Non-equilibrium dynamics of adaptation in sensory systems

TL;DR

This paper develops a Bayesian framework for optimal sensory adaptation, analyzing its speed, accuracy, and thermodynamic irreversibility, providing insights into how biological systems efficiently respond to environmental changes.

Contribution

It introduces a Bayesian filter-based model of sensory adaptation, linking adaptation dynamics to stochastic thermodynamics and entropy production.

Findings

Adaptation speed scales sublinearly with environmental change rate.

Mathematical equivalence established between adaptation and stochastic thermodynamics.

Provides a model-free method to quantify adaptation empirically.

Abstract

Adaptation is used by biological sensory systems to respond to a wide range of environmental signals, by adapting their response properties to the statistics of the stimulus in order to maximize information transmission. We derive rules of optimal adaptation to changes in the mean and variance of a continuous stimulus in terms of Bayesian filters, and map them onto stochastic equations that couple the state of the environment to an internal variable controling the response function. We calculate numerical and exact results for the speed and accuracy of adaptation, and its impact on information transmission. We find that, in the regime of efficient adaptation, the speed of adaptation scales sublinearly with the rate of change of the environment. Finally, we exploit the mathematical equivalence between adaptation and stochastic thermodynamics to quantitatively relate adaptation to the irreversibility of the adaptation time course, defined by the rate of entropy production. Our results suggest a means to empirically quantify adaptation in a model-free and non-parametric way.