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Noise in large-scale neuronal networks, brain rhythms and neural avalanches

Banff International Research Station for Mathematical Innovation and Discovery

It is now folklore that intracellular membrane potential recordings of neurons are highly noisy, owing to a variety of random microscopic processes contributing to maintenance. At macroscopic scales, reliable, fast and accurate responses to stimuli emerge, and are experimentally described through various quantities. I will focus in particular on mathematical models of large neuronal networks can account for the type of experimental data observed in synchronized oscillations ( sources of brain rhythms) or neural avalanches. These two quantities are relevant in that rhythms reportedly support important functions such as memory and attention, while distributions of avalanches were reported to reveal that the brain operates at criticality where it maximizes its information processing capacities. I will show that a simple theory of large-scale dynamics allows understanding under a common framework both phenomena. In particular, I will show the relatively paradoxical phenomenon that noise can contribute to synchronization of large neural assemblies, and that the experimentally reported heavy-tailed distributions of avalanche durations and sizes may be in fact related to Boltzmannâ s molecular chaos that naturally emerges in limits of large interacting networks with noise. These works were developed with Alain Destexhe and Bard Ermentrout.

Mathematics; Statistics; Probability theory and stochastic processes; Applied statistics

video/mp4

Author affiliation: Collège de France & Inria

2019-03-09

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/68548

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/