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    Experience dependent coding of intonations by offsets in mouse auditory cortex

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    CHONG-DISSERTATION-2019.pdf (4.428Mb)
    Date
    2019-01-07
    Author
    Chong, Kelly K.
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    Abstract
    Acoustic communication is an important aspect of many social interactions across mammalian species. The encoding of intra-species vocalizations and plasticity mechanisms engaged during the process of learning vocalizations are poorly understood. This is particularly true with regards to how sensory representations of vocalizations is transformed between primary to secondary auditory cortical areas. Moreover, learning in a natural communication paradigm engages auditory cortical plasticity mechanisms in ways that are distinct from laboratory operant training paradigms, emphasizing the importance of studying learning in social settings. Our work utilizes a natural paradigm in which mouse mothers learn the behavioral significance of pup ultrasonic vocalizations during maternal experience to study auditory cortical plasticity in a natural social context. Specifically, we aim to determine how mice learn to use acoustic features to discriminate vocalization categories. One of the acoustic features that can be used to distinguish whistle-like mouse vocalizations is their frequency trajectory or intonation, which can be modeled using a parameterized sinusoidally frequency modulated tone. We will employ a combination of in vivo head-fixed awake single unit electrophysiology and modeling of the natural mouse vocalization repertoire to explore the frequency trajectory parameter space. With this approach, we aim to study the native sensitivity of auditory cortical neurons to frequency trajectory parameters across primary and secondary auditory regions, as well as how sensitivity to these parameters changes with experience. This work will further our understanding of how the acoustic feature space is represented by the auditory cortex, and uncovers a potential mechanism by which natural sound categories are learned.
    URI
    http://hdl.handle.net/1853/62628
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    • Department of Biomedical Engineering Theses and Dissertations [575]
    • Georgia Tech Theses and Dissertations [23877]

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