Wulfram Gerstner

As director of the Laboratory of Computational Neuroscience LCN at the EPFL, Wulfram Gerstner conducts research in computational neuroscience with special emphasis on models of spiking neurons, spike-timing dependent plasticity, and reward-based learning in spiking neurons. The questions on learning in spiking neurons are linked to the problem of neuronal coding in single neurons and populations. His teaching concentrates on learning in formal models and biological systems.

Biography

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 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'.

Publications

Selected publications

F. Zenke, and E.J. Agnes, and W. Gerstner Nature Communications, 6:6922, 2015.	Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks
G. Hennequin, T. Vogels and W. Gerstner Neuron,82:1394�1406 (2014)	Optimal Control of Transient Dynamics in Balanced Networks Supports Generation of Complex Movements
D.J. Rezende and W. Gerstner, Frontiers In Computational Neuroscience, 8:38, 2014.	Stochastic variational learning in recurrent spiking networks,
T. Vogels, H. Sprekeler, F. Zenke, C. Clopath and W. Gerstner Science 334: 1569-1573, 2011	Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks
C. Clopath, L. Busing, E. Vasilaki and W. Gerstner Nature Neuroscience, 13: 344-352, 2010	Connectivity reflects coding: a model of voltage-based STDP with homeostasis
Wulfram Gerstner list (1992 - )	Full list of publications

Teaching & PhD

Teaching

Computer Science

Life Sciences Engineering
Communication Systems

PhD Students

Becker Sophia, Delrocq Ariane Andrée Marie, Gross Alisa, Gruaz Lucas Louis, Martinelli Flavio, Nguyen Tâm Johan, Pezon Louis Henry, Smeets Kasper, Wang Shuqi, Wu Zihan,

Past EPFL PhD Students

Badel Laurent , Barry Martin Louis Lucien Rémy , Bathellier Brice , Clopath Claudia , Colombo Florian François , Corneil Dane Sterling , Faraji Mohammadjavad , Frémaux Nicolas , Gastaldi Chiara , Gerhard Joao Emanuel Felipe , Gozel Olivia , Hennequin Guillaume , Iatropoulos Georgios , Illing Bernd Albert , Jimenez Rezende Danilo , Lehmann Marco Philipp , Liakoni Vasiliki , Luksys Gediminas , Marcille Nicolas , Mensi Skander , Modirshanechi Alireza , Muscinelli Samuel Pavio , Naud Richard , Pfister Jean-Pascal Théodor , Pozzorini Christian Antonio , Schmutz Valentin Marc , Seeholzer Alexander Kevin , Setareh Hesam , Simsek Berfin , Sourmpis Christos , Stanojevic Ana , Stein Naves de Brito Carlos , Tomm Christian , Zenke Friedemann , Ziegler Lorric ,

Courses

Learning in neural networks

Artificial Neural Networks are inspired by Biological Neural Networks. One big difference is that optimization in Deep Learning is done with the BackProp Algorithm, whereas in biological neural networks it is not. We show what biologically plausible learning algorithms can do (and what not).

Computational neurosciences: neuronal dynamics

In this course we study 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.

Wulfram Gerstner

Full Professor

Computational Neuroscience Laboratory

Fields of expertise

Mission