Forschungsseminar

Das Forschungsseminar ist eine Veranstaltung, die sich an interessierte Studenten des Hauptstudiums richtet. Andere Interessenten sind jedoch jederzeit herzlich willkommen! Die vortragenden Studenten und Mitarbeiter der Professur KI stellen aktuelle forschungsorientierte Themen vor. Vorträge werden in der Regel in Englisch gehalten. Das Seminar findet unregelmäßig Donnerstag um 13:45 Uhr im Raum 336 statt. Den genauen Termin einzelner Veranstaltungen entnehmen Sie bitte den Ankündigungen auf dieser Seite.

Informationen für Diplom- und Masterstudenten

Die im Studium enthaltenen Seminarvorträge (das "Hauptseminar" im Studiengang Diplom-IF/AIF bzw. das "Forschungsseminar" im Master) können ebenso im Rahmen dieser Veranstaltung durchgeführt werden. Beide Lehrveranstaltungen (Diplom-Hauptseminar und Master-Forschungsseminar) haben das Ziel, dass die Teilnehmer selbststängig forschungsrelevantes Wissen erarbeiten und es anschließend im Rahmen eines Vortrages präsentieren. Thematisch behandeln die Seminare das Gebiet der Künstlichen Intelligenz, wobei der Schwerpunkt auf Objekterkennung, Neurocomputing auf Grafikkarten und Multi-Core Rechnern, Reinforcement Lernen, sowie intelligente Agenten in Virtueller Realität liegt. Andere Themenvorschläge sind aber ebenso herzlich willkommen!
Das Seminar wird nach individueller Absprache durchgeführt. Interessierte Studenten können unverbindlich Prof. Hamker kontaktieren, wenn sie wenn sie ein Interesse haben, bei uns eine der beiden Seminarveranstaltungen abzulegen.

Vergangene Veranstaltungen

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.

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