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Professorship Artificial Intelligence

Research Seminar

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. The seminar will be hold irregularly in the room 1/336 whereby the precise schedule is presented in the following table.

Information regarding diploma and master students

Seminar presentations of each major could also be hold in the frame of this event. This includes mainly the seminar “Hauptseminar" in the major Diplom-IF/AIF or the "Forschungsseminar" in all master majors of Informatik. Both courses have the goal that the attendees acquire research-oriented knowledge by themself and present it during talks. The research topics will be typically from the field of Artificial Intelligence with emphasis on object recognition, neuro-computing at graphic cards or multi-core-CPUs, reinforcement learning and intelligent agents in virtual reality. However, other topics could also be chosen. Interested students should write an Email to Prof. Hamker, the talk itself will be scheduled after an individual consultation.

Next Events

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, 10:30, 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.

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

Oliver Maith

Mon, 26. 8. 2019, 10:00, 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.

Object classification based on high resolution LiDAR

Megha Rana

Thu, 26. 9. 2019, 14:00, Room TBA

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.

Recent Events

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 https://julien-vitay.net/2019/05/successor-representations

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.

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