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[Abstract Title]. - Society for Neuroscience

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Poster<br />

259. Cochlear Mechanoreceptors<br />

Time: Sunday, November 16, 2008, 1:00 pm - 5:00 pm<br />

Program#/Poster#: 259.15/BB11<br />

<strong>Abstract</strong>: Withdrawn<br />

Poster<br />

259. Cochlear Mechanoreceptors<br />

Time: Sunday, November 16, 2008, 1:00 pm - 5:00 pm<br />

Program#/Poster#: 259.16/BB12<br />

Topic: D.02.a. Mechanoreceptors and cochlea<br />

Support: University of Illinios SURGE Fellowship<br />

National Science Foundation Graduate Research Fellowship<br />

NIH Sensory <strong>Neuroscience</strong> Training Grant<br />

<strong>Title</strong>: A dynamical point process model <strong>for</strong> auditory nerve spiking encoding complex sounds<br />

Authors: *A. TREVINO 1 , R. E. WICKESBERG 2 , J. B. ALLEN 3 , T. P. COLEMAN 3 ;<br />

2 Psychology, 3 EE, 1 Univ. Illinois CU, Urbana, IL<br />

<strong>Abstract</strong>: Statistical point process analysis has recently proved quite successful <strong>for</strong> modeling<br />

neural spiking activity in many brain regions. We apply a dynamic point process approach to<br />

model the neural encoding of complex sounds in the auditory nerve. Specifically, voiced phones<br />

at different sound levels are presented to the cochlea of chinchillas, and subsequent auditory<br />

nerve spiking is recorded. We develop a parametric dynamic statistical point process model to<br />

per<strong>for</strong>m maximum-likelihood estimation and apply statistical goodness-of-fit procedures to<br />

verify our model. For this auditory model, we focus on spontaneous firing rate, envelope phaselocking,<br />

cochlear nonlinearities, and onset/offset characteristics. This rich dataset, combined with

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