Instabilities in Attractor Networks with Fast Synaptic Fluctuations and Partial Updating of the Neurons Activity

TL;DR

This paper investigates how partial and fast synaptic fluctuations in attractor neural networks lead to different dynamic regimes, including attractor relaxation, itinerancy, and chaos, with implications for neurobiological processes.

Contribution

The study introduces a probabilistic neural automaton with a tunable parameter for partial neuron updating, revealing new dynamic behaviors and potential biological relevance.

Findings

For small rho, the network relaxes to attractors with high sensitivity to stimuli.

At rho above a critical value, the network exhibits itinerant behavior among attractors.

Tuning rho induces transitions between regular and chaotic oscillations, affecting search efficiency.

Abstract

We present and study a probabilistic neural automaton in which the fraction of simultaneously-updated neurons is a parameter, rho (0, 1) . For small rho, there is relaxation towards one of the attractors and a great sensibility to external stimuli and, for rho >= rho_c, itinerancy among attractors. Tuning rho in this regime, oscillations may abruptly change from regular to chaotic and vice versa, which allows one to control the efficiency of the searching process. We argue on the similarity of the model behavior with recent observations and on the possible role of chaos in neurobiology.