A Robust, Compact and Diverse Population Code for Competing Sounds in Auditory Cortex

Abstract

Cortical circuits encoding sensory information consist of populations of neurons, yet how information aggregates via pooling individual cells remains poorly understood. Such pooling may be particularly important in noisy settings where single neuron encoding is degraded. One example is the cocktail party problem, with competing sounds from multiple spatial locations. How populations of neurons in auditory cortex (ACx) code competing sounds have not been previously investigated. Here, we apply a novel information theoretic approach to estimate information in populations of neurons in ACx about competing sounds from multiple spatial locations, including both summed population (SP) and labeled line (LL) codes. We find that a small subset of neurons is sufficient to nearly maximize mutual information over different spatial configurations, with the LL code outperforming the SP code, and approaching information levels attained without noise. Moreover, with a LL code, units with diverse spatial responses, including both regular and narrow-spiking units, constitute the best pool. Finally, information in the population increases with spatial separation between target and masker, in correspondence with behavioral results on spatial release from masking in human and animals. Taken together, our results reveal that a compact and diverse population of neurons in ACx provide a robust code for competing sounds from different spatial locations.