CRCNS: Hierarchical Computations for Vocal Communication.

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Vocal communication relies on the production and perception of stereotyped signals that carry particular messages. Bioacousticians studying animal communication and linguists studying human speech have used a unique simple classification scheme for these information-bearing sound elements: phonemes in speech and call categories in animal calls. Although appealing from a computational perspective, classification of communication sounds into a fixed number of phonemes or call types fails to capture all of the complexities of the vocal signaling used by humans or animals. At the same time, this core view has guided past neuroscientific research, including ours: auditory neuroscientists and neurolinguists have searched with mixed success for high-level auditory neurons or regions that are selective for call types or phonemes and invariant to variations of sounds within these groups. One of the sobering lessons of this past research is that we have yet to find a cortical map representing phonemes or call types. In addition, the non-linear transformations yielding invariant and selective responses for certain phonemes/call types remain to be described. For this project, we propose to explore a broader hypothesis of auditory signaling and perception and explore its neural correlate. Our hypothesis is that signaling is organized in an information hierarchy that is best represented as a tree classification scheme. This hierarchical organization of information is found in the acoustics, in the behavioral responses and in the neural representations and computations performed by the auditory system and association areas. We propose to test this hypothesis and reveal the neural representations of communication signals using songbirds as a model system. In this collaborative proposal, we will 1) develop a novel wireless device that will combine array neural recordings, physiological measurements and acoustical recordings, 2) study the hierarchical organization of a complete repertoire as revealed by the acoustical structure of calls, 3) measure the physiological and behavioral responses to these communication sounds, 4) measure the neural representation for these communication sounds and 5) model this communication system using our hierarchical information hypothesis as a guiding principle. This analysis will elucidate not only the nature of the meaning in communication signals but also how the sound to meaning transformations are performed by the brain.