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

Michael Noukhovitch mnoukhov at gmail.com
Mar 10 Avr 14:57:24 EDT 2018


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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