Rhythmic inhibition allows neural networks to search for maximally consistent states

TL;DR

This paper demonstrates that gamma-band rhythmic inhibition in neural networks enables efficient search for consistent states, learning internal models through Hebbian plasticity, and reproduces perceptual multi-stability phenomena matching experimental data.

Contribution

It introduces a neural network model utilizing rhythmic inhibition for constraint satisfaction and perceptual inference, with learning via Hebbian plasticity, advancing understanding of neural computation.

Findings

Networks solve constraint satisfaction problems without stochastic assumptions.

Rhythmic inhibition dynamics approximate stochastic sampling processes.

Model reproduces perceptual multi-stability with realistic switching times.

Abstract

Gamma-band rhythmic inhibition is a ubiquitous phenomenon in neural circuits yet its computational role still remains elusive. We show that a model of Gamma-band rhythmic inhibition allows networks of coupled cortical circuit motifs to search for network configurations that best reconcile external inputs with an internal consistency model encoded in the network connectivity. We show that Hebbian plasticity allows the networks to learn the consistency model by example. The search dynamics driven by rhythmic inhibition enable the described networks to solve difficult constraint satisfaction problems without making assumptions about the form of stochastic fluctuations in the network. We show that the search dynamics are well approximated by a stochastic sampling process. We use the described networks to reproduce perceptual multi-stability phenomena with switching times that are a good match to experimental data and show that they provide a general neural framework which can be used to model other 'perceptual inference' phenomena.