Energy consumption and cooperation for optimal sensing

TL;DR

This paper models two interacting sensors to explore how energy use, noise, and signal statistics influence optimal environmental sensing, revealing that energy consumption is sometimes unnecessary but often beneficial for maximizing information.

Contribution

It introduces a model analyzing the role of energy consumption and cooperation in sensor networks, highlighting conditions for optimal sensing strategies.

Findings

Energy-consuming asymmetric couplings maximize information in low-noise, high-correlation scenarios.

Optimal sensing sometimes occurs without energy expenditure.

Sensor interaction and energy use remain crucial when incorporating time integration of signals.

Abstract

The reliable detection of environmental molecules in the presence of noise is an important cellular function, yet the underlying computational mechanisms are not well understood. We introduce a model of two interacting sensors which allows for the principled exploration of signal statistics, cooperation strategies and the role of energy consumption in optimal sensing, quantified through the mutual information between the signal and the sensors. Here we report that in general the optimal sensing strategy depends both on the noise level and the statistics of the signals. For joint, correlated signals, energy consuming (nonequilibrium), asymmetric couplings result in maximum information gain in the low-noise, high-signal-correlation limit. Surprisingly we also find that energy consumption is not always required for optimal sensing. We generalise our model to incorporate time integration of the sensor state by a population of readout molecules, and demonstrate that sensor interaction and energy consumption remain important for optimal sensing.