MSE-based analysis of optimal tuning functions predicts phenomena observed in sensory neurons

TL;DR

This paper develops an MSE-based theoretical framework predicting how neural tuning functions adapt to environmental uncertainty and noise, aligning with observed phenomena in sensory neurons and explaining sensory system reliability.

Contribution

It introduces a novel theory linking optimal Bayesian encoding to neural tuning adaptations, including dynamic tuning functions and their dependence on environmental factors.

Findings

Optimal tuning width increases with environmental noise and prior uncertainty.

Dynamic tuning functions improve sensory performance at short time scales.

Narrowing of tuning functions over time matches biological observations.

Abstract

Biological systems display impressive capabilities in effectively responding to environmental signals in real time. There is increasing evidence that organisms may indeed be employing near optimal Bayesian calculations in their decision-making. An intriguing question relates to the properties of optimal encoding methods, namely determining the properties of neural populations in sensory layers that optimize performance, subject to physiological constraints. Within an ecological theory of neural encoding/decoding, we show that optimal Bayesian performance requires neural adaptation which reflects environmental changes. Specifically, we predict that neuronal tuning functions possess an optimal width, which increases with prior uncertainty and environmental noise, and decreases with the decoding time window. Furthermore, even for static stimuli, we demonstrate that dynamic sensory tuning functions, acting at relatively short time scales, lead to improved performance. Interestingly, the narrowing of tuning functions as a function of time was recently observed in several biological systems. Such results set the stage for a functional theory which may explain the high reliability of sensory systems, and the utility of neuronal adaptation occurring at multiple time scales.