Optimal Region of Latching Activity in an Adaptive Potts Model for Networks of Neurons

TL;DR

This paper introduces an adaptive Potts model for neural networks that explores how noise and adaptation influence latching dynamics, revealing an optimal region for cognitive-like state transitions.

Contribution

It proposes a combined stochastic and adaptive Potts model demonstrating how noise and adaptation govern latching behavior in neural networks.

Findings

Latching occurs in a specific noise-adaptation parameter region.

Noise is fundamental for state transitions in the model.

An optimal region for realistic neural dynamics is identified.

Abstract

In statistical mechanics, the Potts model is a model for interacting spins with more than two discrete states. Neural networks which exhibit features of learning and associative memory can also be modeled by a system of Potts spins. A spontaneous behavior of hopping from one discrete attractor state to another (referred to as latching) has been proposed to be associated with higher cognitive functions. Here we propose a model in which both the stochastic dynamics of Potts models and an adaptive potential function are present. A latching dynamics is observed in a limited region of the noise(temperature)-adaptation parameter space. We hence suggest noise as a fundamental factor in such alternations alongside adaptation. From a dynamical systems point of view, the noise-adaptation alternations may be the underlying mechanism for multi-stability in attractor based models. An optimality criterion for realistic models is finally inferred.