Surround Inhibition Mechanism by Deep Learning

TL;DR

This paper models the surround inhibition mechanism, vital in sensory perception, using a deep neural network to replicate physiological neural processes and compare them with biological understanding.

Contribution

It introduces a neural network model that simulates the surround inhibition mechanism, bridging deep learning with physiological sensory processing.

Findings

Neural network successfully mimics surround inhibition behavior.

Model aligns with physiological understanding of sensory signal enhancement.

Provides a computational framework for studying sensory inhibition mechanisms.

Abstract

In the sensation of tones, visions and other stimuli, the "surround inhibition mechanism" (or "lateral inhibition mechanism") is crucial. The mechanism enhances the signals of the strongest tone, color and other stimuli, by reducing and inhibiting the surrounding signals, since the latter signals are less important. This surround inhibition mechanism is well studied in the physiology of sensor systems. The neural network with two hidden layers in addition to input and output layers is constructed; having 60 neurons (units) in each of the four layers. The label (correct answer) is prepared from an input signal by applying seven times operations of the "Hartline mechanism", that is, by sending inhibitory signals from the neighboring neurons and amplifying all the signals afterwards. The implication obtained by the deep learning of this neural network is compared with the standard physiological understanding of the surround inhibition mechanism.