Wulfram Gerstner is Director of the Laboratory of Computational Neuroscience LCN at the EPFL. His research in computational neuroscience concentrates on models of spiking neurons and spike-timing dependent plasticity, on the problem of neuronal coding and neural dynamics in single neurons and populations, as well as on the link between biologically plausible learning rules and behavioral manifestations of learning. He teaches courses for Physicists, Computer Scientists, Mathematicians, and Life Scientists at the EPFL.

 After studies of Physics in Tübingen and at the Ludwig-Maximilians-University Munich (Master 1989), Wulfram Gerstner spent a year as a visiting researcher in Berkeley. He received his PhD in theoretical physics from the Technical University Munich in 1993 with a thesis on associative memory and dynamics in networks of spiking neurons. After short postdoctoral stays at Brandeis University and the Technical University of Munich, he joined the EPFL in 1996 as assistant professor. Promoted to Associate Professor with tenure in February 2001, he is since August 2006 a full professor with double appointment in the School of Computer and Communication Sciences and the School of Life Sciences. Wulfram Gerstner has been invited speaker at numerous international conferences and workshops. He has served on the editorial board of the Journal of Neuroscience, `Network: Computation in Neural Systems', `Journal of Computational Neuroscience', and `Science'.  

Spiking Neurons
Biologically Plausaible Learning Rules
Loss Landsacpe of Neural Networks

For Master students in NeuroX, Physics, Life Science Engineering, Computer Science, Mathematics/Computational Science.

This course covers mathematical models of neurons and neuronal networks in the context of biology and establish links to models of cognition. The focus is on brain dynamics approximated by deterministic or stochastic differential equations. 
Hodgkin-Huxeley model, Hopfield model, Decision model,  stochastic leaky integrate-and-fire model, spike response model, phase plane analysis, Poisson process, Renewal process, mean-field methods for dynamics.

Master students in Computer Science and NeuroX.

 Biological brains show powerful learning without BackProp, how? By a smart combination of Reinforcement Learning and Self-supervised learning with local learning rules at the connections (synapses). 

 - Why BackProp is biologically not plausible. 
- Two-factor and three-factor rules  in biology (synaptic plasticity) and neuromorphic hardware (
- Three-factor rules for reward-based learning 
- Reinforcement Learning in the brain
- Learning of efficient representations with biologically plausible learning rules