NEURAL ENCODING OF COMPLEX SOUNDS

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DESCRIPTION (provided by applicant): The normal auditory system possesses exquisitely well-adapted processes for extracting behaviorally relevant acoustic signals from the environment, especially under challenging listening conditions such as in the presence of competing signals or background noise. These processes are poorly understood physiologically, however. The objective of this proposal is to understand the neurobiology of a particular auditory processing nonlinearity (auditory spectral contrast tuning) as it relates to noisy vocalization representation in the mammalian auditory system. Aim 1 will involve measuring the sound frequency range that auditory neurons use for making estimates of spectral contrast. This information will prove critical for understanding how contrast tuning is computed biologically. Aim 2 will determine the coding portion of the input/output function for neurons tuned to spectral contrast. The nature of this function will allow inferences to be made about how this particular processing feature is read out by neurons at a later processing stage. Aim 3 will directly test the responses of contrast-tuned neurons to vocalizations embedded in background noise, with the expectation that noisy vocalizations will produce spiking responses in these neurons that carry more information as the amount of noise increases, up to a limit. When completed, this research will result in an improved understanding of a potentially important response property in the central auditory system with potential relevance for engineered noise-reduction systems, which could have great potential for individuals reliant upon a hearing prosthesis for acts of daily living. This project addresses our long-term goal of understanding more clearly how environmental and communication sounds are encoded in the mammalian auditory system under noisy conditions. We will achieve this goal by exploring how particular nonlinear processing features of neurons in primate auditory cortex are created, encode stimulus features and respond to vocalizations degraded by noise. Ultimately, this information may aid in refining algorithms that could allow auditory prosthesis devices such as hearing aids and cochlear implants to function better in everyday noisy environments.