Information-theoretic vs. thermodynamic entropy production in autonomous sensory networks

TL;DR

This paper compares information acquisition and thermodynamic costs in sensory networks, revealing no universal limit on information rate relative to entropy production, supported by analytical bounds and a cellular sensing model.

Contribution

It introduces a general bipartite model to analyze the relationship between information rate and thermodynamic entropy production in sensory networks, challenging previous assumptions.

Findings

No universal bound exists between information rate and thermodynamic entropy production.

An analytical upper bound on mutual information rate is derived.

Numerical methods estimate the entropy of time-series in the model.

Abstract

For sensory networks, we determine the rate with which they acquire information about the changing external conditions. Comparing this rate with the thermodynamic entropy production that quantifies the cost of maintaining the network, we find that there is no universal bound restricting the rate of obtaining information to be less than this thermodynamic cost. These results are obtained within a general bipartite model consisting of a stochastically changing environment that affects the instantaneous transition rates within the system. Moreover, they are illustrated with a simple four-states model motivated by cellular sensing. On the technical level, we obtain an upper bound on the rate of mutual information analytically and calculate this rate with a numerical method that estimates the entropy of a time-series generated with a simulation.