Abstract
Neural spike trains are commonly characterized as a Poisson point process. However, the Poisson assumption is a poor model for spiking in auditory nerve fibres because it is known that interspike intervals display positive correlation over long time scales and negative correlation over shorter time scales. We have therefore developed a biophysical model based on the well-known Meddis model of the peripheral auditory system, to produce simulated auditory nerve fibre spiking statistics that more closely match the firing correlations observed in empirical data. We achieve this by introducing biophysically realistic ion channel noise to an inner hair cell membrane potential model that includes fractal fast potassium channels and deterministic slow potassium channels. We succeed in producing simulated spike train statistics that match empirically observed firing correlations. Our model thus replicates macro-scale stochastic spiking statistics in the auditory nerve fibres due to modeling stochasticity at the micro-scale of potassium channels.
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Acknowledgments
Mark D. McDonnell’s contribution was supported by an Australian Research Fellowship from the Australian Research Council (project number DP1093425) and the National Health and Medical Research Council (NHMRC) of Australia (project grant, APP1050832). N. Iannella’s contribution was supported by the People Programme (Marie Curie Actions) of the European Unions Seventh Framework Programme (FP7/2007-2013) under REA grant agreement No PCOFUND-GA-2012-600181. We would like to thank Peter Heil from Leibniz Institute for Neurobiology, Idan Segev of Hebrew University and Luke Hallam of Charles Sturt University for helpful discussions.
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Bahar Moezzi, Nicolangelo Iannella and Mark D. McDonnell declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Moezzi, B., Iannella, N. & McDonnell, M.D. Ion channel noise can explain firing correlation in auditory nerves. J Comput Neurosci 41, 193–206 (2016). https://doi.org/10.1007/s10827-016-0613-9
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DOI: https://doi.org/10.1007/s10827-016-0613-9