PIRSA:23020062

Video URL

https://pirsa.org/23020062

A simple parameter can switch between different weak-noise–induced phenomena in neurons

Yamakou, M. (2023). A simple parameter can switch between different weak-noise–induced phenomena in neurons. Perimeter Institute for Theoretical Physics. https://pirsa.org/23020062

Yamakou, Marius. A simple parameter can switch between different weak-noise–induced phenomena in neurons. Perimeter Institute for Theoretical Physics, Feb. 21, 2023, https://pirsa.org/23020062 

          @misc{ scivideos_PIRSA:23020062,
            doi = {10.48660/23020062},
            url = {https://pirsa.org/23020062},
            author = {Yamakou, Marius},
            keywords = {Other Physics},
            language = {en},
            title = {A simple parameter can switch between different weak-noise{\textendash}induced phenomena in neurons},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2023},
            month = {feb},
            note = {PIRSA:23020062 see, \url{https://scivideos.org/index.php/pirsa/23020062}}
          }

Marius Yamakou University of Erlangen-Nuremberg

Talk numberPIRSA:23020062
DOI10.48660/23020062
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

This talk will consider a stochastic multiple-timescale dynamical system modeling a biological neuron. With this model, we will separately uncover the mechanisms underlying two different ways biological neurons encode information with stochastic perturbations: self-induced stochastic resonance (SISR) and inverse stochastic resonance (ISR). We will then show that in the same weak noise limit, SISR and ISR are related through the relative geometric positioning (and stability) of the fixed point and the generic folded singularity of the model’s critical manifold.  This mathematical result could explain the experimental observation where neurons with identical morphological features sometimes encode different information with identical synaptic input. Finally, if time permits, we shall discuss the plausible applications of this result in neuro-biologically inspired machine learning algorithms, particularly reservoir computing based on liquid-state machines.

Zoom link:  https://pitp.zoom.us/j/94345141890?pwd=aTRFM3M0a0xCOEM3aXZjY2hFYzVrQT09