Applied Math Seminar | Marius Yamakou, A simple parameter can switch between different weak-noise–induced phenomena in neurons

MC 6460

Speaker

Marius Yamakou, Friedrich-Alexander-Universität Erlangen-Nürnberg

Title

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

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, particularly reservoir computing based on liquid-state machines.