- Goal-directed learning
- Emotions and Motivation
- Working memory
- Reinforcement learning
- Interval timing
- Autonomous robotics
Previously postdoc in the Psychology Department of the University of Münster (Germany), under the supervision of Prof. Dr. Fred Hamker and PhD student at the Inria Nancy (Lorraine, France), in the CORTEX lab headed by Dr. Frédéric Alexandre.
Born on December 11th, 1979 in Saint-Nazaire (Loire-Atlantique, France).
ANNarchy (Artificial Neural Networks architect) is a parallel and hybrid simulator for distributed rate-coded or spiking neural networks. The core of the library is written in C++ and distributed using openMP or CUDA. It provides an interface in Python for the definition of the networks. It is released under the GNU GPL v2 or later.
Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We developed the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++ code.
Content: The course covers various fields of machine learning, including supervised learning, unsupervised learning and reinforcement learning. See the course's page : http://www.tu-chemnitz.de/informatik/KI/edu/ml