Structural and Molecular Correlates of Auditory Plasticity
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CitationDiel, Erin. 2018. Structural and Molecular Correlates of Auditory Plasticity. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractThe correspondence of a neuron’s spatial location and its tuning to particular features of the external environment is described as topography. Topography represents the brain's map of the world and is a pervasive parameter in circuit organization. The onset of experience drives topographic refinement, and atypical experience can drive large-scale shifts in map organization. The most widely studied topographies in the context of map plasticity are in sensory cortex, where thalamocortical axons provide patterned innervation to layer IV. The precision of thalamocortical projections that underlie these topographies has not been systematically explored, and functional responses suggest that despite smooth macroscopic gradients in receptive fields, an appreciable amount of local heterogeneity exists. The auditory system has advantages for questions such as these because its one dimensional gradient of frequency tuning, termed tonotopy, can be manipulated precisely with spectrally simple or complex stimuli. In this dissertation, I describe the previously unknown branching patterns and connectivity of thalamocortical axons within primary auditory cortex in mice that have experienced normal or abnormal auditory environments early in life. These findings clarify existing questions surrounding local heterogeneity in topographic maps and have implications for spectral computations performed within A1. Next, I use molecular methods to probe the diverse cellular responses within cortex to early experience. Finally, I take a functional imaging approach to understand the changing activity patterns within this malleable circuit that actualize topographic refinement. Considering the pervasiveness and plasticity of topographic maps, characterizing these organizations at diverse resolutions has implications for understanding circuit computations and their capacity for change.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41121231
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