Short term synaptic depression improves information transfer in perceptual multistability

TL;DR

This paper investigates how short-term synaptic depression combined with noise influences perceptual switching in neural networks, revealing that depression enhances information transfer and creates perceptual memory in bistability and tristability models.

Contribution

It introduces a detailed analysis of synaptic depression effects on perceptual switching dynamics, demonstrating improved information transfer and perceptual memory in competitive neural networks.

Findings

Depression-driven switching times can be approximated using timescale separation.

Combination of depression and noise produces realistic dominance time distributions.

Synaptic depression enhances information transfer about stimuli.

Abstract

Competitive neural networks are often used to model the dynamics of perceptual bistability. Switching between percepts can occur through fluctuations and/or a slow adaptive process. Here, we analyze switching statistics in competitive networks with short term synaptic depression and noise. We start by analyzing a ring model that yields spatially structured solutions and complement this with a study of a space-free network whose populations are coupled with mutual inhibition. Dominance times arising from depression driven switching can be approximated using a separation of timescales in the ring and space-free model. For purely noise-driven switching, we use energy arguments to justify how dominance times are exponentially related to input strength. We also show that a combination of depression and noise generates realistic distributions of dominance times. Unimodal functions of dominance times are more easily differentiated from one another using Bayesian sampling, suggesting synaptic depression induced switching transfers more information about stimuli than noise-driven switching. Finally, we analyze a competitive network model of perceptual tristability, showing depression generates a memory of previous percepts based on the ordering of percepts.