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PhD Defense by Dakshitha Anandakumar

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Dakshitha Anandakumar
BME PhD Defense Presentation

Date: 2022-12-02
Time: 9:30 am
Location / Meeting Link: Whitehead Research building #400, Emory University

Committee Members:
Dr. Robert C Liu (Advisor), Dr. Gordon Berman, Dr. Ming-Fai Fong, Dr. Joseph Manns, Dr. Malavika Murugan

Title: Noncanonical Auditory Cortical Plasticity for Associative Learning of New Sounds that Drive an Innate Social Behavior

Abstract:
The circuits underlying processing of sounds that elicit stereotyped innate behaviors are largely shaped by evolutionary pressures of natural selection. But within the constraints laid out by predisposed circuits, everyday experiences can cause new sounds to be associated with already familiar behaviors. However, it is still unclear where along the auditory pathway the different acoustic signals are transformed into a representation that reflect a sound’s common behavioral salience. In this thesis, we investigate the coding mechanisms of the auditory cortex (AC) in representing a novel sound that become associated with an innate behavior normally elicited by a natural vocalization. We train mice in a social cue conditioning paradigm to recognize a complex target sound to localize pups for retrieval - just like pups’ ultrasonic calls would naturally do. Following training, we recorded neural activity from populations of neurons within the core and secondary AC of awake, head-fixed mice. Our results show that the secondary AC acts as a site of convergence in the coding of the novel and natural sounds, which are acoustically distinct but behaviorally fall into the same semantic category. We suggest that the core region reformats the representation of learned sound cues in a way that acoustic information reaching the secondary AC can be efficiently interfaced with the existing representation of sounds that drive retrieval behavior. Taken together, this work furthers our understanding of how plasticity emerges in primary and secondary auditory cortices to drive adaptive learning of new sounds that map onto existing natural behaviors.

 

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  • Workflow Status:Published
  • Created By:Tatianna Richardson
  • Created:11/18/2022
  • Modified By:Tatianna Richardson
  • Modified:11/18/2022

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