Functional diversity among sensory neurons from efficient coding principles

Abstract

In many sensory systems the neural signal is coded by the coordinated response of heterogeneous populations of neurons. What computational benefit does this diversity confer on information processing? We derive an efficient coding framework assuming that neurons have evolved to communicate signals optimally given natural stimulus statistics and metabolic constraints. Incorporating nonlinearities and realistic noise, we study optimal population coding of the same sensory variable using two measures: maximizing the mutual information between stimuli and responses, and minimizing the error incurred by the optimal linear decoder of responses. Our theory is applied to a commonly observed splitting of sensory neurons into ON and OFF that signal stimulus increases or decreases, and to populations of monotonically increasing responses of the same type, ON. Depending on the optimality measure, we make different predictions about how to optimally split a population into ON and OFF, and how to allocate the firing thresholds of individual neurons given realistic stimulus distributions and noise, which accord with certain biases observed experimentally. The brain processes external stimuli through special receptor cells and associated sensory circuits. In many sensory systems the population of neurons splits into ON and OFF cells, namely cells that signal an increase vs. a decrease of the sensory variable. This happens in brains from worm to man, and in the sensing of temperature, odor, light, and sound. Here we analyze the possible benefits of “pathway splitting” using information theory. We derive the most efficient split of a pathway into ON and OFF neurons and predict the response range of each neuron type as a function of noise and stimulus statistics. Our theory offers insight into this ubiquitous phenomenon of neural organization and suggests new experiments in diverse sensory systems.