Computer simulation of inhibition-dependent binding in a neural network

TL;DR

This paper models neural network dynamics to demonstrate how inhibition can control the binding of neural patterns, using a simulation with adjustable inhibition levels to switch between connected and disconnected activity states.

Contribution

It introduces a neural network model that uses inhibition as a control mechanism for pattern binding, incorporating learning via synaptic strength and delay adjustments.

Findings

High inhibition leads to disconnected patterns.

Low inhibition results in bound patterns.

Inhibition acts as a switch for pattern binding.

Abstract

Reverberating dynamics of neural network is modelled on PC in order to illustrate possible role of inhibition as binding controller in the network. The network is composed of binding neurons. In the binding neuron model the degree of temporal coherence between synaptic inputs is decisive for triggering, and slow inhibition is expressed in terms of the degree, which is necessary for triggering. Two learning mechanisms are implemented in the network, namely, adjusting synaptic strength and/or propagation delays. By means of forced playing of external pattern the network is taught to support dynamics with disconnected and bound patterns of activity. By choosing either high, or low inhibition one can switch between the disconnected and bound patterns, respectively. This is interpreted as inhibition-controlled binding in the network.