Emergent organization of receptive fields in networks of excitatory and inhibitory neurons

TL;DR

This paper introduces a computational model of neural networks with excitatory and inhibitory neurons that develop topographic receptive fields, demonstrating emergent patterns for visual and linguistic data, and exploring plasticity mechanisms.

Contribution

It presents a novel sparse coding algorithm inspired by neural wave dynamics that produces topographic maps for both images and text, bridging biological plausibility and artificial neural network design.

Findings

Emergence of pinwheel patterns in visual receptive fields

Topographic maps for word semantics in text processing

Insights into neuronal map plasticity from synthetic somatosensory experiments

Abstract

Local patterns of excitation and inhibition that can generate neural waves are studied as a computational mechanism underlying the organization of neuronal tunings. Sparse coding algorithms based on networks of excitatory and inhibitory neurons are proposed that exhibit topographic maps as the receptive fields are adapted to input stimuli. Motivated by a leaky integrate-and-fire model of neural waves, we propose an activation model that is more typical of artificial neural networks. Computational experiments with the activation model using both natural images and natural language text are presented. In the case of images, familiar "pinwheel" patterns of oriented edge detectors emerge; in the case of text, the resulting topographic maps exhibit a 2-dimensional representation of granular word semantics. Experiments with a synthetic model of somatosensory input are used to investigate how the network dynamics may affect plasticity of neuronal maps under changes to the inputs.