[Lisa_seminaires] [Lisa_labo] [Tea Talk] Guillaume Lajoie (UdeM) Fri April 13 10:30AM AA1360

xavier bouthillier xavier.bouthillier at gmail.com
Ven 13 Avr 12:09:39 EDT 2018


You can access the recording at this link: https://bluejeans.com/s/b7lnV/

On Tue, Apr 10, 2018 at 2:57 PM, Michael Noukhovitch <mnoukhov at gmail.com>
wrote:

> This week we have *Guillaume Lajoie* from *DMS *at *UdeM *giving a talk
> on *Friday April 13* at *10:30AM* in room *AA1360*.
>
> Make sure to come to this dynamic talk and your research will spike!
> Michael
>
> *TITLE *Dynamics of high-dimensional recurrent networks: how chaos shapes
> computation in biological and artificial neural networks
>
> *KEYWORDS *Dynamic Systems Analysis, Neuroscience, DL Theory
>
>
> *ABSTRACT*Networks of neurons —either biological or artificial— are
> called recurrent if their connections are distributed and contain feedback
> loops. Such networks can perform remarkably complex computations, as
> evidenced by their ubiquity throughout the brain and ever-increasing use in
> machine learning. They are, however, notoriously hard to control and their
> dynamics are generally poorly understood, especially in the presence of
> external forcing. This is because recurrent networks are typically chaotic
> systems, meaning they have rich and sensitive dynamics leading to variable
> responses to inputs. How does this chaos manifest in the neural code of the
> brain? How might we tame sensitivity to exploit complexity when training
> artificial recurrent networks for machine learning?
>
> Understanding how the dynamics of large driven networks shape their
> capacity to encode and process information presents a sizeable challenge.
> In this talk, I will discuss the use of Random Dynamical Systems Theory as
> a framework to study information processing in high-dimensional,
> signal-driven networks. I will present an overview of recent results
> linking chaotic attractors to entropy production, dimensionality and input
> discrimination of dynamical observables. I will outline insights this
> theory provides on how cortex performs complex computations using sparsely
> connected inhibitory and excitatory neurons, as well as implications for
> gradient-based optimization methods for artificial networks.
>
>
> *BIO*Since Jan 2018, Guillaume Lajoie is an Assistant Professor at the
> Math & Stats Dept. of UdeM. He obtained his PhD in applied mathematics from
> the University of Washington in 2013 and carried on research as an
> independent Bernstein Fellow at the Max Planck Institute for Dynamics and
> Self-Organization, as a visiting Scholar at the Courant Institute for
> Mathematical Sciences at NYU, and as a Washington Research Foundation
> Innovation Fellow at the University of Washington's Institute for
> Neuroengineering. His research interests lie at the intersection of applied
> mathematics, neuroscience and AI, where he works at leveraging tools from
> Dynamical Systems, Stochastic Processes, Information Theory and Machine
> Learning to better understand how recurrent network dynamics --either
> biological or artificial-- support computations. Applications of his
> research range from contributions to a theory of computation in recurrent
> networks, to the development of brain-computer interfaces.
>
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>
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