Formation of Artificial Neural Assemblies by Biologically Plausible Inhibition Mechanisms

TL;DR

This paper introduces a biologically plausible inhibition mechanism for neural assembly formation, improving the dynamics and recovery rate of assemblies in a model inspired by cortical networks.

Contribution

It proposes a new gamma oscillation-inspired selection process, enhancing the Assembly Calculus model to better reflect biological neural dynamics.

Findings

The new E%-WTA model aligns assembly size with network dynamics.

Assembly recovery rate is improved with the new inhibition mechanism.

Model dynamics are more consistent with cortical neural activity patterns.

Abstract

As proposed by Hebb's theory, neural assemblies are groups of excitatory neurons that fire synchronously and exhibit high synaptic density, representing external stimuli and supporting cognitive functions such as language and decision-making. Recently, a model called Assembly Calculus (AC) was proposed, enabling the formation of artificial neural assemblies through the $k$-winners-take-all selection process and Hebbian learning. Although the model is capable of forming assemblies according to Hebb's theory, the adopted selection process does not incorporate essential aspects of biological neural computation, as neural activity, which is often governed by statistical distributions consistent with power-law scaling. Given this limitation, the present work aimed to bring the model's dynamics closer to that observed in real cortical networks. To achieve this, a new selection mechanism inspired by the dynamics of gamma oscillation cycles, called E%-winners-take-all, was implemented, combined with an inhibition process based on the ratio between excitatory and inhibitory neurons observed in various regions of the cerebral cortex. The results obtained from our model (called E%-WTA model) were compared with those of the original model, and the analyses demonstrated that the introduced modifications allowed the network's own dynamics to determine the size of the formed assemblies. Furthermore, the recovery rate of these groups, through the evocation of the stimuli that generated them, became superior to that obtained in the original model.