that, decisions made by the DNN diagnostics model can be better understood to (i) comprehend the model's outputs and thus increase model performance as well as (ii) enhance their acceptability in vehicle development domain.

Extrinsic Camera Pose Estimation Using Existing Parallel Lines of the Surrounding Area

Layth Hadeed

Mon, 8. 4. 2019, Room 131

Camera pose estimation is an essential process for computer vision systems used by intelligent automotive systems. This work is concerned with the pose estimation of a train camera that is installed in the front windshield of the train. The pose is estimated by using only the rails and sleepers of the railway track without any additional calibration objects. The information extracted from the rail lines in the image helped estimate the pitch and yaw by determining the vanishing point in the world X-axis direction. The orthogonality between the rails and sleepers helped estimate the roll by using the orientation of the sleepers in the track, it was possible to estimate the value of the role. The translation parameters are calculated from the rotation matrix and the normal vectors of the projective planes between the camera center and the rails. The results show that a good approximation of the pose can be made by adding an additional step that compensates for the error in the estimated roll.

Weight estimation using sensory soft pneumatic gripper

Hardik Sagani

Mon, 25. 3. 2019, Room 132

Current soft pneumatic grippers might robustly grasp flat and flexible objects with curved surfaces without distorting them, but not estimate the properties by manipulating it to a certain extent. On the other hand, it is difficult to actively deform a complex gripper to pick up surface objects. Hence, this research project represents a prototype design of soft sensory pneumatic gripper utilized to estimate the weight of objects. An easy to implement soft gripper is proposed by soft silicon material with four fingers. It can be fabricated by placing piezoresistive material sandwiched between soft silicon rubber and conducting threads. Sixteen Pressure sensors and 4 curvature sensors of velostat piezoresistive material have been placed into the hand. The layers were designed with the help of conductive thread electrode material. Hence, the sensory gripper can evaluate the weight property of grasped objects.

Neuro-computationale Modellierung der Basal Ganglien für Stopp-Signal Aufgaben

Iliana Koulouri

Tue, 12. 3. 2019, Room 132

Baladron, Nambu und Hamker haben 2017 ein biologisch motiviertes neuro-computationales Basalganglienmodell für die Aktionsauswahl während einer Erkundungsphase nach Änderung von Umweltkonditionen durch die GPe-STN Schleife entwickelt. Die Basalganglien beeinflussen ebenso die Unterdrückung von Handlungen. Es wird angenommen, dass ein Rennen zwischen Go-Signalen und Stopp-Signalen einem Rennen zwischen den neuronalen Bahnen der BG entspricht. Dazu postulierten Schmidt et al. (2013) und Mallet et al. (2016) ein zweistufiges Abbruchmodell, welches einen Pause- und einen Abbruchmechanismus beinhaltet. Der Abbruch einer Handlung werde durch einen neu entdeckten Zelltyp, den arkypallidalen globus pallidus Zellen, realisiert. Über ihre Vernetzung sind bislang nicht alle Einzelheiten bekannt. Die vorliegende Arbeit befasst sich mit der Erweiterung des BG Modells von Baladron et al. (2017) um die Implementierung dieses postulierten zweistufigen Abbruchmodells, der Replizierung der Ergebnisse von Mallet et al. (2016) und der Untersuchung des Einflusses der Stärke der Inputsignale, sowie ihre Verzögerung auf das Modell. Es zeigt sich, dass das erweiterte Modell die Ergebnisse von Mallet al al. (2016) erfolgreich replizieren kann. Dabei scheint auch der Kortex als Quelle der Erregung der arkypallidalen globus pallidus Zellen als plausibel. Das Modell weist einen geringen Einfluss gegenüber Änderungen der Stoppraten und der zeitlichen Verzögerung zwischen Go- und Stopp-signalen auf. Allerdings erweist es sich als empfindlich gegenüber Änderungen der Goraten.

Modelling and controlling of the soft pneumatic actuator for the Elbow assistance

Sidhdharthkumar Vaghani

Tue, 12. 3. 2019, Room 132

More than a decade the wearable robotics is in the trend for the rehabilitation or for the assistance of human motion. The wearable robots are attracting the focus of the human because of its safe interaction with the human and external environment. Because of their stiffness and wearable property, wearable robots are very famous for the rehabilitation and assistance of human body joints e.g. knee rehabilitation, elbow or shoulder assistance etc. However the controlling of the soft robots is not an easy task compared to rigid robots. Mainly the wearable robots are using the air pressure as the activation parameter of their body. Therefore the aim of this thesis is to modelling and controlling of the exosuit, which is useful for the elbow assistance. The structure of the exosuit consists of two pneumatic actuator one is for elbow flexion movement and other is for extension movements. One IMU unit is mounted on the top of the exosuit to monitor the relative angle between two links of the exosuit. The modelling of the exosuit is carried out separately for flexion and extension actuator by considering the geometry of the actuators. For precise control of the actuator proportional valve has been developed and used along with the PID controller. The multi-layer multi-pattern central pattern generator (MLMP-CPG) is adapted to generate the rhythmic and non-rhythmic behaviour of the exosuit. To implement a better control strategy, the reflexes are introduced with the CPG model. At the end of the thesis work, the experiments are carried to prove the implementation of PID and reflex based controller using the MLMP-CPG. The reflexes with the CPG generate the adaptive behaviour of the exosuit.

Decorrelation and sparsification by homeostatic inhibitory plasticity in cooperation with voltage-based triplet STDP for receptive field formation

René Larisch

Mon, 4. 3. 2019, Room 202

Several recent models have shown how V1 simple-cell receptive fields may emerge from spike-timing dependent plasticity (STDP) mechanisms. These approaches mostly depend on normative learning paradigms, such as maximizing information transmission or optimizing sparseness, or on simplified rate-based learning dynamics. These assumptions make it unclear how inhibition influences neuronal properties (for example orientation selectivity and contrast invariance tuning curves) and network effects (for example input encoding). We here propose a model of V1 based on a phenomenological excitatory and an inhibitory plasticity rule. Our simulation results showing the interplay of excitatory and inhibitory plasticity on the connectivity structure and the influence on input encoding. Further, we demonstrate how the right amount of excitatory and inhibitory input leads to the occurrence of contrast invariance tuning curves.

Computing with hippocampal sequences

Prof. Dr. Christian Leibold

Fri, 15. 2. 2019, Room 336

Hippocampal place cells are activated in sequence on multiple time scales during active behavior as well as resting states. Reports of prewired hippocampal place cell sequences that were decoded as future behaviors are hard to bring into register with the suspected roles of the hippocampus in the formation of autobiographic episodic memories and spatial learning. Here, I propose a computational model, that shows how a set of predefined internal sequences that are linked to only a small number of salient landmarks in a large random maze can be used to construct a spatial map of a previously unknown maze.

A network model of the function and dynamics of hippocampal place-cell sequences in goal-directed behavior

Lorenz Gönner

Fri, 15. 2. 2019, Room 336

Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, I propose a spiking network model of spatial learning and sequence generation as interdependent processes. Simulations show that this model explains the generation of never-experienced sequence trajectories in familiar environments and highlights their utility in flexible route planning. In addition, I report the results of a detailed comparison between simulated spike trains and experimental data, at the level of network dynamics. These results demonstrate how sequential spatial representations are shaped by the interaction between local oscillatory dynamics and external inputs.

Adaption of a neuro-computational model of space perception to new physiological data and evaluation with experimental data from human and monkeys

Nikolai Stocks

Wed, 13. 2. 2019, Room 132

A. Zietsche and F.H. Hamker developed a computational model for the simulation visual perception and perisaccadic mislocalisation [A. Ziesche et al. 2011]. .A paper by Joiner et al. published in 2013 demonstrates that monkeys do perform different than humans in Saccadic Supression of Displacement (SSD) trials under the stimulus blanking condition which the model does not account for. Furthermore does data by Xu et al. 2012 show that the neural representation of current eye position updates later than the original model accounted for. The goal of this thesis is to find adjustments to the parameters of the original model and to allow it to accurately simulate SSD experiments for both monkeys and humans using Xu's revised timeframe.

Development of Collision Prevention Module using Reinforcement Learning

Arshad Pasha

Wed, 6. 2. 2019, Room 132

The tremendous advancements in the field of application of Artificial Intelligence in Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AVs) have still not been able to curb the accident rate. Many automotive manufacturers aim to achieve the 'Zero Fatalities' goal with advanced collision prevention modules by the next decade. This thesis work mainly focuses on the implementation of ADAS functionalities such as Autonomous Emergency Braking (AEB), Autonomous Emergency Steering (AES), and Adaptive Cruise Control (ACC) using Reinforcement Learning (RL). RL has attracted researchers' attention in recent years due to its immense potential. Q-Learning is one of the most exploited RL algorithms. It has not only been employed to solve a simple problem as grid world problem but also to solve complex problems as playing advanced levels video games such as Doom, Breakout and various other Atari games. This thesis work also uses the Q-learning algorithm. This thesis was carried out at the Research and Development unit of ZF known as Zukunft Mobility GmbH, Kösching, Germany. The principal objective of this thesis is to use Tabular Q-learning approach to implement complex ADAS functionalities as mentioned earlier and analyze the RL agent's behavior in addition to integrating these developed algorithms within the organization's proprietary simulation environment (SimpleSpec). Further, the continuous State-Action space has been discretized to adapt to the tabular Q learning approach which avoids the 'Curse Of Dimensionality' and 'Matrix Explosion' problems. In the penultimate part, a qualitative and quantitative evaluation of the performance has been carried out. The results obtained after a full-scale model testing for low, medium and high-speed scenarios have been recorded. In addition, random scenarios were generated to test the scope and capabilities of the trained RL agent. Further research has been carried out in the area of the impact of reward function structure on the RL agent which includes the sparse and non-sparse reward structure design and their influence on the reward convergence and learning process. Further, this segment also includes the analysis between the explicitly distributed non-sparse reward design and the non-sparse inline reward design. The final part summarizes the tasks covered, the goal achieved and draws a path to the future work.

Road scene semantic segmentation using residual factorized convnet and surround view fisheye cameras

Saiffuddin Syed

Wed, 30. 1. 2019, Room 367a

Automotive industry is continuously evolving, especially in the self-driving domain which creates a demand for new concepts to be developed, implemented and tested. At present the only sensor capable of sensing the immediate surrounding of the vehicle is a camera.This thesis addresses the 360 degrees road scene semantic segmentation problem for fisheye cameras. Present vehicles are equipped with distinct types of cameras used for various practical real-time applications, the most common camera model being the wide-angle fisheye cameras which are considered for this thesis. Usage of this camera brings two major challenges: firstly, CNN-based semantic segmentation task requires a huge amount of pixel-level annotated data. So far there is no open-source annotated dataset available for wide-angle images. Secondly, a fisheye camera introduces severe distortions and negates the positional invariance offered by a conventional pinhole camera model. To overcome this, training the images on transformed images that are augmented using a fisheye filter is proposed. An approach to integrate blocks which improve the representational power of existing architectures by explicitly modelling interdependencies between channels of convolutional features, has been tested. The experiments carried out prove the effectiveness of these blocks when augmented data is used. Much of the work presented in the thesis was devoted to a rigorous comparison of the architectures.The evaluation of the thesis is done on two different kind of datasets, a real world dataset and a synthetic dataset. The primary metric used for evaluation was the Intersection-over-Union (IoU). The results at the end of the thesis showed that a large amount of existing annotated data taken from pinhole cameras can be reused through augmentation and relatively small amount of annotated from fisheye cameras is required to account for domain shift. Further, the new architectures presented in this thesis show promising results when applied to augmented data.

A network model of the function and dynamics of hippocampal place-cell sequences in goal-directed behavior

Lorenz Gönner

Mon, 21. 1. 2019, Room 219

Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, I propose a spiking network model of spatial learning and sequence generation as interdependent processes. Simulations show that this model explains the generation of never-experienced sequence trajectories in familiar environments and highlights their utility in flexible route planning. In addition, I report the results of a detailed comparison between simulated spike trains and experimental data, at the level of network dynamics. These results demonstrate how sequential spatial representations are shaped by the interaction between local oscillatory dynamics and external inputs.

Categorizing facial emotion expressions with attention-driven convolutional neural networks

Valentin Forch

Mon, 21. 1. 2019, Room 219

The development of so-called deep machine learning techniques has brought new possibilities for the automatic processing of emotion-related information which can have great benefits for human-computer interaction. Vice versa machine learning can profit from concepts known from human information processing (e.g., visual attention). Being located in the spectrum of human and artificial intelligence, the aim of the present thesis was twofold: (a) to employ a classification algorithm for facial expressions of emotions in the form of a deep neural network incorporating a spatial attention mechanism on image data of facial emotion expressions and (b) to compare the output of the algorithm with results from human facial emotion recognition experiments. The results of this thesis show that such an algorithm can achieve state-of-the-art performance in a facial emotion recognition task. With regard to its visual search strategy some similarities with human saccading behavior emerged when the model's perceptive capabilities were restricted. However, there was only limited evidence for emotion-specific search strategies as can be found in humans.

3D reconstruction with consumer depth cameras

Manh Ha Hoang

Wed, 9. 1. 2019, Room 132

In this thesis, we develop an RGB-D camera-based system that is able to generate a 3D model of a single household object using a consumer depth (RGB-D) camera. The system then grabs textures of the object from a high-resolution DSLR camera and applies them to the reconstructed 3D model. Our approach specially addresses on generating a highly accurate 3D shape and recovering high-quality appearance of the object within a short time interval. The high-quality 3D texture object models can be used for the products of online shopping, augmented reality, and further research of 3D Machine Learning.

Hierarchical representations of actions in multiple basal ganglia loops

Javier Baladron

Wed, 5. 12. 2018, Room 132

I will introduce here three novel concepts, tested and evaluated by means of a neuro-computational model that brings together ideas regarding the hierarchical organization of the basal ganglia and particularly assigns a prominent role to plasticity. I will show how this model reproduces the results of two cognitive tasks used to measure the development of habitual behavior and introduce a model prediction.

Investigating reservoir-based reinforcement learning for robotic control

Oleksandr Nikulkov

Wed, 28. 11. 2018, Room 132

Reservoir Computing is a relatively novel approach for training recurrent neural networks. It is based on generating a random recurrent reservoir as a part of the network and training only the readout of the reservoir. This separation makes the setup easy to be implemented and offers different directions for further research to be done. Existing methods for learning cognitive tasks often require continuous reward signals, which are not always available in cognitive tasks. However, this disadvantage can be avoided by using supralinear amplification on the trace of node-perturbation weight updates to suppress the relaxation-effect, as proposed by (Miconi, 2017). In this presentation, I will show how such a network can be applied to a robotic control task and investigate the role of the different parameters.

Model Uncertainty estimation for a semantic segmentation network with a real time network deployment analysis on Nvidia Drive PX2 for Autonomous Vehicles

Abhishek Vivekanandan

Mon, 19. 11. 2018, Room 132

Autonomous vehicles require a high degree of perception capabilities in order to perceive the environment and predict objects therein at a high precision in real time. For such cases we use semantic segmentation networks. A major challenge in using semantic segmentation is determining how confident the network is in its prediction or in other words how trustworthy classification outcomes are. Integrating uncertainty estimates with semantic segmentation help us to understand the confidence measure with which a network predicts its output. Bayesian approaches along with dropouts provide us the necessary tool in deep learning to extract the uncertainty involved in the prediction from a model. In Bayesian Neural Networks, we place a distribution over the weights, giving us a probabilistic interpretation about the classification. For such networks, multiple Monte Carlo sampling is needed to generate a reliable posterior distribution from which we can infer uncertainty statistics. The serial nature of this sampling approach restricts its use in the real time environment. In this work through in-depth analysis we show the best possible places in a neural network to deploy dropouts along with the number of MC sampling which needs to be done such that we can maximize the quantifications to estimate uncertainty. We also exploit parallel capabilities of GPU to realize certain neural operations such as convolution and dropouts directly on an embedded hardware with minimal abstraction. As a result we propose the necessary alternative changes to the kernel functions needed to implement parallel Monte Carlo dropout sampling to estimate uncertainty in real-time. Finally, we provide a brief comparison in terms of benchmarking about the kernel implementations on a CPU (Intel Xeon processor) and a GPU (DrivePX2 and Nvidia Geforce 1080Ti).

Disentangling representations of grouped observations in adversarial autoencoders

Felix Pfeiffer

Wed, 14. 11. 2018, Room 131

Being able to classify the shown emotion or facial action from mere pictures of faces is a challenging task in machine learning, since simple classification requires at least reliably labeled data, which is hard to get in sufficient quantity. Unsupervised learning methods can at least in part avoid the problem of dependency from such data, by finding representations that are meaningful. In my thesis I present an algorithm that teaches an Adversarial Autoencoder how to find representations of data. With clever administration of the training process it is possible to strip information from the representation that would not be beneficial for specific tasks like classification. This process is called disentangling and the administrative strategy is to find groups of data. I will show the results of some experiments that verify that the algorithm does what it promises and elaborate on where its weaknesses may be, by training an Adversarial Autoencoder on a colorful MNIST dataset and let it produce disentangled representations that separate style from content.

Interpreting deep neural network-based models for automotive diagnostics

Ria Armitha

Wed, 7. 11. 2018, Room 131

With the breakthrough of Artificial intelligence over the last few decades and extensive improvements in Deep Learning methodologies, the field of Deep Learning has gone through major changes. AI has outdone humans in computing complex tasks like object and image recognition, fault detection in vehicles, speech recognition, medical diagnosis etc. From a bird's-eye view the models are basically algorithms which try to learn concealed patterns and relationships from the data fed into it without any fixed rules or instructions. Although these models' prediction accuracies may be impressive, the system as a whole is a black-box (non-transparent). Hence, explaining the working of a model to the real world poses its own set of challenges. This work deals with interpreting vehicle fault-detection model. Current fault detection approaches rely on model-based or rule-based systems. With an increasing complexity of vehicles and their sub systems, these approaches will reach their limits in detecting fault root causes in highly connected and complex systems. Furthermore, current vehicles produce rich amounts of data valuable for fault detection which cannot be considered by current approaches. Deep Neural Networks (DNN) offer great capabilities to tackle these challenges and automatically train fault detection models using in-vehicle data. However, fault detection models based on DNNs (here, CNNs and LSTMs) are black boxes so it is nearly impossible to back-trace their outputs. Therefore, the aim of this work is to identify, implement and evaluate available approaches to interpret decisions made by DNNs applied in vehicle diagnostics. With that, decisions made by the DNN diagnostics model can be better understood to (i) comprehend the model's outputs and thus increase model performance as well as (ii) enhance their acceptability in vehicle development domain.

Learning the Motor Program of a Central Pattern Generator for Humanoid Robot Drawing

Deepanshu Makkar

Thu, 1. 11. 2018, Room 132

In this research project, we present a framework where a humanoid robot, NAO, acquires the parameter of a motor program in a task of drawing arcs in Cartesian space. A computational model based on Central Pattern Generator is used. For the purpose of drawing a scene, geometrical features such as arcs are extracted from images using Computer Vision algorithms. The algorithm used in the project which considers only important features for the purpose of robot drawing is discussed. These arcs can be described as a feature vector. A discussion is done on how genetic algorithms help us in parameter estimation for the motor representation for selected feature vector. This understanding of parameters is used further to generalize the acquired motor representation on the workspace. In order to have a generalization for achieving a mapping between the feature vector and the motor program, we propose an approximation function using a multilayer perceptron (MLP). Once the network is trained, we present different scenarios to the robot and it draws the sketches. It is worth noting that our proposed model generalizes the motor features for a set of joint configuration, unlike the traditional way of robots drawing by connecting intermediate points using inverse kinematics.

Cortical routines - from experimental data to neuromorphic brain-like computation

Prof. Dr. Heiko Neumann (Ulm University, Inst. of Neural Information Processing)

Tue, 30. 10. 2018, Room 1/336

A fundamental task of sensory processing is to group feature items that form a perceptual unit, e.g., shapes or objects, and to segregate them from other objects and the background. In the talk a conceptual framework is provided, which explains how perceptual grouping at early as well as higher-level cognitive stages may be implemented in cortex. Different grouping mechanisms are implemented which are attuned to basic features and feature combinations and evaluated along the forward sweep of stimulus processing. More complex combinations of items require integration of contextual information along horizontal and feedback connections to bind neurons in distributed representations via top-down response enhancement. The modulatory influence generated by such flexible dynamic grouping and prediction mechanisms is time-consuming and is primarily sequentially organized. The coordinated action of feedforward, feedback, and lateral processing motivates the view that sensory information, such as visual and auditory features, is efficiently combined and evaluated within a multiscale cognitive blackboard architecture. This architecture provides a framework to explain form and motion detection and integration, higher-order processing of articulated motion, as well as scene segmentation and figure-ground segregation of spatio-temporal inputs which are labelled by enhanced neuronal responses. In addition to the activation dynamics in the model framework, steps are demonstrated how unsupervised learning mechanisms can be incorporated to automatically build early- and mid-level visual representations. Finally, it is demonstrated that the canonical circuit architecture can be mapped onto neuromorphic chip technology facilitating low-energy non-von Neumann computation.

Neural Reflexive Controller for Humanoid Robots Walking

Rishabh Khari

Thu, 25. 10. 2018, Room 131

For nearly three decades, a great amount of research emphasis has been given in the study of robotic locomotion, where researchers, in particular, have focused on solving the problem of locomotion control for multi-legged humanoid robots. Especially, the task of imitating human walking has since been the most challenging one, as bi-pedal humanoid robots implicitly experience instability and tend to topple itself over. However, recently new machine learning algorithms have been approached to replicate the sturdy, dexterous and energy-efficient human walking. Interestingly many researchers have also proposed that the locomotion principles, although run on a centralized mechanism (central pattern generator) in conjunction with sensory feedback, they can also independently run on a purely localized sensory-feedback mechanism. Therefore, this thesis aims at designing and evaluating two simple reflex-based neural controllers, where the first controller generates a locomotion pattern for the humanoid robot by combining the sensory feedback pathways of the ground and joint sensors to the motor neuron outputs of the leg joints. The second controller makes use of the Hebb's learning rule by first deriving locomotion patterns from the MLMP-CPG controller while observing the sensory feedback simultaneously and finally generating motor-neuron outputs associatively. In the end, this thesis also proposes a fast switching principle where the output to motorneurons after a certain interval is swiftly transferred from the MLMP-CPG to the associative reflex controller. This is implemented to observe adaptive behavior present for centralized locomotor systems.

Improving autoregressive deep generative models for natural speech synthesis

Ferin Thunduparambil Philipose

Wed, 24. 10. 2018, Room 132

Speech Synthesis or Text To Speech (TTS) synthesis is a domain that has been of research interest for several decades. A workable TTS system would essentially generate speech from textual input. The quality of this synthesized speech would be gauged based on how similar it sounds to the human voice and the ease of understanding it clearly. .A fully end to end neural Text-To-Speech system has been set up and improved upon, with the help of WaveNet and Tacotron deep generative models. The Tacotron network acts as a feature prediction network that outputs the log-mel spectrograms, which are in-turn utilized by WaveNet as the local conditioning features. Audio quality was improved by the logmel local conditioning and the fine-tuning of hyper-parameters such as mini-batch size & learning rate. Computational effort was reduced by compressing the WaveNet network architecture.

Fatigue detection using RNN and transfer learning

Azmi Ara

Wed, 24. 10. 2018, Room 132

Driving car is a insecure activity which requires full attention. Any distraction can lead to dangerous consequences, such as accidents. While driving, many factors are involved, such as: fatigue, drowsiness, distractions. Drowsiness is a state between alert and sleep. For this reason, it is important to detect drowsiness in advance which will help in protecting the people from accidents. The research guides us to understand an implicit and efficient approach to detect the different levels of drowsiness. Every driver has different driving patterns. The developed system should be able to adopt to the changes of driver?s behavior. The aim of this thesis is to contribute to the study of detecting drivers drowsiness levels while driving through different approaches which integrates of two sensory data to improve detection performance.

Car localization in known environments

Prafull Mohite

Tue, 2. 10. 2018, Room 131

Localization in a broader sense is very wide topic and at present basic localization takes place with the help of GPS sensor but lacks accuracy which is important for Autonomous driving. To overcome this problem, there are different environmental sensors used (typically, Sonar, Lidar, Camera). Lidar sensor being very accurate in case of depth perception is the used. In this thesis, Simultaneous Localization And Mapping (SLAM) approach is selected. SLAM, as name suggested Localization and mapping is chicken egg problem and to solve it, we are creating map of an environment before performing localization. For mapping, Gmapping and for localization within map, Adaptive Monte Carlo Localization (AMCL) is selected. AMCL is basically a particle filter. After giving a map of an environment, the algorithm estimates the position and orientation of a car as it moves and senses the environment.

Training approaches onsemantic segementation using transfer learning, dataset quality assessment and intelligent data augmentation

Mohamed Riyazudeen Puliadi Baghdad

Mon, 24. 9. 2018, Room 131

Data Sparsity is one of the key problems that automotive industries face today. One way to overcome this is to use synthetic data that are generated from graphics engines or virtual world generator, that can be leveraged to train neural networks and accomplish tasks such as autonomous driving. The features learned from synthetic data yield better performance with a suitable training approach and some real data. The number of images in the synthetic dataset, and its similarity to real world dataset play a major role in transferring the learned features effectively across domains. This similarity in the distribution of these datasets was achieved through different approaches, the most effective one being Joint Adaptation Network Approach. Also, data augmentation in a smart way could boost the performance achieved. Intelligent data augmentation was achieved using conditional Generative Adversarial Networks and Color Augmentation technique. With the findings of this research work, a possible solution for tackling data sparsity problem was achieved.

Image anonymization using GANs

Thangapavithraa Balaji

Mon, 24. 9. 2018, Room 131

Millions of images are being collected every day for applications to enable scene understanding, decision making, resource allocation and policing to ease the human life. Most of these applications doesn't require the identity of the people in the images.There is an increasing concern in these systems invading the privacy of the users and the public. On one side, the camera/robots can assist a lot in everyday life, but on the other side, the privacy of the user or the public should not be compromised. In this master thesis, a novel approach was implemented to anoymize faces in the datasets which enable privacy protection of the individuals in the datasets. The Generative Adversarial Network(GAN) approach was extended and the loss function was formulated in a combined fashion. The performance of conventional image anonymization techniques like blurring, cropping, pixelating were compared against GAN generated images using autonomous driving applications like object detection and semantic segmentation.

Investigating Model-based Reinforcement Learning Algorithms for Continuous Robotic Control

Frank Witscher

Wed, 19. 9. 2018, Room 368

Obwohl model-free, deep Reinforcement Learning eine immer größer werdende Bandbreite an Aufgaben erfüllen kann, leiden die jeweiligen Algorithmen an einer großen Ineffizienz bezüglich der dafür erforderlichen Datenmenge. Model-based Reinforcement Learning, welches ein Dynamics Model der Umwelt erlernt, verspricht hierbei Abhilfe. Jüngste Forschungen kombinieren model-free Algorithmen mit model-based Ansätzen, um die Stärken beider Reinforcement Learning-Zweige auszunutzen. In meiner Verteidigung gebe ich eine Einleitung in model-based Reinforcement Learning und einen Überblick über die mögliche Nutzung von Dynamics Models, wie sie in neusten Publikationen zu finden ist. Wir konzentrieren uns dabei auf Umgebungen mit kontinuierlichen Action Spaces, wie sie in der Robotik anzutreffen sind. Temporal Difference Model ist ein solcher Hybrid aus model-free Learning mit model-based Control. Dieser wird im Detail vorgestellt und ausgewertet.

Sensor simulation and Depth map prediction on Automotive Fisheye camera using automotive deep learning

Deepika Gangaiah Prema

Wed, 12. 9. 2018, Room 131

The aim is to create a synthetic 3D environment which enables to obtain a supervised dataset using Unity framework and simulating different sensors like lidar and fisheye camera in the simulation environment. This dataset will be used to develop, test and validate different machine learning algorithms for automotive use cases. The big advantage of the simulation environment is the possibility to generate data from different sensors which are still under development and the final hardware is still not available. Another advantage is that the known ground truth of the simulation environment. This much cheaper than equipping a vehicle with those sensors, record lots of data and manually label the ground truth by humans. The 3D environment shall include urban and highway driving scenarios with balanced object categories like vehicles, pedestrians, trucks, terrain and street or free space to cover all levels for autonomous driving The simulation of a fish eye camera such as next generation lidar will be carried out in the thesis on the same Unity 3D framework, the generated images and point cloud data are used to generate different data sets. The final goal is to use this for training different models and test them on a real environment. Qualitative test are carried out by benchmarking the data sets with the aid of different algorithms. The aim of this thesis is to study the different approaches with which CNNs could be used in the task of depth estimation from a single fisheye camera image (180 degree FoV) for Autonomous Driving.

Digital Twin Based Robot Control via IoT Cloud

Tauseef Al-Noor

Tue, 14. 8. 2018, Room 131

Digital Twin (DT) technology is the recent key technology for Industry 4.0 based monitoring and controlling industrial manufacturing and production. There are a lot of researches and development happening on DT based robot control. Monitoring and controlling the robot from a remote location is a complex process. In this research work, I have developed a prototype for controlling a robot using DT and cloud computing. Different technologies and techniques related to Digital Twin have been researched and analyzed to prepare an optimal solution based on this prototype. In this work, the latency of different types of machine to machine (M2M) communication protocols is observed. Different type of network protocols such as AMQP, MQTT, and HTTP has a lot of latency variation in the end to end data transfer communication. Furthermore, different external factors impact on persistent communication. For example, the cloud computing service as like as Azure?s data processing and throughput is not constant-time. A robot controlling mechanism expects a minimum constant time response for the quality of service. In this research, the main focus was to minimize communication latencies for a remote robot controller in a cloud-based communication. Finally, an average quality of service in the range of 2-5 seconds for persistent robot communication has been achieved based on different setup.

Humanoid robot learns walking by human demonstration

Juncheng Hu

Tue, 14. 8. 2018, Room 131

In this thesis, a method designed for making the humanoid robot walking is developed by using the Q learning based on MLMP-CPG and wrist sensors. Machine learning has demonstrated a promising feature in many fields including robotics. However, the supervised learning algorithms are more often applied. However, supervised learning like neural networks always need a massive amount of data to train, which is sometimes not permitted in the real situation. Although not much data is required in reinforcement learning, it needs many attempts in its environment thus concluding a strategy. For a humanoid robot, it is not allowed to have too many wrong attempts because a fall may lead to the injury of joints. In this thesis, a method that the robot learns walking with the help of a human can avoid accidental fallings is proposed.

Vision-based Mobile Robotics Obstacle Avoidance with Deep Reinforcement Learning

Zishan Ahmed

Wed, 8. 8. 2018, Room 131

Obstacle avoidance is a fundamental and challenging problem for autonomous navigation of mobile robots. In this thesis, the problem of obstacle avoidance in simple 3D environments where the robot has to rely solely on a single monocular camera is considered. Inspired by the recent advantages of deep reinforcement learning (DRL) in Atari games and understanding highly complex situations in Go, the obstacle avoidance problem is tackled in this thesis as a data-driven end-to-end deep learning approach. An approach which takes raw images as input, and generates control commands as output is presented. The differences between discrete and continuous control commands are compared. Furthermore, a method to predict the depth images from monocular RGB images using conditional Generative Adversarial Networks (cGAN) is presented and the increase in learning performance by additionally fusing predicted depth images with monocular images is demonstrated.

Deep Convolutional Generative Adversarial Networks (DCGAN)

Indira Tekkali

Tue, 24. 7. 2018, Room 132

Generative Adversarial Networks (GAN) have made great progress in the recent years. Most of the established recognition methods are supervised, which have strong dependence on image labels. However obtaining large number of image labels is expensive and time consuming. In this project, we investigate the unsupervised representation learning method that is DCGAN. We base our work on previous paper by Radford and al., and aim to replicate their results. When training our model on different datasets such as MNIST, CIFAR-10 and Vehicle dataset, we are able to replicate some results for e.g. smooth transmission.

Using Transfer Learning for Improving Navigation Capabilities of Common Cleaning Robot

Hardik Rathod

Tue, 10. 7. 2018, Room 131

A lot of robotic vacuum cleaners fail during the cleaning task because they get stuck under furniture or within cords or some other objects on the floor. Once such situation occurs, the robot is hardly able to free itself. One possible cause of this behavior is insufficient information of the environment, the robot enters. In unstructured environments, recognition of objects has been proven to be highly challenging. By executing an analysis of the environment before the cleaning operation starts, the robot will be aware of the objects around it, especially those that might harmful in the navigation. Methods from machine learning have been investigated and tested as they give impressive results in object detection tasks. Taking adequate actions according to objects in the environment helps to overcome or reduce the possibilities to getting stuck the robot under the objects, and eventually it reduces the effort of the customers. The insight from this analysis has been incorporated within the locomotion behavior of a dummy robot.

Vergence control on humanoid robots

Torsten Follak

Mon, 9. 7. 2018, Room 131

For the orientation in the 3D-space, a good depth is needed. This estimation is reached through effective stereoscopic vision. There the disparity between both eyes images is used to derive the 3D-structure. Therefore, it is important that both eyes are fixating at the same point. This fixation is managed by vergence control. To implement and use vergence control in robotics, different approaches exits. In this talk three of them are shown. A short overview of the two first is given, while the third one is presented in detail.

Docker for machine learning

Alexander J. Knipping and Sebastian Biermann

Tue, 3. 7. 2018, Room 131

Handling software dependencies for research and or production environments often comes with a certain amount of complexity. Libraries like TensorFlow or PyTorch don't always behave in the same way across several major version releases, especially in combination with various other third-party libraries, different Python versions and CUDA toolkits. Several solutions such as anaconda, virtualenv or pyenv have emerged from the Python community, but managing those in regards to reproducibility and portability often feels clumsy and leads to unexpected errors, especially for system administrators. In this presentation we will evaluate if Docker containers can be a more efficient way to encapsulate project code with its dependencies, to build once, ship anywhere. For demonstration we have used Docker to train a machine learning model able to recognize 194 birds by their calls, through a variation of an existing, VGG based, model trained on Google's Audioset and using their feature extractor for our own classes. Training was then performed on over 80 000 audio files of ten to twenty seconds length on nucleus. We will demonstrate how we have used Docker in our workflow from developing the model, training it on the nucleus node to deploying the model into a productive environment for users to query it. The aim of our project is to provide both users and system administrators an overview of how Docker works, what its benefits and costs are and if it's a viable option to use in typical machine learning workflows and environments.

Humanoid robots learn to recover perturbation during swing motion in frontal plane: mapping pushing force readings into appropriate behaviors

Ibrahim Amer

Tue, 19. 6. 2018, Room 131

This thesis presents a learning method to tune recovery actions for humanoid robot during swinging movement based on central pattern generator. A continuous state space of robot is learned through self-organized map. A disturbance detection technique is proposed based on robot states and sub-states. Predefined recovery actions space are used in this thesis and they are composed of non-rhythmic patterns. A hill climb algorithm and a neural network have been used to tune the non-rhythmic patterns parameters to obtain the optimum values. A humanoid robot NAO was able to recover from disturbance with an adaptive reaction based on disturbance amplitude. All experiments were done on Webots simulation.

Humanoid robot grasping in 3D space by learning an inverse model of a central pattern generator

Yuxiang Pan

Tue, 19. 6. 2018, Room 131

Grasping is one of the most important functions of humanoid robots. However, an inverse kinematics model for the robot arm is required to reach an object in the workspace. This model can be mathematically described using the exact robot parameters, or it can be learned without a prior knowledge about these parameters. The later has an advantage as the learning algorithm can be generalized to other robots. In this thesis, we propose a method to learn the inverse kinematics model of NAO humanoid robot using a multilayer perceptron (MLP) neural network. Robot actions are generated through the multi-layered multi-pattern central pattern generator (CPG) model. The camera captures the information of the object provided by the ArUco markers, the MLP model provides the desired arm configurations to reach the object, and then the CPG parameters are calculated to move the arm from its current position into the goal position. The proposed model have been tested in simulation, and on the real robot where a soft sensory robotic gripper was used to interact with a human subject (tactile servoing). Grasping was done using both the learned inverse model and the sensory feedback.

Scene Understanding on a Humanoid Robotic Platform Using Recurrent Neural Networks

Saransh Vora

Wed, 13. 6. 2018, Room 131

Since near perfect levels of performance have been reached for object recognition using convolutional neural networks. The ability to describe the content and organization of a complex visual of the scene is called scene understanding. In this thesis the deterministic attention model has been used with back propagation with two different pre-trained encoder CNN models along with a RNN as a decoder to generate captions. The trained attention model is then used for a humanoid robot to describe the scene. This would represent first step towards robotic scene understanding. The robot can not only associate words with images but it can also point at the locations of features which are attended to and locate them in space.

Transferring deep Reinforcement Learning policies from simulations to real-world trajectory planning

Vinayakumar Murganoor

Tue, 5. 6. 2018, Room 131

Machine learning is really progressed a lot in recent days but most of the applications and demonstrations are done in simulated environments, especially with continuous control tasks. When it comes to continues control tasks, the reinforcement learning algorithms are proven to produce the good policies. In this project, the problem of trajectory planning is solved using reinforcement learning algorithms, where the simulated trained agent in the real-world moves the RC car from any given point A to point B with no training in real world itself. Also identified the minimum parameters that influence the agent behavior in the real world and listing out the problems and solutions found during the transfer of the policy from simulation to the real world.

Investigating dynamics of Generative Adversarial Networks (GANs)

Vivek Bakul Maru

Tue, 29. 5. 2018, Room 131

Generative Adversarial Networks (GANs) are very recent and promising approach in generative models. GANs are the approaches to solve problems by unsupervised learning using deep neural networks. GANs work on an adversarial principle, where two different neural networks are fighting with each other to get better. This research project aims to understand the underlying mechanism of GANs. GANs certainly have proved to have an edge over all the existing generative models like Variational autoencoders and Autoregressive models but they are known to suffer instability while training. Implementation research in this project focuses on investigating the issues regarding training of GANs and the convergence properties of the model. Apart from vanilla GAN, this project also focuses on the extension of regular GAN using convolutional neural networks, called Deep Convolutional GAN and one of the very recently proposed approaches called, Wasserstein GAN. Analysis of the travel of the loss functions insights into the convergence properties. Training of these models on multiple datasets allowed to compare and observe the learning of both the networks in GAN.

Design and Fabrication of Complex Sensory Structure of Curving and Pressure Sensors for Soft Robotic Hand

Vishal Ghadiya

Wed, 23. 5. 2018, Room 131

This Research project represents the prototype design of the complex sensory structure for a soft hand. This can be easily adapted to the soft material like silicon. A superposition of four piezoresistive pressure sensors and one curving sensor was arranged on the inner face of each finger. This research focuses on the design of flexible pressure and curving Sensors, in particular to the response of force sensitive resistor based pressure sensor. Thanks to the multi-layered design of Sensors, the structure was able to measure the curve of the finger and amount of tactile pressure applied by the object grasped to the hand. Sixteen pressures sensor and four curving sensors with Velostat as piezoresistive layer were designed with a diversity of electrode material i.e. conductive thread, with and without using of conductive fabric. The multilayer structure of pressure and the curving sensor can be placed on the inner face of the soft hand and easily evaluate the properties of the object such as size and stiffness of the object.

Erforschung der Rolle von Aufmerksamkeit in rekurrenten Modellen von deep reinforcement learning

Danny Hofmann

Wed, 9. 5. 2018, Room 1/131

Viele Probleme erfordern das erlernen einer Strategie direkt auf den Ausgabedaten der Umgebung, wie Pixelbilder einer Simulation oder auch Kamerabilder eines Roboterarmes. Es wird die Theorie von RAM und der Glimps-Aufmerksamkeit besprochen (Mnih, Heess und Graves 2014) dazu wurde der von Jung 2017 implementierte Algorithmus Asynchronous Attentional Advantage Actor-Critic (A4C), mit kontinuierlichen Aktionsräumen kompatibel gemacht. Die so neu gewonnene Eingabe und Ausgabe Verarbeitung wurde mithilfe eines einfach simulierten Roboterarmes getestet. Es wird Besprochen wie verschiedene Konfigurationen der Simulation zeigen, welche Informationen für den Aufmerksamkeitsmechanismus von Bedeutung sind und wie es dem Agenten gelingt einen Lösungsweg zu finden. Die Ergebnisse lassen einen Vergleich mit anderen Ansätzen des Deep Reinforcement Learnings zu. Unter anderem werden die entstandenen Ergebnisse mit den von Lötzsch 2017 entwickelten DDPG-Varianten verglichen. Es konnte mit A4C keine schnellere Lösung als mit den DDPG-Varianten gefunden werden, jedoch ist es mit A4C möglich nach dem Ende-zu-Ende-Prinzip zu lernen. Demnach lernte A4C direkt auf Bilddaten, wohingegen in den Implementierungen von Lötzsch 2017 eine Abstrahierung der Umwelt benötigt wurde.

A Framework for Adaptive Sensory-motor Coordination of a Humanoid-Robot

Karim Ahmed

Wed, 2. 5. 2018, Room 1/131

This project was done over the research area of sensory-motor coordination on humanoid robots and the ability to generate the coordination for new motor skills. The goal is to modulate the sensory motor connections that generate independently the same motor coordination demonstrated using the Central Pattern Generator (CPG). We propose two neural networks, one network for extracting the coordination features of a robot limb moving by the CPG. And the other network for creating a similar coordinated movement on the other limb moving by simple sensory-motor connections (without the CPG). Thanks to the proposed model, different coordination behaviors were presented at the sensory-motor level. A coordinated rhythmic movement generated by the CPG in the first stage can be demonstrated only by a simple sensory motor network (a reflexive controller) in the next stage where no CPG network is involved.

Knowledge Extraction from Heterogeneous Measurement Data for Datasets Retrieval

Gharbi Ala Eddine

Mon, 30. 4. 2018, Room 1/367

Due to the exponential increase of data amount produced on daily basis, innovative solutions are developed to tackle the problem of properly managing and organizing it with crisp and recent technologies such as Artificial Intelligence (AI) frameworks and Big Data Management(BDM) tools. The need for such solutions rises from the fact that our everyday interconnections soars towards pure digitalization. Therefore, leading Information Technology companies strive to come up with ideas to handle this bulky amount of data in the most efficient and concise way. The challenge faced with this huge data amount is not only to properly organize it but rather to make use of it. That is deriving knowledge from unstructured data is as important as structuring it in an effective way. Throughout this thesis, knowledge derivation techniques are applied on the available data in IAV GmbH. Data can be described as data files used to test the implemented software components and hence the importance of its proper organization. This Master thesis investigates and develops a prototypical solution for the organization of data sets as well a concept implementation for additional information extraction from data files. Different problems and solutions related to Knowledge Discovery (KD) and data organization are presented and discussed in details. Furthermore, an overview of the frameworks and algorithms used Data Minig (DM) is given as well.

Gait Transition between Simple and Complex Locomotion in Humanoid Robots

Sidhdharthkumar Vaghani

Mon, 23. 4. 2018, Room 1/367

This project presents the gait transition between the rhythmic and the non-rhythmic behavior during walking of a humanoid robot Nao. In biological studies, two kinds of locomotion behaviors were observed during cat walking on a flat terrain and walking on a ladder (simple and complex locomotion). In this work, both locomotion behaviors were produced on the robot using the multi-layers multi-patterns central pattern generator model (Nassour et al.). We generate the rhythmic behavior from the non-rhythmic one based on the frequency of interaction between the robot feet and the ground surface during the complex locomotion. While complex locomotion requires a sequence of descending control signals to drive each robot step, simple locomotion requires only a triggering signal to generate the periodic movement. The overall system behavior fits with the biological finding in cat locomotion (Marlinski et al.).

On the role of cortex-basal ganglia interactions for category learning: A neuro-computational approach

Francesc Villagrasa Escudero

Mon, 16. 4. 2018, Room 1/367a

Both the basal ganglia (BG) and the prefrontal cortex (PFC) are involved in category learning. However, their interaction in category learning remains unclear. A recent theory proposes that the basal ganglia, via the thalamus, slowly teach the PFC to acquire category representations. To further study this theory, we propose here a novel neuro-computational model of category learning which performs a category learning task (carried out by monkeys in the past to also study this theory). By reproducing key physiological data of this experiment, our simulations show evidence that further support the hypotheses held by the theory. Interestingly, we show that the fast learner (BG) with a performance of 80% correctly teaches a slow learner (PFC) up to 100% performance. Furthermore, new hypotheses obtained from our model have been confirmed by analyzing previous experimental data.

Verbesserung eines Aufmerksamkeitsmodells mit Deep Learning und Data Augmentation

Philip Hannemann

Thu, 22. 3. 2018, Room 1/309

Das von Frederik Beuth und Fred H. Hamker entwickelte Aufmerksamkeitsmodell wurde unter Nutzung des Coil Datensatzes getestet und Verbesserungen implementiert. Durch Anpassungen des im Aufmerksamkeitsmodell integrierten CNN und Optimierung der Struktur der verwendeten Layer, wurde es möglich, die bisherige Erkennungsrate auf Basis eines realitätsnahen Hintergrundes von 42% auf 60% zu erhöhen. Neben diesen CNN Modifikationen bestand ein weiterer Schwerpunkt der Arbeit in der Vereinfachung der eingesetzten Datenbank und mehrfach wiederholten Test- und Anlernvorgängen unter Nutzung der Methodik des Deep Learning. Der im Coil Datensatz enthaltene Hintergrund der Coil Bilder wurden hierfür testweise entfernt und die Coil Objekte wurden mit unterschiedlichen Hintergründen für den Lernprozess verwendet.

Untersuchung der Auswirkungen von Feedback in einem Modell zur Ziffererkennung

Miriam Müller

Wed, 14. 3. 2018, Room 1/368

Der überwiegende Teil des Informationsflusses im Gehirn erfolgt über rekurrente Verbindungen. Um die Auswirkungen von Rückwärtsverbindungen in einem künstlichen neuronalen Netzwerk zu untersuchen, wurden einem Modell zur Ziffererkennung diese Verbindungen in einem zwei- und dreischichtigen Modell hinzugefügt. Die dritte Schicht ist dabei kategoriespezifisch. Der unüberwachte Lernalgorithmus wurde zusätzlich durch einen externen Aufmerksamkeitseinfluss ergänzt. Diese Veränderungen werden mit dem ursprünglichen zweischichtigem Feedforward-Modell verglichen. Weiterhin werden in diesem Vortrag die Auswirkungen der Feedbackverbindungen auf ein zwei- und dreischichtiges Modell vorgestellt.

Continuous Deep Q-Learning with Model-based Acceleration

Oliver Lange

Tue, 20. 2. 2018, Room 1/273

Model-free reinforcement learning has been successfully applied to a range of challenging problems and has recently been extended to handle large neuronal network policies and value functions. However, the sample complexity of model-free algorithms tends to limit their applicability to physical systems. To reduce sample complexity of deep reinforcement learning for continuous control tasks a continuous variant of the Q-learning algorithms was introduced. This talk will be about the algorithm called normalized advantage functions (NAF) and the incorporation of a learned model to accelerate model-free reinforcement learning.

Inhibition and loss of information in unsupervised feature extraction

Arash Kermani

Wed, 14. 2. 2018, Room 1/273

In this talk inhibition as a means for inducing competition in unsupervised learning will be discussed. The focus will be on the role of inhibition in overcoming the loss of information and the loss of information caused by inhibition itself. A learning mechanism for learning inhibitory weights will be introduced and a hypothesis for explaining the function of VIP dis-inhibitory cells in the brain will be proposed.

Development of a Self-Organizing Model of the Lateral Intraparietal Cortex

Alex Schwarz

Tue, 30. 1. 2018, Room 1/336

Visual perception of objects in space depends on a stable representation of the world. Such a robust world centered depiction would be disrupted with every eye movement. The Lateral Intraparietal Cortex (LIP) in the human brain has been suggested to solve this problem by combining the retino-centric representations of objects from the visual input with proprioceptive information (PC) and corollary discharge signals (CD). Thereby it enables a steady positioning of objects in a head-centered frame of reference. Bergelt and Hamker (2016) had build a model of the LIP that included four-dimensional maps of the LIP. In this master thesis a modification of this model is presented introducing hebbian and anti-hebbian learning to achieve a connection pattern that shows a similar behavior without the necessity of a fixed predefined weighting pattern. Thereby a reduction of dimensions is possible, as well as a higher biological plausibility. The model shows the influence of both signals, PC and CD, on the representation of stimuli over the time of a saccade.

Using Recurrent Neural Networks for Macroeconomic Forecasting

Thomas Oechsle

Mon, 29. 1. 2018, Room 1/367a

Despite the widespread application of Recurrent Neural Networks (RNNs) in various fields such as speech recognition, stock market prediction and handwriting recognition, they have so far played virtually no role in the field of macroeconomic forecasting (i.e. in the prediction of variables such as GDP, inflation or unemployment). At the moment, not a single central bank in the world uses RNNs for macroeconomic forecasts. The purpose of the talk is to highlight the potential of RNNs in improving macroeconomic forecasting. First, the history of and current knowledge on RNNs are reviewed. Then, the performance of RNNs in forecasting US GDP figures is described and compared to that of the Survey of Professional Forecasters (SPF).

Implementation und Evaluation eines Place-Cell Modells zur Selbstlokalisation in einer Robotik Umgebung

Toni Freitag

Mon, 22. 1. 2018, Room 1/205

Die Orientierung im Raum stellt für Lebewesen und mobile Roboter eine essentielle Funktion dar. Die Lebewesen nutzen dazu Geschwindigkeit und Richtung ihrer Bewegung. Samu et al. (2009) stellten ein Modell eines neuronalen Netzes vor, um die Orientierung im Raum zu verbessern. Dazu wurden bei Ratten erforschte Rasterzellen (engl. grid cells) und Ortszellen (engl. place cells) verwendet. Wird sich dieses Modell auch in einer realen Umgebung beweisen? Es wurde das neuronale Netz in einer realen Umgebung trainiert. In einer anschließenden Testfahrt wurde versucht, die aktuelle Position des mobilen Roboters in der Arena zu bestimmen. In diesem Vortrag werden das zugrundeliegende Modell und die verschiedenen Varianten der Positionsbestimmung vorgestellt.

Inhibition and loss of information in unsupervised feature extraction

Arash Kermani

Wed, 17. 1. 2018, Room 1/367a

In this talk inhibition as a means for inducing competition in unsupervised learning will be discussed. The focus will be on the role of inhibition in overcoming the loss of information and the loss of information caused by inhibition itself. A learning mechanism for learning inhibitory weights will be introduced and a hypothesis for explaining the function of VIP dis-inhibitory cells in the brain will be proposed.

The connections within the visual cortex including the Pulvinar

Michael Göthel

Wed, 20. 12. 2017, Room 1/367a

How are the regions in the brain are connected to each other? This question was examined in the past a lot by different scientists to figure out how the information are transmitted between the areas V1 and V2. This Presentation will summarize some of them to generate a model which covers all layers in V1, V2 and includes the pulvinar.

Neuro-computational model for spatial updating of attention during eye movements

Julia Bergelt

Mon, 27. 11. 2017, Room 1/367a

During natural vision, scene perception depends on accurate targeting of attention, anticipation of the physical consequences of motor actions, and the ability to continuously integrate visual inputs with stored representations. For example, when there is an impending eye movement, the visual system anticipates where the target will be next and, for this, attention updates to the new location. Recently, two different types of perisaccadic spatial attention shifts were discovered. One study shows that attention lingers after saccade at the (irrelevant) retinotopic position, that is, the focus of attention shifts with the eyes and updates not before the eyes land to its original position. Another study shows that shortly before saccade onset, spatial attention is remapped to a position opposite to the saccade direction, thus, anticipating the eye movement. In this presentation, it will be shown how a published computational model for perisaccadic space perception accounting for several other visual phenomena can be used to explain the two different types of spatial updating of attention.

Decorrelation and sparsification by homeostatic inhibitory plasticity in cooperation with voltage-based triplet STDP for receptive field formation

René Larisch

Mon, 20. 11. 2017, Room 1/367a

In the past years, many models to learn V1 simple-cells from natural scene input which use spike-timing dependent plasticity have been published. However, these models have several limitations as they rely on theoretical approaches about the characteristics of the V1 simple-cells instead of biologically plausible neuronal mechanisms. We implemented a spiking neural network with excitatory and recurrently connected inhibitory neurons, receiving inputs from Poisson neurons. For excitatory synapses we use a phenomenologically voltage-based triplet STDP rule, from Clopath et al. (2010). The inhibitory synapses are learned with a symmetric inhibitory STDP rule, from Vogels et al. (2011), inspired by observations of the auditory cortex. We show that the cooperation of both phenomenological motivated learning rules leads to the emergence of a huge variety of known neuron properties. We focus on the role of inhibition on these neuron properties. To evaluate how inhibition influences the learning behavior, we compare model implementations without inhibition and with different levels of inhibition. Moreover, to separate the impact on neuronal activity from the impact on the learned weights, we deactivated inhibition after learning. We found that stronger inhibition sharpened the neuron tuning curves and decorrelated neuronal activities. Furthermore, we see that inhibition can improve the input encoding quality and coding efficiency.

Automatische Landmarken-Identifizierung aus einer hoch genauen 3D-Puntkwolke für die Selbstlokalisierung autonomer Fahrzeuge unter Anwendung eines biologisch inspirierten Algorithmus

Sebastian Adams

Mon, 6. 11. 2017, Room 1/367a

Autonomes Valet-Parken ist ein aktueller Bereich der Forschung in Richtung des autonomen Automobils. Eine präzise Selbstlokalisierung stellt in Parkhäusern eine Herausforderung dar, da oft kein GPS verfügbar und die Umgebung sowohl monoton, als auch sehr dynamisch ist. Menschen können sich im Parkhaus erfolgreich orientieren. Dazu nutzen sie unter Anderen auch die Möglichkeit der Orientierung über prägnante Punkte oder Objekte als sogenannte Landmarken. Diese Landmarken sollen nun in einer hoch genauen Punktwolke identifiziert werden, um später durch autonome Automobile bei einer Fahrt durch das Parkhaus zur Eigenlokalisierung genutzt zu werden. Eine Punktwolke ist das Ergebnis mehrerer Messungen mit einem Laserscanner. Die resultierenden Punkte werden durch ihre x,y und z Komponente im Raum sowie Intensitätswerte beschrieben. Ein Vorteil der Nutzung von Punktwolken zur Landmarken-Identifizierung ist die genaue 3D Repräsentation der identifizierten prägnanten Objekte. Um Landmarken zu identifizieren wurden zwei biologisch inspirierte Algorithmen verwendet. Das Biological Model of Multiscale Keypoint Detection (Terzic et al., 2015) nutzt nachempfundene Zellen der im visuellen Kortex angesiedelten Neurone und verbessert so die Erkennungsleistung sowie die Robustheit von Keypoints. Als zweiter Algorithmus wurde das Saliency Attentive Model (Cornia et al., 2016) genutzt, ein neuronales Netz, das die Wahrscheinlichkeit vorhersagt, auf welche Regionen in einem Bild Menschen ihre Aufmerksamkeit richten würden. Im Anschluss wurde die Einzigartigkeit der salienten Punkte validiert und evaluiert, ob die Anzahl der identifizierten Landmarken theoretisch für eine erfolgreiche Selbstlokalisierung ausreicht.

Anpassung eines Neuronenmodells für visuelle Wahrnehmung an neue physiologische Daten gemessen an Affen

Vincent Marian Plikat

Wed, 27. 9. 2017, Room 1/367

A. Ziesche und F.H. Hamker haben 2011 ein biologisch motiviertes Neuronenmodell für visuelle Wahrnehmung entwickelt. Dieses Modell siedelt die Wahrnehmung in der Lateral Intraparietal Area (LIP) an. Es ist erfolgreich in der Modellierung des Saccadic Surpression of Displacement Experiments (SSD), sowohl in Verbindung mit Blanking und Masking. Dieses Modell wird jedoch von neuen physiologischen Befunden, gemessen an Affen, herausgefordert (Xu et al, 2012). Affen verhalten sich jedoch im SSD Experiment nicht exakt wie Menschen. Bei ihnen ist kein Effekt im Blanking zu finden (Joiner et al, 2013). Meine Arbeit befasst sich damit das Modell an die neuen physiologischen Daten anzupassen und Varianten zu finden, die die Unterschiede zwischen Menschen und Affen erklären können.

Veränderung der Wahrscheinlichkeiten von explorierten Aktionen durch die Plastizität der STN - GPe Verbindung

Oliver Maith

Mon, 4. 9. 2017, Room 1/336

Viele Theorien und Modelle der Basalganglien haben gezeigt, dass dieses System mittels Aktivität verschiedener Wege (indirekt, direkt, hyperdirekt) zwischen Cortex und Thalamus belohnte Aktionen anregen und andere hemmen kann. Die Funktion der Verbindung zwischen dem Nucleus subthalamicus (STN) und dem externalen Globus pallidus (GPe) ist in diesem Zusammenhang noch relativ unklar. In einer Vorgängerstudie wurde mittels eines Basalganglien Computermodels gezeigt, dass die Verbindung zwischen STN und GPe dafür sorgen kann, dass bestimmte alternative Aktionen während einer Explorationsphase bevorzugt werden. Es kommt somit nicht zu einem zufälligen Ausprobieren aller möglichen Aktionen. Die Funktion der Verbindung zwischen STN und GPe könnte somit sein, zu lernen, welche Aktionen in einer Situation schon einmal erfolgreich waren und welche nicht. Während einer Exploration würden dann die erfolgversprechenderen Aktionen bevorzugt werden. Wie dies mittels der Implementation einer Lernregel zwischen STN und GPe realisiert wurde und wie hierfür zunächst das Basalganglienmodel der Vorgängerstudie angepasst werden musste, werden Schwerpunkte der Präsentation sein.

Classification of Aircraft Cabin Configuration Packages using Machine Learning

Sreenath Ottapurakkal Kallada

Fri, 18. 8. 2017, Room 1/336

Aircraft manufacturers are constantly trying to enhance the comfort of the passengers in the cabin. But the tradeoff with increasing the comfort adversely affects the maximum number of passengers an aircraft can carry. This will gradually affect the overall profit for the airlines. Designing a good aircraft cabin requires careful analysis of the various monuments which are placed inside the cabin and their corresponding features. These monuments can range from the crew rest compartment to the galleys where the food is stored. Each of these monuments has exclusive features which can be used as a guiding metric to revise the cabin architecture. These revisions of cabin layouts may occur frequently during the development process where many marketing layouts are pitched to the prospective customers but not all of them make it to the final production line. The A350 program from Airbus is analyzed in this master thesis where the cabin layout data is retrieved from various XML sheets using Python parsers and merged together into a processible format. Various feature selection methods belonging to filter, wrapper and embedded methods are used to analyze the important features of the monuments and cabin layouts. Finally, three tree classifiers (single decision tree classifier, ensemble tree classifier, boosted tree classifier) are fitted on the datasets to classify the cabin layouts and monuments. The performance of the classifiers and the analysis of the relationship between the predictors and the target variable are recorded.

Automating Scientific Work in Optimization

Prof. Dr. Thomas Weise

Fri, 21. 7. 2017, Room 1/336

In the fields of heuristic optimization and machine learning, experimentation is the way to assess the performance of an algorithm setup and the hardness of problems. Good experimentation is complicated. Most algorithms in the domain are anytime algorithms, meaning they can improve their approximation quality over time. This means that one algorithm may initially perform better than another one but converge to worse solutions in the end. Instead of single final results, the whole runtime behavior of algorithms needs to be compared (and runtime may be measured in multiple ways). We do not just want to know which algorithm performs best and which problem is the hardest - a researcher wants to know why. In this paper, we introduce a methodology which can 1) automatically model the progress of algorithm setups on different problem instances based on data collected in experiments, 2) use these models to discover clusters of algorithm (and problem instance) behaviors, and 3) propose reasons why a certain algorithm setup (problem instance) belongs to a certain algorithm (instance) behavior cluster. These high-level conclusions are presented in form of decision trees relating algorithm parameters (instance features) to cluster ids. We emphasize the duality of analyzing algorithm setups and problem instances. Our methodology is implemented as open source software and applied in two case studies. Besides its basic application to raw experimental data, yielding clusters and explanations of quantitative algorithm behavior, our methodology also allows for qualitative conclusions by feeding it with data which is normalized with problem features or algorithm parameters. It can also be applied recursively, e.g., to further investigate the behavior of the algorithms in the cluster with the best-performing setups on the problem instances belonging to the cluster of hardest instances. Both use cases are investigated in the case studies.

Erweiterung der S2000 Sensorplattform für die Verwendung auf einer mobilen Plattform.

Shadi Saleh

Thu, 13. 7. 2017, Room 1/367

Stereo cameras are important sensors to provide good recognition of objects with color perception and very high resolution. It has been used in a wide range of automotive application prototypes, especially in advanced driver assistant system (ADAS). In this thesis, different methods based only on a stereo vision system are proposed, in order to estimate the ego-motion within a dynamic environment and extract information about the moving objects and stationary background. This is realized using Intenta S2000 sensor, a new generation of intelligent cameras that generates 3D point cloud at each time stamp. However, constructing the static objects map and dynamic objects list is a challenging task, since the motion is introduced by a combination between the movement of the camera and objects. The first proposed solution is achieved based on the dense optical flow, which illustrates the possibility of estimation ego-motion, and splitting the moving object from the background. In the second proposed solution, the ego-motion within a dynamic environment is realized using methods for solving a 3D-to-3D point registration problem. The iterative closest point (ICP) method is used to solve this problem by minimizing the geometric differences function between two consecutive 3D point clouds. Then the subtraction between the two aligned clouds is computed where the output result represents the subset of the 3D points from the objects in the environment. The most effective method for estimating the multiple moving and static objects is presented in the third proposed solution. The ego-motion is estimated based on the ICP method and then the post-processed 3D point cloud is projected on a 2D horizontal grid and based on the additional features to each grid cells is possible to distinguish between different grid cells into free, occluded and border. The cells status can be used later to construct the static object map and dynamic object list. Determination of noise regions is an essential step, which can be used later to generate 3D point cloud with low level of noise. This is done depending on the unsupervised segmentation methods.

Multi-GPU Simulation of Spiking Neural Networks

Joseph Gussev

Tue, 16. 5. 2017, Room 1/367

With the increasing importance of parallel programming in the context of achieving more computation power, general purpose computation on graphics processing units (GPGPU) has become more relevant for scientific computing. An example would be the simulation of spiking neural networks. During this talk, an introduction to GPU programming as well as the basics of spiking network simulation on GPUs is given. Afterwards, the concepts of Multi-GPU spiking network simulation used by the neural network simulators HRLSim and NCS will be explained.

Sound Source Detection and Localizing Active Person during Conversation

Ranjithkumar Jayaraman

Tue, 11. 4. 2017, Room 1/367

Robots has been emerging for interaction with people. Finding the correct sound source is very important for engaging in conversation between people or for being a co-worker with human. This project aims to find the sound direction and to get the source person of the sound in NAO robot. Sound direction can be determined easily with two microphones in free space, but when it placed in uneven spherical head of robot became complex because of diffraction and scattering of sound waves at the surfaces of robot head. Linear and logistic regression algorithm had been used to localize the direction of the sound source. OpenCV libraries had been used to find the human face in the sound direction. Finally we associate the sound source direction with the detected human faces. This algorithm promises to return robots more interactive while lessening to a conversation or working together with human co-workers.

Modelling attention in the neurons of area MT

Christine Ripken

Wed, 15. 3. 2017, Room 1/208

In order to model attention for motion detecting neurons, responses of the Model of Neuronal Responses in visual area MT (middle temporal area) of Simoncelli and Heeger were processed by the Mechanistic Cortical Microcircuit of Attention of Beuth and Hamker. The Mechanistic Cortical Microcircuit of Attention has replicated a great range of data sets, only implementing four basic neural mechanisms: amplification, spatial pooling, divisive normalization and suppression. The Model of Neuronal Responses in Visual Area MT is replicating very well recordings from area MT. The aim was to replicate responses of a representative MT neuron, whose data was collected by Maunsell and Lee. The defense will give a overview over the models and discuss briefly the results, which do not yet replicate the physiological data to a satisfying extent.

Untersuchung der Auswirkungen von Feedback in einem Modell des visuellen Systems

Daniel Johannes Lohmeier-von Laer

Wed, 15. 3. 2017, Room 1/208

Das visuelle System ist nicht nur auf Feedforward- und Laterale-Verarbeitung begrenzt, es gibt auch Rückverbindungen von Arealen höherer Hierarchie in frühere kortikale Areale. Welche Rolle diese Rückverbindungen bzw. Feedback spielt, ist noch nicht ausreichend verstanden und im Vergleich zur Feedforward-Hierarchie weniger erforscht. In einem Modell, dass den primären und sekundären visuellen Kortex umfasst, wurde eine Feedbackverbindung von V2 nach V1 hinzugefügt. Da die Wirkung des Feedbacks auf der Aktivität der V2-Neuronen beruht, wird diese Aktivität zusätzlich gesteuert werden, indem Aufmerksamkeit in der V2-Schicht simuliert wird. Wie stark der Feedbackstrom in Relation zu Feedforward ist, was er bewirkt und welche Auswirkungen die Simulation von Aufmerksamkeit auf die rezeptiven Felder der Neurone hat, wird in diesem Vortrag vorgestellt.

Inhibition decorrelates neuronal activities in a network learning V1 simple-cells by voltage-based STDP and homeostatic inhibitory plasticity

Rene Larisch

Tue, 14. 3. 2017, Room 1/208

In the past years, many models to learn V1 simple-cells from natural scene input which use spike-timing dependent plasticity are published. However, these models have several limitations as they rely on theoretical approaches about the characteristics of the V1 simple-cells instead of biologically plausible neuronal mechanisms. We implemented a spiking neural network with excitatory and recurrently connected inhibitory neurons, receiving inputs from Poisson neurons. The excitatory synapses are learned with a phenomenologically voltage-based triplet STDP rule, from Clopath et al. (2010), and the inhibitory synapses are learned with a symmetric inhibitory STDP rule, from Vogels et al. (2011). To analyze the effect of inhibition during learning, we compare model implementations without inhibition and with different levels of inhibition. Furthermore, to study the impacts on the neuronal activity, we compare a model learned with inhibition to itself without inhibition. We see, that inhibition leads to better differentiated receptive fields, with decreased correlation between the neuron's activities and improved robustness to small input differences. Furthermore, we can reproduce observations from other computational V1 simple-cell models and from physiological experiments.

Fast simulation of neural networks using OpenMPI

Florian Polster

Tue, 14. 3. 2017, Room 1/208

In order to make the simulation of very large neural networks possible, ANNarchy has been extended by an MPI implementation. ANNarchy uses Cython to transfer the simulation setup from the Python application to the generated C++ simulator, which is unsuitable in a distributed context and therefore replaced by a combination of ZeroMQ (for transmission) and Protobuf (for serialization). For the distributed simulation a neural network is partitioned into subgraphs by splitting populations and projections by post-synaptic neurons.

Deep Reinforcement Learning in robotics

Winfried Lötzsch

Mon, 27. 2. 2017, Room 1/367

For creating intelligently and autonomously operating robotics systems, Reinforcement Learning has longly been the method of choice. Combining the Reinforcement Learning principle with a deep neural network does not only improve the performance of these algorithms on commonly known robotics tasks, but also enables more complex tasks to be performed. I will show ways to implement this combination for both simulated and real robotics systems. One approach is to train the deep network to directly output motor commands for the robot [], but it can also be used to predict the success of a given action []. Furthermore, recent work has shown, that using multiple learner instances in parallel improves training performance [].

Mechanisms for the Cortex-Basal Ganglia interactions in category learning via a computational model

Francesc Villagrasa Escudero

Wed, 1. 2. 2017, Room 1/208

We present a novel neuro-computational model for learning category knowledge via the interaction between the basal ganglia and the prefrontal cortex. According to this model the basal ganglia trains the acquisition of category knowledge in cortico-cortical projections. In particular, our model shows that the fast learning in the basal ganglia alone is not optimal for correctly classifying a large number of stimuli whereas, the combination between fast and slow learning in the prefrontal cortex produces stable representations that can perfectly categorize even a larger number of exemplars. Our model also adds novel predictions to be empirically tested. The basal ganglia is classically known for being involved in stimulus-reponse (S-R) learning. However, our results show that the striatum does not encode representations of full stimuli but of features. Therefore, we propose that the striatum learns feature-response (F-R) associations related to reward. In addition, our model predicts that the thalamic activation correlates with the final category decision throughout the whole experiment ? different from the striatum which is only strongly involved early in learning and from the prefrontal cortex which is only highly engaged later in learning.

Korrektur perspektivischer Verzerrungen in der virtuellen Realität für eine blickinvariante Objektlokalisation

Frank Witscher

Wed, 18. 1. 2017, Room 1/367

Perspektivische Varianz besteht seit Jahrzehnten als Problem für die Objekterkennung einer künstlichen Intelligenz. In einer virtuellen Versuchsumgebung wird diese Problematik durch das standardmäßige Renderingverfahren erweitert, da dieses Objekte verzerrt darstellt. Um hier dem steigenden Variantenreichtum der Darstellung eines Objektes entgegenzuwirken, wird in den Renderingprozess eingegriffen und die konventionelle Projektion durch eine sphärische ersetzt. Hierbei wird eine Minimierung der perspektivischen Verzerrung erreicht und zusätzlich die Praktikabilität der neuen Projektion präsentiert.

Evolving from 2D and 3D Morphable Models to mixed Models

Felix Pfeiffer

Wed, 14. 12. 2016, Room 1/336

One major subject of Computer Vision and Artificial Intelligence is the tracking of deformable Objects. A common approach is the usage of Distributed Point Models, which can hold the parameters of the object in question. Based on the infamous Image Alignment algorithm developed by Lucas and Kanade, Simon and Baker came up with improvements that included the alignment of not only position, scale and rotation but also its very basic form. Together with a set of clever and quite efficient algorithms it gained the name Active Appearance Model (AAM). A usual application would be the tracking of human faces, which can be useful for e.g. reading emotions. The common approach is to take a simple camera to get pictures (or whole series of pictures) and apply the AAM only in that 2D space. Since the input data is only 2 dimensional it is seems quite intuitive that this model has a bunch of limits modeling our in fact 3 dimensional world. In the 5 part paper series 'Lucas Kanade 20 Years on: A Unifying Framework', Simon and Baker stated that the model is easily applicable on images in 3D Space, like you would gain from MRI or CRT. The to-the-3rd-dimension-modified model is than called 3D Morphable Model (3DMM). So the model itself is able to work in 3D space, provided you have a 3D image as input which is hard. To get rid of the AAMs disadvantages regarding the modeling power, it would be neat if the best of the 2 models can be combined to gain a mixed model, the 2.5D Morphable Model: Using 2D input from a simple camera, but having the model residing in the 3rd dimension. Simon and Baker do exactly that in the 5th part of the already mention paper series. Sadly they found that *none* of the algorithms, that worked so well in AAMs and 3DMMs alone, are applicable. But luckily there are already solutions to it ... The seminar will have 2 major parts: The first one subjects the Image Alignment algorithm, its techniques and its evolution into the AAM without diving to deep into mathematics. After showing that it is easy to lift the AAM into 3D realm, I will present what has been thought about the development of the 2.5D Model, why it does not work as intended and two solutions that show how it can be done anyways.

Perspectives of Object Recognition and Visual Attention

Johannes Jung

Wed, 9. 11. 2016, Room 1/336

Visual attention is a diversified field of research, incorporating neuroscience, psychology and computer science. I would like to talk about different perspectives of visual attention and its influence on object recognition, based on the ideas of various attention systems. Traditionally saliency map models, emerging from psychological theories, are very important in this domain. However, recent attention models for object recognition often strongly benefit from machine learning ideas, such as deep learning, but partially neglect biological plausibility.

Modellierung einer Face-to-Face-Interaktion zwischen Mensch und Agent hinsichtlich der Übertragung emotionaler Werte durch Mimik

Sarah Paul

Mon, 29. 8. 2016, Room 1/336

Ziel dieser Arbeit bestand darin, im Rahmen eines Spieles, eine nonverbale Interaktion zwischen Mensch und Computer, auf Basis der Übertragung und Erkennung von Gesichtsausdrücken,zu erstellen. Im Rahmen des Vortages wird zunächst die Motivation und das Ziel der Arbeit genannt und anschließend die erstellte Mensch-Maschine-Interaktion anhand der Architektur vorgestellt. Es wird kurz auf die einzelnen Komponenten, die für diese Kommunikation notwendig sind eingegangen. Die erste ist die maschinelle Wahrnehmung von menschlichen Gesichtern und bestimmten Merkmalspunkten mit Hilfe des Active Appearance Model. Die Zweite ist das Facial Expression-Recognition-Modell, welches auf der Basis der wahrgenommenen Merkmalspunkte Action Units des Facial Action Coding System erkennt. Die Interpretation der Gesichtsausdrücke und Reaktion darauf erfolgt im Rahmen eines Spieles, in dem mit dem Computer über den Gesichtsausdruck kommuniziert und eine Entscheidung im Rahmen eines sozialen Dilemmas getroffen werden kann. Es wird während des Vortrages der Ablauf des Spiel näher erläutert. Abschließend wird auf die Qualität der einzelnen Komponenten sowie der Mensch-Computer-Interaktion als Ganzes eingegangen und Anhaltspunkte für mögliche weiterführende Untersuchungen und Entwicklungen genannt.

Predictive Place-cell Sequences for Goal-finding emerge from Goal Memory and the Cognitive Map: A Model

Valentin Forch

Mon, 4. 7. 2016, Room 1/336

Individuals need a neural mechanism enabling them to navigate effectively in changing environments. The model of predictive place-cell sequences for goal-finding is put forward to explain some neural activities found in rats which were observed in the context of navigation. A characteristic phenomenon is the occurrence of so called sharp wave ripples in the place-cells of hippocampus when rats are orienting themselves in a new environment. These place-cell sequences were shown to be predictive for future paths of these animals. The model also draws from the concept of the cognitive map, which should allow individuals to form a representation of their environment. I will introduce the theoretical framework of the cognitive map, present some specific findings relevant for the creation of the model, the model itself, and discuss some implications for future research.

Emotional attention and other amygdala functionalities

René Richter

Wed, 22. 6. 2016, Room 1/346

Emotional stimuli attract attention so the brain can focus its processing resources. But how do these stimuli acquire their emotional value, and how can they influence attention processes is still an open issue. Evidence suggests that this association might be learned through conditioning in the basal lateral amygdala (BLA) who also sends back feedback connections to the visual cortex (a possible top-down attention mechanism). The learning of the association on the other hand is strongly modulated by dopamine and the exact timing of its distribution is crucial. Accordingly, a rate-coded, biological realistic, neuro-computational model constructed of 3 combined functional models (Visual Cortex, Amygdala, Basal Ganglia Timing Circuit) will be presented. Moreover, the amygdala needs a specific range of functions to work well in this context and a detailed explanation of these and additional functions will round up the talk.

RoboCup 2016: An overview

John Nassour

Mon, 13. 6. 2016, Room 1/336

I will present an overview of the days at RoboCup in Leipzig (30 June to 4 July). Different types of completion are taking places: RoboCup Soccer, RoboCup Industrial, RobotCup Rescue, RobotCup@Home, and RobotCup Junior. Then I present the robotic side of our group's contribution in the stand 'Robots in Saxony'.

Spatial Synaptic Growth and Removal for Learning Individual Receptive Field Structures

Michael Teichmann

Mon, 6. 6. 2016, Room 1/336

A challenge in creating neural models of the visual system is the appropriate definition of the connectivity. Structural plasticity can overcome the lack of a priori knowledge about the connectivity of each individual neuron. We present a new computational model which exploits the spatial configuration of connections for the formation of synapses and demonstrate its functioning and robustness.

Learning Object Representations for Modeling Attention in Real World Scenes

Alex Schwarz

Mon, 9. 5. 2016, Room 1/336

Many models of visual attention exist, but only a few have been shown with real-world scenes. The Model of (Beuth and Hamker, 2015, NCNC) is one of these models. It will be shown how this model could adapted to work on real-world scenes, using a temporal continuity paradigm. The influence of a high post-synaptic threshold and a normalization mechanism will be shown, driving the learned weights into being background-invariant.

A computational model of Cortex Basal Ganglia interactions involved in category learning

Francesc Villagrasa Escudero

Tue, 19. 4. 2016, Room 1/336

We present a detailed neuro-computational model of Cortex-Basal Ganglia interactions that can account for a specific category-learning paradigm, the prototype distortion task. It proposes a novel principle of brain computation in which the basal ganglia read out cortical activity and, by their output, determine cortico-cortical connections to learn category specific knowledge. In order to better link computational neursocience with cognitive function, our model reproduces a physiological experimental and its main results. Finally, this model supports a recent hypothesis: the striatum 'trains' a slow learning mechanism in the Prefrontal Cortex to acquire category representations. Moreover, it provides insight into how the striatum encodes stimuli information.

Reinforcement Learning with Object Recognition in a Virtual Environment

Joseph Gussev

Wed, 23. 3. 2016, Room 1/367

Instead of being dependent on detailed external information from a complex environment, the reinforcement learning agent in this Bachelor Thesis can extract information from visual stimuli. It uses Q-Lambda-Learning and a recently developed attention-driven object localization model in a virtual environment. The implemented system is presented and results of learning analyzed.

Attentional deep networks

Priyanka Arora

Tue, 8. 3. 2016, Room 208

We all know what attention is and we all even know how attention is used in the field of biology. As a part of research, a deep dive conducted on what attention focuses on and how it is been used in the field of machine learning. The following seminar would deal with how images or data is identified using models built by taking Spatial attention and Feature based attention into consideration.Takeaway of the talk would include how these two attention based models differ in their working.

A Computational Model of Neurons in Visual Area MT

Tobias Höppner

Thu, 25. 2. 2016, To be announced

The model for motion estimation from Simoncelli and Heeger (1998) will be explained in detail. The Middle Temporal (MT) area of the brain are selective for velocity (both direction and speed). For a neural representation of velocity information in MT it is necessary to encode local image velocities. The model perfrorming these steps consists of two similar stages. Each stage computes a sum of inputs, followed by rectification and divisive normalization. The mathematical underpinnings of the model will be explained acompanied by simulution results.

Modelling / simulation of the exhaust path of a diesel TDI common rail engine with Emission standard EU 6, taking into account the real-time capability on a HIL simulator.

Sowmya Alluru

Thu, 25. 2. 2016, Room 1/336

Different model strategies such as zero dimensional and empirical methods are analyzed for the diesel engine exhaust system created for state variables such as temperature, pressure and mass flow in the exhaust pipes between the exhaust components. The models are evaluated to show the predicted results are in comparison with actual measurements. In addition, the real-time capability of the models in the Hardware-in-the-Loop (HIL) is estimated based on prediction function run-time metrics obtained by C++ and Matlab simulink implementation of the algorithms. Neural Networks show higher accuracy and succeed in predicting or regressing maximum variance in the inputs as compared to classical analytical methods (zero-D), SVM based kernel methods and simple polynomial regression methods.

Revealing the impairments of thalamic lesions using automatic parameter tuning for a neurocomputational model of saccadic suppression of displacement

Mariella Dreißig

Wed, 24. 2. 2016, Room 1/336

Several studies suggest, that the thalamus is crucial for maintaining the perception of visual stability across saccades. In this thesis the role of the thalamus in visual processing was investigated closer. With the help of a neurocomputational model, data from patients with focal thalamic lesions and from healthy controls were replicated by adjusting the model parameters in an automated fitting procedure. By mapping the model's performance onto the subjects' performance in the saccadic suppression of displacement task, the impacts of thalamic impairments on the perception of visual stability could be revealed.

Kombination von STDP- und iSTDP-Lernregeln zum Lernen von rezeptiven Feldern in einem Modell des primären visuellen Kortex unter Berücksichtigung von lateraler Exzitation
(Combination of STDP and iSTDP rules to learn receptive fields in a model of the primary visual cortex taking account of lateral excitation)

René Larisch

Wed, 10. 2. 2016, Room 1/336

To understand the basic mechanisms of the neuronal processing, it is necessary to use models which are biologically plausible and show efficient performance in network simulations. Spike-Timing-Dependent-Plasticity rules seem to be a good choice. In this thesis an STDP model, where the weight development is based on the membrane potential, is used to learn receptive fields of V1. It was modified to be more biologically plausible, while the ability to learn receptive structures should be preserved. Furthermore, the ability to learn excitatory lateral connections and their effect on the neuronal activity were studied.

Inference statistics in neuroscience: their application and interpretation

Henning Schroll

Thu, 17. 12. 2015, Room 1/367

I will give an introduction to parametric and non-parametric statistics as commonly used in neuroscientic reports. Core concepts of different statistical approaches will be portrayed; the issue of choice among measures will be tackled. Finally, I will highlight, how to (not) interprete various statistical results.

Improving the robustness of Active Appearance Models for facial expression recognition

Lucas Keller

Wed, 16. 12. 2015, Room 1/336

Active Appearance Models are a popular method to detect facial expressions in an image or video. While there exists a very efficient algorithm to fit an AAM to an image, it still has some deficits in terms of robustness. This presentation discusses these problems and presents solutions implemented for my thesis.

Classification of motion of a humanoid robot while walking

Saqib Sheikh

Wed, 2. 12. 2015, Room 1/336

The goal of the master thesis was to find a classification technique that is accurate, reliable and easily implemented. This thesis suggests three techniques that were fully implemented and results documented. The techniques used for classification are Dynamic time warping algorithm, Fourier fast transform with random forest algorithm and Fourier fast transform features with decision trees algorithm.

A unified divisive-inhibition model explaining attentional shift and shrinkage of spatial receptive fields in middle temporal area

Alex Schwarz

Wed, 25. 11. 2015, Room 1/336

Attention reshapes receptive fields in area MT based on the locations of attended and unattended stimuli. There are several models explaining attentional shift, shrinkage, expansion, exitation and suppression individually. A divisive-inhibition model will be presented unifying all those attentional effects in one biologically plausible model, relying on data of Anton-Erxleben et al. (2009) and Womelsdorf et al. (2008).

Simulation eines virtuellen Agenten zum Finden von ergonomisch optimalen Arbeitsabfolgen

Sergej Schneider

Wed, 11. 11. 2015, Room 1/273

Ob ein Reinforcement Learning Agent eine Lösung für ein Problem findet und wie gut diese Lösung dann sein wird, hängt stark von den gewählten Parametern ab. In meiner Arbeit habe ich die Auswirkungen verschiedener Parameter auf das Lernverhalten eines Q-Learning bzw. eines Q(λ)-Learning Agenten im Smart-Virtual-Worker-Framework getestet. Dabei wurden sowohl zeitliche als auch ergonomische Kriterien in einem Transportszenario untersucht.

Entwicklung einer virtuellen Versuchsumgebung zur experimentellen Untersuchung von Raumorientierung und visueller Aufmerksamkeit

Sascha Jüngel

Wed, 28. 10. 2015, Room 1/208A

Ein visuelles System erhält so viele sensorische Daten, das eine clevere Fokussierung auf das Wesentliche nötig ist. Diese Präsentation beschäftigt sich mit der Erstellung einer virtuellen Umgebung mit verschiedenen Szenarien (unter anderem einem Memory-Spiel), um diesen noch nicht vollständig verstandenen Prozess der Aufmerksamkeit in Zukunft besser erforschen zu können. Dafür führt ein virtueller Agent verschiedene Aufgaben mit den Schwerpunkten des aufmerksamkeitsbasierten Beobachtens mehrerer Objekte, der Objekterkennung und dem Erinnern an eine Position im Raum aus.

Deep Learning - Beating the ImageNet Challenge

Haiping Chen

Wed, 14. 10. 2015, Room 1/336

Deep Learning is now a popular algorithm model in machine learning, it has great capability in visual recognition tasks. A competition of recognition tasks which is called “Large Scale Visual Recognition Challenge” is taken place every year to find out the best approach of the year solving the given tasks. This presentation will introduce the concept of deep learning model and a illustration of “Large Scale Visual Recognition Challenge”. A learning model called “GoogLeNet” will be analyzed, to find out why and how this learning model is the champion of the “Large Scale Visual Recognition Challenge 2014”

Intrinsically Motivated Learning of a Hierarchical Collections of Skills

Marcel Richter

Tue, 8. 9. 2015, Room 1/336

Humans and other animals often engage in activities for their own sakes rather than as steps toward solving practical problems. Psychologists call these intrinsically motivated behaviors. What we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. A way to achieve this intrinsic motivated behaviors in the machine learning framework will be presented.

Lernen von zeitlich-dynamischen rezeptiven Feldern in einem Modell des primären visuellen Cortex

Michael Göthel

Tue, 25. 8. 2015, Room 1/368

Ein bestehendes Modell des primären visuellen Cortex wurde erweitert, um die Möglichkeiten des Erlernens von dynamischen rezeptiven Feldern (RFs) zu untersuchen. Hierzu wurden die Lateral Geniculate Nucleus (LGN) Neuronen mit einem räumlichen und zeitlichen RF ausgestattet. Außerdem wurde die Inhibition der Neuronen durch inhibitorische Interneuronen realisiert. Die Veränderungen des Modells wurden evaluiert und auf die E?ektivität untersucht.

Autonomous development of disparity tuning and vergence control for the iCub

Patrick Köhler

Tue, 18. 8. 2015, Room 1/336

Robustness against negative external and internal influences is a desirable feature in A.I. driven robotic systems. This presentation explores the functionality of an intrinsically motivated approach for a self-calibrating binocular vision system on basis of the iCub robot. The core principles of the efficient coding hypothesis and reinforced learning and their implementation in the model are explained and the test results presented.

Approximation of Kernel Support Vector Machine using Shallow Convolutional Neural Networks

Jekin Trivedi

Thu, 30. 7. 2015, Room 1/336

This thesis investigates the problem of efficiently approximating Kernel Support Vector Machines at test time. The classification algorithms have, especially when executed repeatedly, a high computational complexity. In addition, kernel-methods are often hard to understand, given the unknown distribution of the data in feature space. The proposed remedy to this situation, mimicking such classifiers with shallow neural networks, or, in the case of image data, shallow convolutional neural networks. This is done by approximating the classification function of the Support Vector Machine. In addition, the shallow (convolutional) neural networks give an easier insight into the functioning of the classification algorithm c.f. kernel-based methods. We present compelling results on the MNIST, CIFAR-10 and STL-10 dataset.

ANNarchy 4.5: what's new?

Julien Vitay

Tue, 23. 6. 2015, Room 1/336

After a small reminder of the main ideas behind the neural simulator ANNarchy (Artificial Neural Networks architect), the new features introduced in ANNarchy 4.5 will be presented: monitoring, structural plasticity, reporting, multiple networks, parallel simulations, early stopping...

A recurrent multilayer model with Hebbian learning and intrinsic plasticity leads to invariant object recognition and biologically plausible receptive fields.

Michael Teichmann

Tue, 9. 6. 2015, Room 1/336

We developed a model of V1 and V2 based on anatomical evidence of the layered architecture, using excitatory and inhibitory neurons where the connectivity to each neuron is learned in parallel. We address learning by three different mechanisms of plasticity: intrinsic plasticity, Hebbian learning with homeostatic regulations, and structural plasticity.

Long Short Term Memory Networks - a solution for learning from input data showing significant time lags

Simon Kern

Tue, 2. 6. 2015, Room 1/336

Es geht um den Aufbau und Funktionsweise von LSTM Netzwerken. Ich werde diese Sorte Netzwerke mit MLPs vergleichen und versuchen zu erklären, warum LSTM besser für temporale Probleme geeignet ist. Anhand eines Beispiels werde ich die Details erläutern, um abschließend mit der Diskussion über gelöste Probleme, Möglichkeiten und Grenzen von LSTM Netzwerken zu schließen. Der Vortrag wird in Englisch sein.

Efficient learning of large imbalanced training datasets for support vector machines

Surajit Dutta

Tue, 19. 5. 2015, Room 1/336

This Master thesis concentrates mainly on supervised learning. We are provided an adas dataset (provided by Continental AG) containing a number of video recordings. In the context of this thesis we are only interested in the pose of a pedestrian, i.e. whether the pedestrian is facing to the front, to the back (frontal/posterior positioning), to the left or to the right (lateral positioning). Provided with this information, this thesis investigates the task of learning well-performing and well-generalizing detectors for both subclasses. In the provided dataset, the lateral class is of significant lower cardinality than the frontal/posterior class. Such an imbalanced training dataset usually has a negative impact on detector learning in case the detector is ought to perform similar on both classes and hence we focus on learning detectors that overcome the imbalance in the training dataset.

Exploring biologically-inspired mechanisms that allow humanoid robots to generate complex behaviors for dealing with perturbations while walking

Tran Duy Hoa

Tue, 21. 4. 2015, Room 1/368

Human reacts against perturbations while walking by doing a sequence of movements. Reaction movements help human to recover the stabilization of the body by re-posing postures. In case of the fall is unavoidable, reaction movements may help human to have a well fall instead of a bad fall. Inspired from motor sequence learning in primate brain, the proposal aims exploring new mechanisms that help humanoid robots keeping away from falling in deal with perturbations while walking. This will be done through the dynamic sensory-motor interaction with the environment, and a human teacher if needed. In that, sequence of reaction movements are acquired, maintained, and executed through a motor sequence learning architecture that are composed based on the inspiration from the role of brain cognitive loops in motor and sequence learning. Basal ganglia will play the role of the regulator in selection and performance context-based appropriate sequence of reaction movements. Dopamine neurons will play the role of the reinforcement learning mechanism in acquiring sequence of reaction movements. Movements of humanoid robot's legs are actually driven by the Multi-layered Multi-pattern central pattern generator (CPG) to generate sequence of patterns under the regulation of descending control signals from the motor sequence learning architecture, and ascending control signals from sensory feedback. The proposal also refers to the Dynamic field theory (DFT) that models sequence of movements mathematically as the evolution of neuronal populations under the interaction between internal and external forces continuously in time. The humanoid robot (NAO) with this mechanism is expected to be able to learn to perform sequence of reaction movements to self-recover the stabilization of body and avoid the fall in deal with perturbation while walking.

Evolution eines künstlichen neuronalen Netzes zur Steuerung eines Agenten in einem simulierten Umfeld.

Andy Sittig

Mon, 23. 3. 2015, Room 1/336

Der Vortrag gibt eine Einführung in die Grundlagen der Neuroevolution eines Agenten in einem einfachen 2D Actionspiel. Dabei wird auf die Spielumgebung und deren Wahrnehmung, sowie die daraus abgeleitete Konfiguration des künstlichen neuronalen Netzes und evolutionären Algorithmus eingegangen. Abschließend werden die Ergebnisse des Prozesses besprochen und kritisch betrachtet.

Facial feature detection

Lucas Keller

Wed, 18. 3. 2015, Room 1/336

Facial feature detection is an important branch of research in computer vision and can be used for e.g. face recognition, human computer interaction or the classification of facial expressions. Although its an easy task for a human to detect facial features, its still difficult to perform for a computer. This presentation gives an overview over some methods to extract these features from images and further explains and demonstrates the usage of Active Appearance Models.

Erstellung eines Moduls für die Online Performance Messung auf paralleler Hardware.

Leander Herr

Wed, 4. 3. 2015, Room 1/367

Immer komplexere Berechnungen stellen wachsende Herausforderung an die Hardware. Dieser Komplexität wird häufig mit der Verwendung von paraller Hardware begegnet, sowohl Multi-Core als auch GPUs. Für eine effiziente Berechnung müssen dabei verschiedene Umgebungsparameter eingestellt werden (z. B. Anzahl von Threads). Diese Parameter können jedoch nur mit entsprechendem Aufwand analysiert werden. Zur Unterstützung dieses Prozesses werden oft Profiling-Tools (TAU, NVVP oder Vampir) für eine Offline-Analyse verwendet. Ziel der Arbeit war es eine API für die Online-Zeiterfassung von parallelen Code zu entwickeln.

A neurocomputational systems-level model of affective visual attention.

Rene Richter

Wed, 18. 2. 2015, Room 1/336

In order to simulate emotional attention, a biologic realistic system-level model has been developed which simulates how emotions could influence our visual attention system during the presentation of objects and facial features. The system-level model is composed of two different models, a visual attention model for a simulation of attention on the visual processing pathway, and an amygdala model that represents the emotional influence on this pathway.

Fast approximation of deep neural networks.

Jekin Trivedi (With Continental AG)

Wed, 11. 2. 2015, Room 1/208a

Deep convolutional neural networks have become the state of the art for many computer vision applications. During my internship we have focussed on methods of this field, such as sparse (convolutional) autoencoders and denoising autoencoders. The talk will focus on these topics while giving an outlook on the research I will undertake in my master thesis, which involves the approximation of deep models with shallower and faster models.

Revealing the impairments of thalamic lesions using a neurocomputational model of saccadic suppression of displacement.

Christina Axt

Thu, 5. 2. 2015, Room 1/375

The impression of a stable world is mostly taken for granted in everyday life. We perceive our environment as a unified, continuous panorama, always present and seamless in its permanence, although our eyes move the entire time to sample it. The underlying neural mechanisms to ensure this visual stability, however, do not run as seamlessly as our perception. In this bachelor thesis, the main focus laid on revealing the impairments of patients with thalamic lesions, and on identifying the sources affecting their perception of the visual environment. Furthermore, it was examined whether there were similarities amongst the patients that cause similar impairments.

Hippocampal place-cell sequences support flexible decisions in a model of interactions between context memory and the cognitive map. / Praktikumsabschluss Konstantin Willeke.

Lorenz Goenner / Konstantin Willeke

Wed, 28. 1. 2015, Room 1/336

Hippocampal place-cell sequences observed during awake immobility have been found to represent either previous experience, compatible with a role in memory processes, or future behavior, as required during planning. However, a unified account for the sequential organization of activity is still lacking. Using computational methods, we show that sequences corresponding to novel paths towards familiar locations can be generated based on interactions between two types of representations: First, a stable map-like representation of space, represented by a continuous attractor model of hippocampal area CA3. Second, context-dependent goal memory, given by context-specific activity in entorhinal cortex (EC) and reward-modulated plasticity at EC synapses onto dentate granule cells. The model contributes (1) an account of goal-anticipating place cell sequences in open-field mazes, (2) an explanation for the role of sharp wave-ripple activity in spatial working memory, and (3) a prediction for the involvement of spatial learning in the development of place-cell sequences.

In his research internship, Konstantin Willeke has investigated a potential extension of the model to include learning the environmental topology in the recurrent connections between place cells. He will present additional simulation results.

Eine mathematische Handlungstheorie mit Anwendung auf die Mikroökonomie.

Radomir Pestow

Wed, 21. 1. 2015, Room 1/336

Es werden ein agentenbasiertes Modellierungswerkzeug und Begriffsapparat vorgestellt mit welchem dynamische Prozesse, inbesondere materielle, psychologische und soziale Prozesse, exakt beschrieben werden können. Dieser Apparat soll dann beispielhaft auf zwei wirtschaftliche Modelle angewandt werden.

Entwicklung eines parallelen genetischen Algorithmus zur Maschinenbelegungsplanung.

Martin Wegner

Wed, 21. 1. 2015, To be announced

Genetische Algorithmen gelten als leistungsfähige Lösungsverfahren für praxisrelevante Planungsprobleme aus den Bereichen Produktion und Logistik. Ziel der Arbeit ist die Entwicklung eines genetischen Algorithmus mit einem regionalem Modell und der Vergleich der Leistungsfähigkeit dieses Konzeptes mit anderen Lösungsverfahren. Die Besonderheit dieser Form der Parallelisierung ist die Nutzung verschiedener verknüpfter Populationen.

Using a convolutional neural network for cancer detection - the computational model

Arash Kermani

Wed, 17. 12. 2014, Room 1/336

In this talk, the paper: 'Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks' (by Dan C. Cires an, Alessandro Giusti, Luca M. Gambardella, Juergen Schmidhuber) will be discussed. This second part will focus on the computational model.

Reinforcement Learning in Multi Agent Systems

Joseph Gussev

Wed, 10. 12. 2014, Room 1/336

The task of a Q-Learning agent is to learn which actions he should use to recieve the maximum of a certain numerical reward. In this presentation an overview will be given on how agents in Multi Agent Systems can learn and work together or be concurrent with other agents in the same system. There is also a solution presented, which focuses on the 'Credit Assignment Problem' occuring when agents work together on a joint task.

Using a convolutional neural network for cancer detection

Arash Kermani

Wed, 26. 11. 2014, Room 1/336

In this talk, the paper: 'Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks' (by Dan C. Cires an, Alessandro Giusti, Luca M. Gambardella, Juergen Schmidhuber) will be discussed. The focus will be on the deep learning method used for cancer detection.

A unified system-level model of visual attention and object substitution masking

Frederik Beuth

Wed, 5. 11. 2014, Room 1/336

The phenomena of visual attention (Hamker, 2005, Cerebral Cortex) and object substitution masking (OSM; DiLollo and Enns, 2000) are supposed to rely on different processes. However we will show that OSM can be accounted by well-known attentional mechanisms within a unified model.

SVM balancing and object detection

Dr. Patrick Ott (Continental AG) and Surajit Dutta

Mon, 27. 10. 2014, Room 1/336

Dr. Patrick Ott will present an overview of the research conducted at Continental AG on object detection. Surajit Dutta will then present the topic of his master thesis on balancing training sets for support vector machines and its application to the detection of rare events.

Implementation of bar learning based on a triplet STDP Model by Clopath et al.

Rene Larisch

Wed, 22. 10. 2014, Room 1/336

Models with Spike Timing Dependent Plasticity (STDP) are based on the temporal offset between the spikes of the post- and presynaptic neuron. We realized a STDP Model with triplet characteristic proposed by Clopath, Büsing, Vasilaki & Gerstner (2010) in a network with 20 excitatory Neurons and 20 inhibitory neurons to learn bars and to study the model dynamics, like the homeostatic mechanism, and the effect of the triplet property.

Role of competition in robustness against loss of information in feature detectors.

Arash Kermani

Tue, 26. 8. 2014, Room 1/336

In this talk different methods of competition among units of feature detectors will be explained. As a new criterion for effectiveness of competition, robustness of classification under loss of information will be discussed.

Motion detection and receptive field dynamics in early vision processes.

Tobias Höppner

Thu, 19. 6. 2014, Room 1/336

Motion detection and processing is a striking feature of the visual system. Although higher visual areas play an important role in motion processing, the basic detection already begins in retinal ganglion cells. A model based of simple ode's will be presented and compared to existing approaches.

Energy and execution time models for an efficient execution of scientific simulations

Jens Lang

Thu, 5. 6. 2014, Room 1/336

The energy efficiency of computation gains in importance in the area of scientific computing with the energy consumption having a significant impact on the cost of operating big clusters. The thesis presented in this talk uses model-based autotuning for adapting the execution of scientific simulations to the underlying hardware and thus making it more efficient. The term 'efficiency' is regarded here considering both, execution time and energy.

The role of the subthalamic nucleus globus pallidus loop in habit formation.

Javier Baladron Pezoa

Thu, 15. 5. 2014, Room 1/336

The role of the feedback loop between the subthalamic nucleus and the external section of the globus pallidus in the generation of abnormal oscillations in Parkinson's disease has been widely studied both theoretically and experimentally but its role during the learning of stimulus-action association is unknown.
In this presentation I will describe an extension of our spiking model of the basal ganglia that provides new theoretical insights about the function of the subthalamic nucleus during habit formation. This new approach was developed by including the connections between the STN and the GPe that were not present in previous models.
The new network predicts that the STN plays an important role during reverse learning by maintaining the inhibition of alternative actions that was learned by the D2 cells in the striatum.

Entwicklung eines parallelen genetischen Algorithmus zur Maschinenbelegungsplanung (Diplomarbeit Themenvorstellung)

Martin Wegner

Thu, 8. 5. 2014, Room 1/336

Genetische Algorithmen gelten als leistungsfähige Lösungsverfahren für praxisrelevante Planungsprobleme aus den Bereichen Produktion und Logistik. Ziel der Arbeit ist die Entwicklung eines genetischen Algorithmus mit einem Inselmodell und der Vergleich der Leistungsfähigkeit dieses Konzeptes mit anderen Lösungsverfahren. Die Besonderheit dieser Form der Parallelisierung ist die Nutzung verschiedener verknüpfter Populationen.

A Possible Role of the Basal Ganglia in the Spatial-To-Temporal Transformation in Saccadic Eye Movements: A Computational Model

Abbas Al Ali

Wed, 16. 4. 2014, Room 1/368a

A new model system consisting of a model of the superior colliculus, a model of the basal ganglia, and a saccade generator, will be presented. The superior colliculus is known to be involved in controlling saccades. Saccade vectors encoded as activity on the SC motor map are transformed into temporal code which results in the stereotyped dynamic saccadic behaviour. Based on the proposed model a colliculo-thalamo-basalganglio-collicular oculomotor loop, may play a role in generating the temporal profiles of the SC neurons addressing a role of the basal ganglia in controlling saccades dynamics.

Implementierung und Optimierung eines Trace Learning Modells auf einer Coil100 Datenbank (Bachelor Verteidigung).

Daniel Buchholz

Thu, 10. 4. 2014, Room 1/336

Im folgenden Vortrag werden kurz die Grundlagen eines Tracelearning-Modells und seiner Anwendung auf die Coil100- Datenbank beleuchtet. Danach wird gezeigt, wie mithilfe einer geeigneten Klassifizierung Erkennungsraten von weit über 90% erreicht werden können und auf welche Art und Weise die Effektivität des Modells dargestellt werden kann. Zum Schluss werden weitere Möglichkeiten und Probleme dieses Modells präsentiert.

Der Einfluss von Emotionen auf Reinforcement Prozesse in einer virtuellen Realität

Winfried Lötzsch

Tue, 25. 2. 2014, Room 1/336

Autonome Agenten und deren Steuerungsmechanismen können in ihren Entscheidungsprozessen von der Umgebung oder inneren Zuständen beeinflusst werden. Auf Grundlage der Besonderen Lernleistung zu diesem Thema wird die konzeptionelle Erweiterung eines bestehenden Modells beschrieben. Im Mittelpunkt steht auch die grafische Simulation des Agenten und seiner Umwelt.

Implementierung von Zustandsabstraktionsmechanismen für Reinforcement Learning Agenten (BA Verteidigung)

Fabian Bolte

Wed, 12. 2. 2014, Room 1/368

Im Projekt 'Smart Virtual Worker' wird ein Werkzeug für die Simulation und Evaluation von Arbeitsprozessen entwickelt. Die Erstellung einer Simulation erfordert einen hohen Arbeitsaufwand bzw. Expertenwissen. Um die benötigte Zeit für die Erstellung einer Simulation zu reduzieren, soll der virtuelle Arbeiter mit der Fähigkeit Handlungsfolgen autonom zu lernen ausgestattet werden. Vorgestellt wird eine aktuelle Agentenvariante, die im Rahmen der Bachelorarbeit um Zustandsabstraktionsmechanismen erweitert wurde.

Dopamine ramps up?

Julien Vitay and Helge Dinkelbach

Tue, 14. 1. 2014, Room 1/336

A recent experiment by (Howe et al., Nature 2013) showed that the dopamine concentration in the striatum increases linearly during simple T-maze experiments. We will collectively discuss the functional implications of this finding and present a preliminary explanation by Samuel Gershman using quadratic functions of the distance to the goal in a modified TD algorithm. We will end with a discussion on the proposed new features of ANNarchy 4.0.

Entwicklung eines Software-Moduls zur Implementierung der kortikalen Vergrößerung (Bachelorverteidigung)

Andreas Heinzig

Tue, 17. 12. 2013, Room 1/336

In dieser Bachelorarbeit geht es um die Implementierung eines Software-Moduls zur Realisierung der kortikalen Vergrößerung. Die kortikale Vergrößerung ist ein Effekt der bei der Weiterleitung und Bearbeitung von Bildinformationen im Gehirn auftritt. Die genaue Aufgabenstellung beinhaltet die Entwicklung in den Test eines Software-Moduls welches in C++ programmiert ist und eine parametrisierbare, kortikale Vergrößerung implementiert. Es soll 1- und N-kanalige Bilder einzeln und als Bildfolgen transformieren und dabei eine oder mehrere geeignete Interpolationsmethoden verwenden. Der Aufruf als Standalone-Programm soll möglich sein, wobei binäre Dateien als Eingabebilder akzeptiert und Ausgabebilder wieder als solche ausgegeben werden. Allerdings soll das Modul auch in Verbindung mit einer virtuellen Umgebung funktionieren. Es soll eine bidirektionale Transformation zwischen dem visuellen und dem kortikalen Raum durchführen können und auch die Möglichkeit parametrische, geometrische 2D-Figuren (Punkte, Linien, Kreise,...) zu transformieren soll gegeben sein.
Bewerkstelligt wurde dies durch zwei Demo-Programme. Zum einen ein Standalone-Programm welches eine binäre Datei einliest, das Bild transformiert und als binäre Datei wieder ausgibt. Dabei wird das Bild mittels Matlab in eine binäre Datei geschrieben und das Ausgabebild aus der Ausgabedatei wieder auslesen und angezeigt. Zum anderen wurde das Modul als Klasse in die VR-Demo (virtuelle Realität) implementiert.

Timing and expectation of reward: a model of the afferents to VTA

Julien Vitay

Tue, 10. 12. 2013, Room 1/368a

As reflected by the firing patterns of dopaminergic neurons in the ventral tegmental area, temporal expectation is an important component of Pavlovian conditioning. Predicting when a reward should be delivered after the onset of a predicting cue allows to both reduce the associated surprise and avoid over-learning, and to estimate when one should be disappointed by the omission of an expected reward. Several models of the dopaminergic system during conditioning exist, but the substrate of temporal learning is rather unclear. We propose a neuro-computational model of the afferent network to the ventral tegmental area, including the lateral hypothalamus, the pedunculopontine nucleus, the amygdala, the ventromedial prefrontal cortex, the ventral basal ganglia (including the nucleus accumbens and the ventral pallidum), as well as the lateral habenula and the rostromedial tegmental nucleus. Based on a plausible connectivity and realistic learning rules, this neuro-computational model reproduces several experimental observations, such as the progressive cancellation of dopaminergic bursts at reward delivery, the appearance of bursts at the onset of reward-predicting cues or the influence of reward magnitude on activity in the amygdala and ventral tegmental area. While associative learning occurs primarily in the amygdala, learning of the temporal relationship between the cue and the associated reward is implemented as a dopamine-modulated coincidence detection mechanism in the nucleus accumbens.

A spiking neural network based on the basal ganglia functional anatomy

Javier Baladron Pezoa

Tue, 3. 12. 2013, Room 1/336

In this talk I will present a new spiking neural network whose connectivity is defined following anatomical descriptions of the different cortico thalamic pathways. The network is capable of learning action-response association using dopamine modulated spike timing dependent plasticity. The functionality of each pathways is defined following the paper by Schroll et al (2013) and I will emphasize the different between this approach and previous spiking models of the basal ganglia.

Locomotive Brain Model for Humanoid Robots

Dr. John Nassour

Tue, 12. 11. 2013, Room 1/336

More information

Tourette Syndrome - State of the current research and the generation of hypotheses with a computer model of the basal ganglia.

Karoline Griesbach

Tue, 5. 11. 2013, Room 1/336

The Tourette Syndrome (TS) is a developmental neurological disorder. Research discusses different reasons for the TS, for example aberrant patterns of neuron activity, for instance in neurons of the striatum or abnormalities of neurotransmitters such as in dopamine. In my Bachelor thesis I summarize the status of research and generate hypotheses with the help of a computer model based on the model from Hamker, Schroll & Vitay (2013). The goal is to show the characteristics of TS - especially the tics - using a task-switch-paradigm which the model has to solve.

Advances in Neuro-robotics

Dr. Andrea Soltoggio

Tue, 5. 11. 2013, Room 1/368a

More information

Scalable item recommendation in big data and search indexes (diploma thesis defense)

Tolleiv Nietsch

Tue, 29. 10. 2013, Room 1/336

The integration of search technologies with machine learning technology provides various ways to personalize search results and to improve search quality for the users. In my diploma thesis, two possible ways to reach this goal have been described along with the requirements to reach scalability. It compares a web-service based solution with an integrated matrix factorization approach and describes the architectural requirements to build both systems with OpenSource tools like Apache Mahout and Apache Solr. In this presentation, I'll sum up work from my diploma thesis, present the results I found and the methods I used.

Category learning by using a visual motor policy including eye movements

Robert Blank

Wed, 17. 7. 2013, Room 1/336

The Basal Ganglia (BG) plays an important role in cognitive processes like decision making and eye movement. In this work, the controlling of saccades is investigated. A model has to solve an abstract visual categorization task and has additionally to learn how to acquire visual information. An external process executes randomly saccades, hence the model can gather reliable information through saccades to diagnostic features. Thus, the model has to learn to wait for these saccades which is possible through the interplay of the direct, indirect and hyperdirect pathway of the Basal Ganglia.

Intrinsically Motivated Action-Outcome Learning and Goal-Based Action Recall

Orcun Oruc

Tue, 16. 7. 2013, Room 1/336

I will present the article 'Intrinsically motivated action-outcome learning and goal-based action recall: A system-level bio-constrained computational model' by Baldassarre, Redgrave, Gurney and colleagues (2013). This model investigates the influence of intrinsic motivations (based on novelty and curiosity) on the learning of a rewarded exploration task (extrinsic motivation). Its is composed of three cortico-basal ganglia loops, separately processing arm movements, saccadic generation and outcome evaluation but coordinated by dopaminergic modulation.

Intrinsic plasticity

Lucas Keller

Tue, 9. 7. 2013, Room 1/336

Coming soon...

Inhibition through inhibitory interneurons as replacement for lateral inhibition

Michael Göthel

Tue, 2. 7. 2013, Room 1/336

The lateral inhibition between two or more excitatory neurons should be replaced through a new layer of inhibitory neurons, which express the same behavior of the network as using lateral inhibition. Using the results of some experiments with two types of example networks, I will discuss the question if that is even possible and how to deal with some problems like choosing the right limitation of the weights (alpha value) in the network.

Sparse coding and non-negative matrix factorization

Dr. Steinmüller

Tue, 25. 6. 2013, Room 1/336

The talk covers the topics 'Sparse solutions of systems of equations', 'Sparse modeling of signals and images' and 'Non-negative matrix factorization as the generalization of PCA and ICA'.

Biological foundations for computational models of structural plasticity

Maxwell Shinn

Tue, 18. 6. 2013, Room 1/B006

Structural plasticity is the ability of a neural network to dynamically modify its connection patterns by changing the physical structure of the neurons. In this presentation, I discuss what biological experiments have taught us about structural plasticity, and how these results can be used to build more accurate computational models of plastic neural networks.

Animation and simulation of muscles skeleton models - examples of AnyBody

H. Niemann

Tue, 11. 6. 2013, Room 1/336

Each human being has over 200 bones, over 300 joints and more than 600 muscles (Gottlob, 2009). Today there are only certain assumptions and simplifications to model them. One goal of muscles skeleton models is visualisation/animation for vivid impressions how muscles work (also in dysfunctional work like Parkinson). The talk presents an application-orientated introduction to muscle skeleton models using the AnyBody Modelling system. Examples of animation and simulation are shown for movements of the lower extremities. An integrated discussion refers to current possibilities and limits of muscles skeleton models. The majority of researchers use the method of inverse kinematics as inputs of muscles skeleton models. Even though these are still dreams of the future, first steps are shown by introducing motoneurons as inputs. In current research a full alternative replacement of inverse kinematics is - not yet - available.

ANNarchy 3.1 - feature discussion meeting

Helge Dinkelbach

Tue, 21. 5. 2013, Room 1/336

It is planned to introduce a new interface and new features in the neuronal network simulator, ANNarchy. This meeting is intended to present the current version of ANNarchy and to discuss ideas of the next version (ANNarchy 3.1).

Modeling the SSD task of a patient with lesioned thalamus

Julia Schuster

Tue, 14. 5. 2013, Room 1/336

While observing the environment, our eyes move to different points of attention several times each second. Nevertheless, we perceive the visual world as stable and as a result small displacements of visual targets cannot be detected well during such eye movements ? a phenomenon called saccadic suppression of displacement (SSD). Recently, Ostendorf et al. presented data of a patient with a right thalamic lesion showing a bias towards perceived backward displacements for rightward saccades in the SSD-task. To better understand the nature of the behavioral impairment following the thalamic lesion we applied a computational model developed by Ziesche and Hamker to simulate the patient. In this presentation, I will introduce the used model as well as the results of the simulation.

Learning Categories by an interaction between fast and slow plasticity

Francesc V. Escudero

Tue, 7. 5. 2013, Room 1/336

It will be explain how we think category learning happen in our brain. The aim of the presentation is to introduce the background of the project I will start working in.

A computational model of hippocampal forward replay activity at decision points

Lorenz Goenner

Tue, 30. 4. 2013, Room 1/336

During rodent navigation, hippocampal place cells are activated in a sequence corresponding to the animal's trajectory. A recent topic of interest is neural activity corresponding to the replay of sequences of place cells during sleep and awake resting, and its possible link to learning and memory. I will present a model for the generation of forward replay activity in a T-maze. It becomes evident that correct replay requires the disambiguation of overlapping sequences.

Combining scalable Item-Recommendation and Searchindexes

Tolleiv Nietsch

Tue, 16. 4. 2013, Room 1/336

Running internet services today often comes with the requirements to make big amounts of data available to a large crowd of users. Having flexible search systems to make sure information can be found easily is one step towards that. Collaborative recommendation algorithms could be used to enrich and personalise search results and to raise the possible gain for single users. The presentation will give an overview of some of the theoretical foundations, cover the work done so far in the related diploma thesis and explain the chosen system-setup along with the related OpenSource software tools.

Numerical analysis of large scale neural networks using mean field techniques

Javier Baladron-Pezoa (Javier Baladron Pezoa. NeuroMathComp Project Team, INRIA)

Tue, 12. 2. 2013, Room 1/336

In the first part of this talk I will introduce you to a new mean field reduction for noisy networks of conductance based model neurons. This approach allow us to describe the dynamics of a network by a McKean-Vlasov-Fokker-Planck equation. On a second part of this talk I will show you several simulations whose objective was to study the behav- ior of extremely large networks (done with a GPU cluster). Read more

Integration von kognitiven Modellen zur Entwicklung eines virtuellen Agenten

Michael Schreier

Mon, 28. 1. 2013, Room 1/336

Der Vortrag stellt die neuronale Implementierung eines kognitiven Agenten in einer Virtuellen Realität dar. Im Rahmen eines Praktikums wurden neuronale Modelle verschiedenster Gehirnareale (Visual Cortex, Frontal Eye Field, Basal Ganglia) zusammen integriert um einen kognitiven Agenten zu simulieren. Es soll so eine Gehirnsimulation für zukünftige Forschungen bereitgestellt werden.

A neurobiologically founded, computational model for visual stability across eye movements

Arnold Ziesche

Thu, 3. 1. 2013, Room 1/336

ANNarchy 3.0. Presentation of the user-interface.

Julien Vitay and Helge Dinkelbach

Mon, 10. 12. 2012, Room 1/208A

ANNarchy 3.0 is the new version of our neural simulator. The core of the computations is written in C++, with optionally a parallel optimization using openMP. Its structure has changed quite a lot since ANNarchy 1.3 or 2.0, so newbies as well as experienced users may equally benefit from the presentation.
The major novelty is that the main interface is now in Python, thanks to the Boost::Python library. It allows to easily define the structure of a network, visualize its activity and find the correct parameters through a high-level scripting language. The basics of this interface will be presented.

Cortico-basal ganglia loop and movement disorders

Atsushi Nambu (Division of System Neurophysiology, National Institute for Physiological Sciences, Japan)

Mon, 3. 12. 2012, Room 1/336

Modeling of stop-signal tasks using a computational model of the basal ganglia.

Christian Ebner

Mon, 12. 11. 2012, Room 1/336

Selecting appropriate actions and inhibiting undesired ones are fundamental functions of the basal ganglia. An existing computational model of the BG is considered to learn the inhibition of premature decisions using a suitable task. The possible influence of the hyperdirect pathway is to be illustrated.

Spike timing dependent plasticity and Dopamine in the Basal Ganglia.

Simon Vogt

Wed, 7. 11. 2012, Room 1/336

Our brain's Basal Ganglia have historically been proposed for a wide number of brain functions including sensory and motor processing, emotions, drug addiction, decision making, and many more. They are also the main area involved in neurological diseases like Parkinson's Disorder or Chorea Huntington. Current clinical treatments tend to only delay symptoms pharmaceutically or through surgery for a limited time, and have seemed to follow a trial-and-error approach to understanding the basal ganglia. In order to give a better explanation for neurological diseases and the many side effects that today's clinical treatments cause, we should try to understand the basal ganglia's network dynamics, learning paradigms, and single-spike timing features from an information processing perspective.
In my talk, I will re-examine the basis for how high-level reinforcement learning is often assumed to be implemented within the spiking networks of the basal ganglia's Striatum, and show how lower-level dopaminergic modulation of synaptic transmission may guide higher-level network learning while also displaying instant responses in neuronal spiking activity.
By improving our understanding of the neural spike code of the striatum and other basal ganglia nuclei to a depth where we can truly decode multisite electrophysiological recordings of spiking neurons, we will be able to devise better, more informed treatments of typical basal ganglia disorders in the future.

The guidance of vision while learning categories - A computational model of the Basal Ganglia and Reinforcement Learning

Robert Blank

Mon, 5. 11. 2012, Room 1/336

The Basal Ganglia (BG) plays a very important role in cognitive processes like decision making or body movement control. A biologically plausible model of BG solving an abstract visual categorization task will be presented in this work. The model learns to classify four visual input properties into two categories while only two properties are important, called diagnostic features. The results show, that the model is not able to differentiate between diagnostic features and unimportant ones. Finally it just memorizes the presented input and shows only weak abilities of generalization.

Fear conditioning in the Amygdala

René Richter

Thu, 5. 7. 2012, Room 1/367

How do we connect emotions with objects/situations? We will try to find out about this in the special case of fear and take a look at a computational model of the amygdala. Also we will take a closer look at one part of the amygdala, the basal amygdala, where most likely context conditioning takes place.

Learning of V1-simple cell receptive fields using spike-timing dependent plasticity

René Larisch

Thu, 7. 6. 2012, Room 1/336

Spike-timing dependent plasticity, or short STDP, represents an interesting option in the consideration of neuronal networks. This presentation will give an overview about three different views how a STDP network could be realised. The focus is taken on the main features, machanism and how these models realise the development of rezeptive fields.

Comparison of GPUs- (graphic processing units) and CPUs-implementations for neuronal models.

Helge Ülo Dinkelbach

Thu, 24. 5. 2012, Room 1/336

In the past years, the computational potential of multiprocessors (CPUs) and graphic cards (GPUs) increased. On the other hand it gets more and more complicated to evaluate which technologies are usable for a certain computation problem. The available computation frameworks (OpenCL and Cuda for GPUs; OpenMP for CPUs) are not always easy to handle in all use cases. The objective of this talk is to show how the potential of modern parallel hardware could be used for computation of neuronal models. Additional some limititions will be discussed.

Categorization - How humans learn to understand their environment and to select appropriate actions.

Frederik Beuth

Thu, 10. 5. 2012, Room 1/336

Dividing objects into categories is one of the remarkable human abilities, but its neuronal basis and its impressive fast execution is still little understood. I will introduce the neuronal foundations of all three components involved in visual-based categorization: 1) visual information, 2) categories, 3) actions, and the linkage of them together. In the main part, I will give an overview how humans learn to understand their environment and to select appropriate actions. Humans relay on at least three different systems for learning 1) motor skills, 2) concepts and 3) hypothesis. In combination, these systems result in a very powerful learning in order to select the correct response for a specific situation. From the computer science's view, this approach could be used for object recognition.

The Hippocampus - a brain structure for spatial and episodic memory

Lorenz Gönner

Thu, 3. 5. 2012, Room 1/336

Even after decades of research, the hippocampus continues to inspire researchers by a wealth of phenomena. As an introduction to the field of hippocampus research, I will provide a panoramic view of past and present topics both in behavioral and computational neurosciences. A focus will be on the role of the hippocampus in the learning of goal-directed behavior.

Basal ganglia pathways: Functions and Parkinsonian dysfunctions

Henning Schroll

Thu, 26. 4. 2012, Room 1/336

I will review functional contributions of basal ganglia pathways in reinforcement learning, also their dysfunctions in Parkinson's disease will be addressed. Using computational modeling, I will attempt an integration of functions and dysfunctions in a single analytical framework.

Motion detection and receptive field dynamics in early vision processes

Tobias Höppner

Thu, 19. 4. 2012, Room 1/336

Temporal changes in receptive field structures are a field of growing interest. The early vision processing stages are not only concerned with spatial decorrelation of visual representations but also process the temporal information conveyed by the retinal signal. Therefore changing receptive field structures as seen in physiological experiments are investigated. Notably biphasic responses are considered to form lagged and nonlagged neuronal responses which in turn are a promising cause for spatio-temporal receptive field dynamics.

An Extensible and Generic Framework With Application to Video Analysis

Marc Ritter

Thu, 12. 4. 2012, Room 1/336

This presentation gives insights into the outcomes of the project sachsMedia in the field of metadata extraction by video analysis while introducing a holistic, unified and generic research framework that is capable of providing arbitrary application dependent multi-threaded custom processing chains for workflows in the area of image processing. Read more

Runtime optimal partitioning of neural networks (Bachelor-Verteidigung).

Falko Thomale

Wed, 8. 2. 2012, Room 1/368a

This bachelor thesis investigates the parallel execution of the neural network simulator ANNarchy with the help of the OpenMP API and taking advantages of the NUMA architecture of modern computers. The NUMA architecture allows a faster access to the memory by splitting the memory for simulation into multiple partitions and let each partition run on a separate NUMA node. The modifications are tested with random neural networks and two networks with practical importance and the test results of memory placement improvements are shown and discussed.

Computational Modelling of the Oculomotor System.

Abbas Al Ali

Thu, 19. 1. 2012, Room 1/336

Previous works of our group have come out with a series of models describing the crucial role of the basal ganglia (BG) in learning rewarded tasks. BG are shown to be a central part of many cortico-BG-thalamo-cortical loops, such as the visual working memory loop and the motor loop, within which Dopamine is the reward-related learning modulator. BG are also known to play a role in controlling purposive rapid eye movements (saccades). Saccades are driven by the superior colliculus (SC) in the brain stem which has connections from many visual related cortical areas as well as from BG. We want to transfer knowledge gained by previous models and integrate it in a distributed oculomotor system that contains a BG-model, the frontal eye field, SC and some other cortical areas in oder to investigate the emergent behaviour in learning rewarded reactive and goal-guided saccadic eye movement tasks.

Schnelle GPGPU-basierte Simulation Neuronaler Netze (Master-Verteidigung)

Helge Ülo Dinkelbach

Thu, 15. 12. 2011, Room 1/336

Simulationen im Bereich Computational Neuroscience haben eine hohe Laufzeit, sind aber gleichzeitig gut parallelisierbar. Moderne Grafikkarten verfügen über eine sehr hohe Anzahl an Rechenkernen, die für die Ausführung paralleler Programme zur Verfügung stehen. In der Präsentation wird die Beschleunigung des an der Professur entwickleten Neurosimulator vorgestellt, wobei als Ansätze CUDA undObjektorientierung genutzt wurden.

Computational Model for Learning Features in Area V2

Norbert Freier

Thu, 8. 12. 2011, Room 1/336

Computer vision is often used for object recognition. But it can not handle every situation. The brain of primates outperforms every available method. Future algorithms may will reproduce the techniques of the brain. Therefore it is necessary to know how the visual system works. As result of this Studienarbei a computational model of area V2 will be presented in comparison to cell recordings and other related work.

Laufzeitoptimale Aufteilung neuronaler Netze.

Falko Thomale

Thu, 24. 11. 2011, Room 1/336

Die Simulation großer neuronaler Netze benötigt viel Rechenleistung. Der an der Professur entwickelte Neurosimulator ANNarchy nutzt bereits OpenMP für eine parallelisierte Ausführung auf Multicore-Systemen. In diesem Vortrag möchte ich dieses Vorgehen näher betrachten und eine mögliche Verbesserung der Parallelisierung darstellen, indem das zu simulierende neuronale Netz optimal auf die Recheneinheiten verteilt wird.

NNSpace - Eine generische Infrastruktur für neuronale Netze

Winfried Lötzsch

Thu, 17. 11. 2011, Room 1/336

Um moderne neuronale Netze effizient zu nutzen, sind oft komplexe Aufbauprozesse basierend auf mehreren Algorithmen und Netzstrukturen erforderlich. Eine automatisierte Generierung dieser Netze ohne Programmieraufwand war bisher nur in bestimmten Spezialfällen möglich. Die Infrastruktur NNSpace erreicht durch die Möglichkeit neuronale Netze zu kombinieren, dass viele Anwendungsfälle aus Standardbausteinen zusammengesetzt werden können. Außerdem könnte jene Kombination die Leistung und generelle Anwendbarkeit neuronaler Netze steigern. Der Vortrag stellt die Infrastruktur an einem Beispiel vor und geht auf ihre interne Funktionsweise ein.

Entwicklung eines kognitiv-emotionalen Interaktionsmodells der Amygdala und des Hypothalamus.

Martina Truschzinski

Thu, 27. 10. 2011, Room 336

Emotionen sind wichtige Bestandteile des menschlichen Bewusstseins und liefern einen wesentlichen Beitrag zur Effizienz und Leistungsfähigkeit des Gehirns. Sie beeinflussen sowohl kognitive Prozesse, wie die Wahrnehmung, die Lernfähigkeit und subjektive Bewertungen, als auch Reaktionsmechanismen, die auf Grundlage dieser kognitiven Prozesse generiert wurden. Ausgehend vom MOTIVATOR-Modell (Dranias, Gross- berg und Bullock, 2008) wird ein kognitiv-emotionales Modell auf neuen neuroanatomischen und physiologischen Erkenntnissen vorgestellt. Der Fokus liegt auf der Interaktion zwischen den Gehirnarealen der Amygdala und dem Hypothalamus.

Alterations in basal ganglia pathways impair stimulus-response learning in Parkinson's Disease: A computational model.

Henning Schroll

Thu, 13. 10. 2011, Room 336

I present a computational model of how basal ganglia pathways contribute stimulus-response learning. When introducing Parkinsonian lesions to this model, an imbalance in the activities of basal ganglia pathways arises and causes learning deficits typical for Parkinsonian patients.

Hierarchisches Reinforcement Learning

Vincent Küszter

Wed, 29. 6. 2011, Room 1/B309a

Maschinelles Lernen, zu dem auch Reinforcement Learning gehört, bildet einen wichtigen Zweig der Künstlichen Intelligenz und Robotik. Das klassische Reinforcement Learning, wie es zur Entscheidungsfindung von Soft- und Hardware-Agenten eingesetzt wird, hat jedoch mehrere Nachteile - schlechte Skalier- und Genrealisierbarkeit. Ein Ansatz von Matthew M. Botvinick, Yael Niv und Andrew C. Barto versucht diese Probleme mit Hilfe von hierarchischen Policy-Strukturen zu lösen. Dieser Vortrag stellt die Methode und ihre neuronalen Grundlagen vor.

Support Vector Machines -- Eine Einführung

Stefan Koch

Wed, 22. 6. 2011, Room 1/B309a

Zur Entwicklung künstlicher intelligenter Systeme bedarf es leistungsfähiger Klassifikatoren. In den 90er Jahren wurden durch Vladimir Vapnik und Alexey Chervonenkis Untersuchungen der statistischen Eigenschaften von Lernalgorithmen vorgestellt. Basierend auf deren Arbeit wurden die Support Vector Machines (SVMs) entwickelt. Diese stellen eine neue Generation von statistischen Klassifikatoren dar, welche den Anspruch erheben eine hohe Leistungsfähigkeit bei realen Anwendungen zu besitzen. Aufgrund der einfachen Anwendbarkeit auf viele Problemstellungen erfreuen sie sich in den letzten Jahren immer größerer Beliebtheit. Im Rahmen des Forschungsseminars wird eine Einführung in die Thematik der Support Vector Machines gegeben.

Die Suche nach Informationen im Gedächtnis resultiert in Blickbewegungen an den Ort der Informationsaufnahme.

Agnes Scholz

Wed, 15. 6. 2011, Room 1/B309a

Beim Abruf von Informationen aus dem Gedächtnis blicken Personen an den Ort der Informationsaufnahme zurück, selbst wenn die gesuchte Information dort nicht mehr vorhanden ist (Ferreira, Apel & Henderson, 2008, Trends in Cognitive Science, 12(11), 405). Zwei Experimente untersuchten dieses Blickphänomen beim Erinnern zuvor gehörter Eigenschaftsausprägungen fiktiver Objekte und beim Hypothesen testen. Durch das Verfolgen von Blickbewegungen beim Erinnern und beim diagnostischen Schließen ist es möglich gedächtnisbasierte Prozesse der Informationssuche zu beobachten. Die Funktion visuell räumlicher Aufmerksamkeitprozesse für die Erklärung dieser Befunde wird diskutiert.

Objekterkennung mittels der generalisierten Hough-Transformation basierend auf der parallelen Hough-Transformation

Abbas Al Ali

Wed, 8. 6. 2011, Room 1/B309a

Die HT (Hough-Transformation) ist ein weit verbreitetes Verfahren im Bereich der Objekterkennung, welche zum Detektieren parametrischer Kurven in digitalen Bildern dient. Die GHT (Generalisierte HT) ist eine von vielen Variationen der HT, die zum Detektieren beliebiger Kurven eingesetzt wird, wobei Gradientinformationen der Kanten in einem Graustufenbild für das Vorhandensein einer Kurve votieren. Die neulich am Fraunhofer-Instituts für Digitale Medientechnologie IDMT entwickelte sogenannte PHT (Parallele HT) ist ein Echtzeitsystem, das vorbestimmte Muster, wie Geradenstücke oder Kreisbogen, lokal in einem Bild detektiert. In dem Vortrag wird die PHT für Geradenstücke kurz erläutert und eine GHT-Implementierung mit Verwendung des PHT-Systems als Feature-Extraktor zur Gewinnung von Shape-Modellen in Form von R-Tabellen (Referenztabellen) und zur Objekterkennung präsentiert. Tests an synthetisierten Bildern zeigen, dass ideale Klassifikationseigenschaften (eine Erkennungsrate von 100% mit einer Falsch-Positiv-Rate von 0%) erreichbar sind.

Learning invariance from natural images inspired by observations in the primary visual cortex

Michael Teichmann

Tue, 26. 4. 2011, Room 1/336

The human visual system has the remarkable ability to recognize objects invariant of their position, rotation and scale. In part, this is likely achieved from early to late areas of visual perception. For the problem of learning invariances, a set of Hebbian learning rules based on calcium dynamics and homeostatic regulations of single neurons is presented. The performance of the learning rules is verified within a model of the primary visual cortex to learn so called complex cells, based on a sequence of static images. As result the learned complex cells responses are largely invariant to phase and position.

The contribution of basal-ganglia to working memory and action selection

Fred Hamker

Tue, 19. 4. 2011, 4/203 (Wilhelm-Raabe-Str.)

Forschungskollogium Psychologie - Seminar: Aktuelle Themen der Kognitionswissenschaft

Entwicklung einer Schnittstelle zur Simulation eines kognitiven Agenten mit aktiver Umweltinteraktion in einer Virtuellen Realität

Xiaomin Ye

Mon, 17. 1. 2011, Room 1/336

Um Interaktives Lernen im Gebiet Computational Neuroscience durchzuführen ist es eine Möglichkeit den menschliche Körper und sein Gehirn durch einen Agenten zu repräsentiert und die umgebenden Welt des Agenten durch eine Virtual Reality(VR) Umgebung. Die vorliegende Diplomverteidigung wird eine Basisimplementierung für diesen Forschungsansatz mit der VR Umgebung Unity3D präsentieren. Der Agent wird über über folgende Sensoren verfügen: zwei Augen (zwei virtuelle Kameras) und Haut (Kollisionssensoren). Ebenso wird er einfache Aktionen wie Laufen, ein Objekt ergreifen und Augen/Kopfbewegungen ausführen können.
Die Arbeit fokusiert sich auf die Fähigkeiten von Unity3D und die Programmierung der Schnittstellen zwischen VR-Umgebung und Agent. Zusätzlich wird die Arbeit an einem Reinforcement-Learning-Agenten demonstriert werden, welcher den Weg durch ein Labyrinth finden kann.

Das kognitiv-emotionale Modell MOTIVATOR

Martina Truschzinski

Wed, 12. 1. 2011, Room 1/336

Das kognitiv-emotionalen Modell MOTIVATOR, welches auf neuroanatomischen und -physiologischen Erkenntnissen entwickelt wurde und innerhalb der Diplomarbeit an neue Erkenntnisse angepasst werden soll, wird vorgestellt. Die Entwicklung, basierend auf neuronalen Netzen, stellt eine Interaktion zwischen Kognition, Motivation und Emotion bereit. Anpassungen und Neuerungen am MOTIVATOR-Modell werden vorgestellt und diskutiert.

Kognitive Architekturen - Eine Einführung in ACT-R

Diana Rösler

Wed, 5. 1. 2011, Room 1/336

Kognitive Architekturen werden entwickelt, um einen Beitrag zum Verständnis menschlicher Kognitionen zu leisten. Basis für die Entwicklung dieser Modellierungsansätze bilden häufig kognitionspsychologische Theorien. Im Vortrag wird ACT-R (Adaptive Control of Thought - Rational) - eine Umsetzungsform von kognitiven Architekturen - vorgestellt. Aktuelle Erweiterungen von ACT-R (Anderson, et al. 2004) stehen dabei im Mittelpunkt.

Invariant Object Recognition

Norbert Freier

Wed, 15. 12. 2010, Room 1/336

The human brain performs well on the task of object recognition. In the past, serveral models have tried to explain how the brain does this task. But up to now any system is outperformed by nature and the operations of the visual stream are not fully understanded. As Examples two models will be examined and compared to other aproaches.

A detailed examination at a Model with Calcium driven homeostatic dynamics with metaplasticity for visual learning

Jan Wiltschut

Wed, 1. 12. 2010, Room 1/068 (not 1/336!)

The Calcium model is based on the following electrophysiological findings: 1) Learning plasticity, that means the Long-Term-Potentiation (LTP) and Long-Term-Depression (LTD) characteristics of Calcium based synaptic learning; 2) Self-regulating mechanisms (scaling, constraint and redistribution); 3) Maintenance and consolidation of learned connections dependent on the strength of synaptic change.
The model is introduced and network characteristics are compared to electrophysiological data. Learning results are shown and will be discussed as well.

Schnelle Objekterkennung mittels CUDA und OpenCL

Helge Dinkelbach und Tom Uhlmann

Wed, 24. 11. 2010, Room 1/336

Sehr viele Algorithmen im Gebiet der Künstlichen Intelligenz haben eine hohe Laufzeit, sind aber gleichzeitig gut parallelisierbar. Moderne Grafikkarten verfügen über eine sehr hohe Anzahl an Rechenkernen, die für die Ausführung paralleler Programme zur Verfügung stehen. Im Rahmen eines Forschungsseminars wurden die zwei Frameworks, die aktuell zur Verfügung stehen (OpenCL und CUDA), für einen Beispielalgorithmus (aus dem Gebiet Objekterkennung/Computational Neuroscience) genutzt und die Ergebnisse verglichen.

A computational model of predictive remapping and visual stability in area LIP

Arnold Ziesche

Wed, 3. 11. 2010, Room 1/336

Cells in many visual areas are retinotopically organized, i.e. their receptive fields (RFs) are fixed on the retina and thus shift when the eye moves. Hence, their input changes with each eye movement, posing the question of how we construct our subjective experience of a stable world. It has been proposed that predictive remapping could provide a potential solution. Predictive remapping refers to the observation that for some neurons retinotopically organized RFs anticipate the eye movement and become responsive to stimuli which are presented in their future receptive field (FRF) already prior to the saccadic eye movement. Here I show that predictive remapping emerges within a computational model of coordinate transformation in LIP. The model suggests that predicitive remapping originates in the basis function layer from the combined feedback from higher, head-centered layers interacting with the corollary discharge signal. Furthermore it predicts a new experimental paradigm to find predicitive remapping cells.

Unifying working memory and motor control.

Henning Schroll

Wed, 27. 10. 2010, Room 1/336

Basal ganglia have been shown to substantially contribute to both working memory and motor control. In my talk I will present a computational model that unifies both of these functions: Within an anatomically inspired architecture of parallel and hierachically interconnected cortico-basal ganglia-thalamic loops, the model learns both to flexibly control working memory and to decide for appropriate responses based on working memory content and visual stimulation. The model's success in learning complex working memory tasks underlines the power and flexibility of the basic Hebbian and three-factor learning rules used

A model for learning color selective receptive cells from natural scenes

Martina Truschzinski

Wed, 20. 10. 2010, Room 1/336

Different from the standard view of color perception that proposes largely different pathways for color and shape perception, recently it has been discovered that in the primary visual cortex, color and shape are not processed apart from one another. Electrophysiological studies suggest that cells do not only respond to stimuli of a certain orientation or shape but at the same time they can be color selective. Different receptive field types have been reported: color-responsive single-opponent cells, color-responsive double-opponent cells (circular and orientated) and non-color-responsive cells. We here show that such receptive fields can emerge from Hebbian learning when presenting colored natural scenes to a model of V1 that has previously been proven to learn "edge-detecting" receptive fields (RFs) out of gray-scale images similar to those of primary visual cortex of macaque monkey.

Learning invariance in object recognition inspired by observations in the primary visual cortex of primates (Diplomverteidigung)

Michael Teichmann

Wed, 29. 9. 2010, Room 1/336

The human visual system has the remarkable ability to recognize objects invariant of their position, rotation and scale. A better interpretation of neurobiological findings involves a computational model that is capable of simulating signal processing in the visual cortex. Basically, to solve the task to create a computational model for invariant object recognition, an algorithm for learning invariance is required. There are only few studies at hand that cover the issue of learning such invariance.
In this thesis a set of Hebbian learning rules based on the calcium dynamics and homeostatic regulations of single neurons are proposed. These rules implement dynamic traces of activity that allow to learn spatially invariant representations from temporal correlations. They are applied within a simple model of the primary visual cortex of primates and simulate the unsupervised learning of receptive fields from natural scenes.
Furthermore, the discrimination capability of the model is demonstrated regarding simple artificial input and more difficult natural scenes. The properties of network neurons are also compared to properties of V1 complex cells. As a result of the thesis this approach shows that it is possible to create a limited network, which can learn an invariant representation of natural objects in a biologically comparable way.

Performance Gain for Clustering with Growing Neural Gas Using Parallelization Methods

Alexander Adam

Tue, 13. 7. 2010, Room 1/336

The amount of data in databases is increasing steadily. Clustering this data is one of the common tasks in Knowledge Discovery in Databases (KDD). For KDD purposes, this means that many algorithms need so much time, that they become practically unusable. To counteract this development, we try parallelization techniques on that clustering.
Recently, new parallel architectures have become affordable to the common user. We investigated especially the GPU (Graphics Processing Unit) and multi-core CPU architectures. These incorporate a huge amount of computing units paired with low latencies and huge bandwidths between them.
In this paper we present the results of different parallelization approaches to the GNG clustering algorithm. This algorithm is beneficial as it is an unsupervised learning method and chooses the number of neurons needed to represent the clusters on its own.

Methods of face detection and recognition

Arash Kermani

Tue, 6. 7. 2010, Room 1/336

First, we will present an overview of existing methods of face detection and recognition. Second, we will discuss the possibility of using biologically plausible vision models for face recognition.

Generierung von Merkmalen für die fehlerklassifizierende Prozesskontrolle von Laserschweißungen unter Anwendung von Sparse Coding- und ICA-Ansätzen(Diplomarbeit)

Thomas Wiener

Tue, 29. 6. 2010, Room 1/336

Die automatisierte Qualitätskontrolle von Laserschweißungen ist ein aktuelles Forschungsthema. Aufgrund der hohen Komplexität von Laserschweißprozessen existiert bisher kein vollständiges, quantitatives Modell, welches die Zusammenhänge zwischen Prozessparametern, Sensorausgaben und Schweißresultaten beschreibt. Eine vielversprechende Methode für die Qualitätsbewertung von Laserschweißungen ist die Verwendung überwachter Lernverfahren, welche mit Hilfe manuell selektierter Merkmale aus den Sensordaten angelernt werden. In Hinblick auf die Automatisierbarkeit der Qualitätskontrolle kann sich diese Vorgehensweise jedoch nachteilhaft auswirken, da die manuelle Merkmalsselektion Expertenwissen erfordert. In der vorliegenden Arbeit werden sowohl ICA- und Sparse Coding-Ansätze als auch das klassische Verfahren der Hauptkomponentenanalyse für die automatische Generierung von Merkmalen angewandt. Somit entsteht ein System, welches den Arbeitsschritt der manuellen Merkmalsselektion ersetzt. Anhand der im Rahmen dieser Arbeit zur Verfügung stehenden Sensordaten konnte gezeigt werden, dass die Ersetzung der manuellen Merkmalsselektion durch automatisch generierte Merkmale zu einer Verbesserung der Bewertungszuverlässigkeit beiträgt.

Retino-centric vs. ego-centric reference frame - the double-flash experiment

Arnold Ziesche

Tue, 8. 6. 2010, Room 1/336

The spatial localization of visual stimuli by the brain takes place in different reference frames, such as retinal or head-centered coordinate systems. This talk discusses how theoretical models which try to understand how these reference frames interact can benefit from the so-called double-flash experiment where two successive stimuli are shown around the time of eye movements.

The guidance of vision while learning categories - A computational model of the Basal Ganglia and Reinforcement Learning

Robert Blank

Tue, 25. 5. 2010, Room 1/336

Human beings are able to learn categories quite fast. But how does this happen and which role does guidance of visual perception play in this case? We will present a computational model of the Basal Ganglia which is based on a Reinforcement Learning algorithm. The further work will be an adaption of this proposed model to experimental data from humans to validate it

Synaptic Learning: Induction, maintenance and consolidation of synaptic connections.

Jan Wiltschut

Tue, 4. 5. 2010, Room 1/336

Changes in the connection strength between neurons in response to appropriate stimulation are thought to be the physiological basis for learning and memory formation. Long-Term-Potentiation (LTP) and Long-Term-Depression (LTD) of synapses in cortical areas have been the focus in the research of acquisition and storing new information. ... Read more

Perisaccadic shift in complete darkness. A computational model.

Arnold Ziesche

Tue, 27. 4. 2010, Room 1/336

In order to localize visually perceived objects in space the visual system has to take into account the gaze direction. Under normal circumstances this works well and the stimulus position information which in the beginning of the visual path is represented in a coordinate system which is centered on the retina and thus moves with the eyes is transformed into a space representation which is independent of the eye position. However, in complete darkness when there are no reference stimuli available, briefly flashed stimuli around the time of a saccade are systematically misperceived in space. Here I present backgrounds to this misperception and the approaches to explain it. The focus will be on our own computational model.

Learning disparity and feature selective cells in primary vision

Mark-Andre Voss

Tue, 20. 4. 2010, Room 1/336

Depth perception is an important cue used by human vision for many applications. Many details how the brain extracts depth information through binocular vision are still unknown. Most of the existing models use genericly constructed simple V1 cells to model disparity sensitivity. We have developed an unsupervised learning approach using Hebbian and anti-Hebbian learning principles and nonlinear dynamics to learn disparity- and feature-selective cells from a set of stereo images resulting in cells similar to cells found in V1. An introduction to binocular vision and an overview of our ongoing research are being presented.

Object recognition - VVCA (Vergence-Version Control with Attention Effects)

Frederik Beuth

Tue, 13. 4. 2010, Room 1/336

We will present a minimalistic, but complete robotic approach to detect objects in a scene. It uses stereoscopic input pictures of two cameras to detect stereoscopic edges. The system contains two control loops to archive vergence and version. The object recognition uses the edge detection information as inputs and is able to recognize and locate simple objects in the scene. The system is developed as part of the European project Eyeshots

Entwurf und Implementierung einer Schnittstelle für eine verteilte Simulationsumgebung für mobile autonome Systeme

Sebastian Drews

Thu, 28. 1. 2010, Room 2/209

Beim Entwurf und der Verifikation von Algorithmen für einzelne oder mehrere miteinander kooperierende autonome Systeme spielen Simulationsumgebungen eine immer wichtigere Rolle. Die Robotersimulation USARSim stellt einen viel versprechenden Ansatz für eine derartige Simulationsumgebung dar. In der vorliegenden Arbeit werden die Möglichkeiten des Einsatzes von USARSim für die Simulation von Quadrocoptern vorgestellt und bewertet. Es wird eine parallelisierte, verteilte Steuerung der Robotersimulation sowie eine darauf aufbauende Schnittstelle vorgestellt. Diese Schnittstelle zwischen der Simulation und den bereits vorhandenen Softwaremodulen ermöglicht das Ausführen von Anwendungen sowohl in der Simulation als auch auf der realen Hardware, ohne dass dazu Anpassungen am Programmcode erfolgen müssen. Weiterhin wird die Implementierung einer Server-Applikation zum Auslesen der Bilder der simulierten Kameras vorgestellt.

Eye Vergence Achieved without Explicitly Computed Disparity

Nikolay Chumerin (University K.U.Leuven)

Wed, 6. 1. 2010, Room 1/336

Vergence control is still very important topic in robotics and it uses intensively findings from neuroscience, psychophysics, control theory and computer vision. Most of the existing models try to minimize horizontal disparity in order to achieve proper vergence. We propose biologically-inspired approach which does not rely on explicitly computed disparity, but extract the desired vergence angle from the postprocessed response of a population of disparity tuned complex cells, the actual gaze direction and the actual vergence angle. The evaluation of two simple neural vergence angle control models are also discussed.

Combining biological models of attention and navigation with image processing tools for simultaneous localization and mapping (Diplomverteidigung)

Peer Neubert

Tue, 15. 12. 2009, Room 2/209 (Robotik-Lab)

Simultaneous localization and mapping (SLAM) is an essential capability for any autonomous arti?cial or biological system acting mobile in an unknown environment. This present work is about using image regions as landmarks for SLAM, that are likely to get attentional focus of an human observer. Input driven bottom-up processes play an considerable role for visual attention and well understood models for these processes exist. Content of this present work is analysis of these models, implementation and adaption to use for SLAM. Therefore, interesting images regions has to be extracted, tracked over an image sequence and matched when a prior observed scene becomes visible again. For low computational demands, the psychophysical models have to be implemented with efficient image processing tools.
Very recently M. Milford and G. Wyeth presented a biologically inspired approach to SLAM, called RatSLAM. They show promising results on mapping a complete suburb with a camera mounted on the roof of car. While their navigation system is biologically plausible, the used vision system lacks this character. In this present work, the origin visual system is replaced by the attention based visual features. Promising results of the combined system on real world outdoor data are presented.

Feature generation for the classification of laser welding data applying sparse coding and ICA approaches

Thomas Wiener

Tue, 8. 12. 2009, Room 1/336

Quality control of laser weldings is an important field of study, since the requirements on product quality increase continuously. Due to the complexity of laser welding processes, no complete quantitative models are existing to describe the causal relationships between process parameters and sensor outputs. A promising evaluation method for the quality of weld-seams is the use of supervised learning algorithms trained with manual selected features from pre-/ in-/ and postprocess sensor data.

The subject of this presentation will be a concept of replacing the manual feature selection by an automated feature generation using independent component analysis (ICA) and sparse coding approaches. Also a short introduction to the principles of laser welding, suitable sensors and classification through multiple linear

Applying a Calcium-Dependent Learning Rule for the Unsupervised Adaptation of Neuronal-Weights in the Primary Visual Cortex. (Diplomarbeit-Konzeptvortrag)

Michael Teichmann

Tue, 17. 11. 2009, Room 1/336

We will give a short introduction to the biological background of the visual stream, especially V1, followed by foundations of the learning methods and previous models. Finally, we will focus on the concept for the new model.

Press Articles