Abstract
In this paper, we develop a dynamical point process model for how complex sounds are represented by neural spiking in auditory nerve fibers. Although many models have been proposed, our point process model is the first to capture elements of spontaneous rate, refractory effects, frequency selectivity, phase locking at low frequencies, and short-term adaptation, all within a compact parametric approach. Using a generalized linear model for the point process conditional intensity, driven by extrinsic covariates, previous spiking, and an input-dependent charging/discharging capacitor model, our approach robustly captures the aforementioned features on datasets taken at the auditory nerve of chinchilla in response to speech inputs. We confirm the goodness of fit of our approach using the Time-Rescaling Theorem for point processes.
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Acknowledgements
The authors would like to first thank Bob Wickesberg and Hanna Stevens, for the rich dataset that made this work possible; Emery Brown for his advice and mentorship; Anne Dreyer for her input on modeling auditory spikes; and Bryce Lobdell, for his input on using critical bandwidth filters.
A. Trevino would like to acknowledge the financial support from the UIUC SURGE fellowship, the NIH-UIUC Sensory Neuroscience Training Grant, and the NSF Graduate Research Fellowship. T. P. Coleman would like to acknowledge the financial support from the AFOSR Complex Networks Program via award no FA9550-08-1-0079.
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Trevino, A., Coleman, T.P. & Allen, J. A dynamical point process model of auditory nerve spiking in response to complex sounds. J Comput Neurosci 29, 193–201 (2010). https://doi.org/10.1007/s10827-009-0146-6
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DOI: https://doi.org/10.1007/s10827-009-0146-6