Point singularities and suprathreshold stochastic resonance in optimal coding

TL;DR

This paper investigates how noise influences optimal sensory neuron coding, revealing that under certain conditions, a phenomenon called suprathreshold stochastic resonance optimizes information transmission, with threshold distributions exhibiting singularities and bifurcations.

Contribution

It demonstrates that optimal threshold distributions in noisy populations can contain singularities and undergo bifurcations, elucidating the role of noise in neural coding optimization.

Findings

Optimal threshold distribution contains singularities for random inputs.

High noise levels lead to a single-point threshold distribution.

Bifurcational pattern emerges as noise intensity increases.

Abstract

Motivated by recent studies of population coding in theoretical neuroscience, we examine the optimality of a recently described form of stochastic resonance known as suprathreshold stochastic resonance, which occurs in populations of noisy threshold devices such as models of sensory neurons. Using the mutual information measure, it is shown numerically that for a random input signal, the optimal threshold distribution contains singularities. For large enough noise, this distribution consists of a single point and hence the optimal encoding is realized by the suprathreshold stochastic resonance effect. Furthermore, it is shown that a bifurcational pattern appears in the optimal threshold settings as the noise intensity increases. Fisher information is used to examine the behavior of the optimal threshold distribution as the population size approaches infinity.