A Biologically Plausible Learning Rule for Perceptual Systems of organisms that Maximize Mutual Information

TL;DR

This paper introduces a biologically plausible, local, spike-based learning rule that implements the Infomax principle to optimize perceptual systems by maximizing mutual information with sensory inputs.

Contribution

It presents a novel continuous-time, spike-based learning rule that accurately realizes the Infomax principle in biological neural systems.

Findings

The learning rule effectively maximizes mutual information.

It operates locally and in continuous time, aligning with biological plausibility.

The method offers a new approach for understanding neural coding optimization.

Abstract

It is widely believed that the perceptual system of an organism is optimized for the properties of the environment to which it is exposed. A specific instance of this principle known as the Infomax principle holds that the purpose of early perceptual processing is to maximize the mutual information between the neural coding and the incoming sensory signal. In this article, we present a method to implement this principle accurately with a local, spike-based, and continuous-time learning rule.