Neurons as an Information-theoretic Engine

TL;DR

This paper models neuronal gain modulation as an information-theoretic cycle that enhances stimulus information processing, quantifies its efficiency, and draws analogies to heat engines in thermodynamics.

Contribution

It introduces a novel theoretical framework describing neural gain modulation as an entropy cycle, linking information processing to thermodynamic principles.

Findings

Neural gain modulation can be modeled as an entropy cycle.

The cycle enhances stimulus information dynamically.

Theoretical limits of internal computation efficiency are derived.

Abstract

We show that dynamical gain modulation of neurons' stimulus response is described as an information-theoretic cycle that generates entropy associated with the stimulus-related activity from entropy produced by the modulation. To articulate this theory, we describe stimulus-evoked activity of a neural population based on the maximum entropy principle with constraints on two types of overlapping activities, one that is controlled by stimulus conditions and the other, termed internal activity, that is regulated internally in an organism. We demonstrate that modulation of the internal activity realises gain control of stimulus response, and controls stimulus information. A cycle of neural dynamics is then introduced to model information processing by the neurons during which the stimulus information is dynamically enhanced by the internal gain-modulation mechanism. Based on the conservation law for entropy production, we demonstrate that the cycle generates entropy ascribed to the stimulus-related activity using entropy supplied by the internal mechanism, analogously to a heat engine that produces work from heat. We provide an efficient cycle that achieves the highest entropic efficiency to retain the stimulus information. The theory allows us to quantify efficiency of the internal computation and its theoretical limit.