Neural and perceptual signatures of efficient sensory coding

TL;DR

This paper develops a formal framework for efficient sensory coding in the brain, linking environmental statistics, neural resource allocation, and perceptual discrimination, and validates predictions with empirical data.

Contribution

It derives a closed-form optimal encoding solution for heterogeneous noisy neurons based on environmental stimulus probabilities.

Findings

Optimal neural population parameters depend on environmental stimulus distribution.

Predicted relationships between neural coding and perceptual discriminability match empirical data.

The framework applies to visual and auditory sensory attributes.

Abstract

The mammalian brain is a metabolically expensive device, and evolutionary pressures have presumably driven it to make productive use of its resources. For sensory areas, this concept has been expressed more formally as an optimality principle: the brain maximizes the information that is encoded about relevant sensory variables, given available resources. Here, we develop this efficiency principle for encoding a sensory variable with a heterogeneous population of noisy neurons, each responding to a particular range of values. The accuracy with which the population represents any particular value depends on the number of cells that respond to that value, their selectivity, and their response levels. We derive the optimal solution for these parameters in closed form, as a function of the probability of stimulus values encountered in the environment. This optimal neural population also imposes limitations on the ability of the organism to discriminate different values of the encoded variable. As a result, we predict an explicit relationship between the statistical properties of the environment, the allocation and selectivity of neurons within populations, and perceptual discriminability. We test this relationship for three visual and two auditory attributes, and find that it is remarkably consistent with existing data.