Neural mechanisms for sparse, informative and background-invariant coding of vocalizations

To efficiently process natural environments, many species have sensory systems that selectively encode behaviorally relevant information. Vocal communicators such as humans and songbirds rely on their auditory systems to recognize vocalizations and to extract vocalizations from complex auditory scenes. Yet many of the neural correlates of these perceptual abilities remain poorly understood. In this dissertation, I describe neural mechanisms by which the songbird auditory system produces sparse, informative and background-invariant neural representations of vocalizations. First, I show that auditory midbrain neurons encode vocalizations differently than other complex sounds, and that subthreshold excitation and inhibition may facilitate stimulus-dependent encoding of vocalizations. Second, I show that the responses of individual midbrain neurons can be unreliable, and that pooling the responses of correlated and similarly tuned neurons facilitates the neural discrimination of vocalizations. Third, I show that sparse coding neurons in the songbird forebrain extract individual vocalizations from auditory scenes at signal-to-noise ratios that match behavior. Lastly, I show that a simple neural circuit of delayed inhibition transforms a dense and background-sensitive neural representation into a sparse and background-invariant representation, in as little as one synapse. Together, these findings illuminate previously unknown mechanisms for selective vocalization coding, suggest a behaviorally relevant role for the ubiquitous phenomenon of sparse neural coding, and provide a neural correlate for the perceptual extraction of vocalizations from complex auditory scenes.