Decision-making and interacting neuron populations

TL;DR

This paper models the decision-making process in interacting neuron populations using stochastic differential equations, simplifying complex dynamics to analyze firing rates, reaction times, and performance.

Contribution

It introduces a reduced one-dimensional stochastic model for bi-stability in neuron populations, facilitating analysis of decision-making dynamics.

Findings

Reduced model effectively captures key decision-making variables

Provides insights into reaction times and performance metrics

Simplifies complex neuron interaction dynamics

Abstract

In this article we present the modeling of bi-stability view problems described by the activity or firing rates of two interacting population of neurons. Starting from the study of a complex system, the sys-tem of stochastic differential equations describing the time evolution of the activity of the two populations of neurons, we point out the strength and weakness of this model and consider its associated par-tial differential equation, which resolution gives statistical information on the firing rates distributions. The slow-fast characterization of the solutions finally leads us to a complexity reduction of the model by the definition of a one-dimensional stochastic differential equation and its associated one-dimensional partial differential equation. This last model turns out to be well adapted to the resolution of the prob-lem giving access, in particular, to reaction times and performance, two macroscopic variables describing the decision-making in the view problem.