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.