Decoding Sound Source Location and Separation Using Neural Population Activity Patterns

Abstract

The strategies by which the central nervous system decodes the properties of sensory stimuli, such as sound source location, from the responses of a population of neurons are a matter of debate. We show, using the average firing rates of neurons in the inferior colliculus (IC) of awake rabbits, that prevailing decoding models of sound localization (summed population activity and the population vector) fail to localize sources accurately due to heterogeneity in azimuth tuning across the population. In contrast, a maximum-likelihood decoder operating on the pattern of activity across the population of neurons in one IC accurately localized sound sources in the contralateral hemifield, consistent with lesion studies, and did so with a precision consistent with rabbit psychophysical performance. The pattern decoder also predicts behavior in response to incongruent localization cues consistent with the long-standing “duplex” theory of sound localization. We further show that the pattern decoder accurately distinguishes two concurrent, spatially separated sources from a single source, consistent with human behavior. Decoder detection of small amounts of source separation directly in front is due to neural sensitivity to the interaural decorrelation of sound, at both low and high frequencies. The distinct patterns of IC activity between single and separated sound sources thereby provide a neural correlate for the ability to segregate and localize sources in everyday, multisource environments.