Neural Population Coding is Optimized by Discrete Tuning Curves

Abstract

The sigmoidal tuning curve that maximizes the mutual information for a Poisson neuron, or population of Poisson neurons, is obtained. The optimal tuning curve is found to have a discrete structure that results in a quantization of the input signal. The number of quantization levels undergoes a hierarchy of phase transitions as the length of the coding window is varied. We postulate, using the mammalian auditory system as an example, that the presence of a subpopulation structure within a neural population is consistent with an optimal neural code.

7 pages total, including 2 figures, published by Physical Review Letters

References in corpus (3)

• Point singularities and suprathreshold stochastic resonance in optimal coding
• How behavioral constraints may determine optimal sensory representations
• Maximally Informative Stimuli and Tuning Curves for Sigmoidal Rate-Coding Neurons and Populations

Cited by in corpus (7)

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