Hi everyone,

tomorrow we will have a guest tutorial on neuroscience by Prof. Wulfram Gerstner. Here is the detail!

Who:    Wulfram Gerstner

When:  Nov. 22 (Tuesday) 3PM-5PM

Where: AA6214

Title: Synaptic Plasticity to Learning Rules: an approach of theoretical neuroscience

Abstract: In this tutorial I will show what type of constraints experimental data set for biologically plausible learning rules and why inside these constraints we (as theoretical neuroscientists) have a lot of freedom to think about learning rules that are functionally relevant in  learning paradigms. Taking examples from unsupervised learning, reinforcement learning, and surprise modulated learning I will point out potential links (and differences) to machine learning approaches.

Bio: Wulfram Gerstner is Director of the Laboratory of Computational Neuroscience LCN at the EPFL. He studied physics at the universities of Tubingen and Munich and received a PhD from the Technical University of Munich. 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 role of spatial representation for navigation of rat-like autonomous agents. He currently has a joint appointment at the School of Life Sciences and the School of Computer and Communications Sciences at the EPFL. He teaches courses for Physicists, Computer Scientists, Mathematicians, and Life Scientists.

Best Regards,

--Junyoung

On Mon, Nov 14, 2016 at 12:52 PM, Junyoung Chung <elecegg@gmail.com> wrote:

Hi everyone,

on next Tuesday, we will have a guest tutorial on neuroscience by professor Wulfram Gerstner who is visiting our lab for two weeks. Looking forward to see many of you there!

Who:    Wulfram Gerstner

When:  Nov. 22 (Tuesday) 3PM-5PM

Where: AA6214

Title: Synaptic Plasticity to Learning Rules: an approach of theoretical neuroscience

Abstract: In this tutorial I will show what type of constraints experimental data set for biologically plausible learning rules and why inside these constraints we (as theoretical neuroscientists) have a lot of freedom to think about learning rules that are functionally relevant in  learning paradigms. Taking examples from unsupervised learning, reinforcement learning, and surprise modulated learning I will point out potential links (and differences) to machine learning approaches.

Bio: Wulfram Gerstner is Director of the Laboratory of Computational Neuroscience LCN at the EPFL. He studied physics at the universities of Tubingen and Munich and received a PhD from the Technical University of Munich. 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 role of spatial representation for navigation of rat-like autonomous agents. He currently has a joint appointment at the School of Life Sciences and the School of Computer and Communications Sciences at the EPFL. He teaches courses for Physicists, Computer Scientists, Mathematicians, and Life Scientists.

Best,

--Junyoung

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