A robust and compact population code for competing sounds in auditory cortex.

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 code competing sounds have not been previously investigated. Here, we apply a novel information-theoretic approach to estimate information in populations of neurons in mouse auditory cortex 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 labeled line code outperforming the summed population code and approaching information levels attained in the absence of competing stimuli. Finally, information in the labeled line code increases with spatial separation between target and masker, in correspondence with behavioral results on spatial release from masking in humans and animals. Taken together, our results reveal that a compact population of neurons in auditory cortex provides a robust code for competing sounds from different spatial locations.NEW & NOTEWORTHY Little is known about how populations of neurons within cortical circuits encode sensory stimuli in the presence of competing stimuli at other spatial locations. Here, we investigate this problem in auditory cortex using a recently proposed information-theoretic approach. We find a small subset of neurons nearly maximizes information about target sounds in the presence of competing maskers, approaching information levels for isolated stimuli, and provides a noise-robust code for sounds in a complex auditory scene.

Estimating Mutual Information for Spike Trains: A Bird Song Example.

Zebra finches are a model animal used in the study of audition. They are adept at recognizing zebra finch songs, and the neural pathway involved in song recognition is well studied. Here, this example is used to illustrate the estimation of mutual information between stimuli and responses using a Kozachenko-Leonenko estimator. The challenge in calculating mutual information for spike trains is that there are no obvious coordinates for the data. The Kozachenko-Leonenko estimator does not require coordinates; it relies only on the distance between data points. In the case of bird songs, estimating the mutual information demonstrates that the information content of spiking does not diminish as the song progresses.