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Noise and Nonlinearity in Neuron Modeling

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Fluctuations and Order

Part of the book series: Institute for Nonlinear Science ((INLS))

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Abstract

We consider the interpretation of time series data from firing events in periodically stimulated models of neurons. Theoretical models, representing the neurons as nonlinear switching elements subject to a Gaussian noise background, are considered. The cooperative effects, in particular, stochastic resonance, arising through the coupling of the noise to the modulation are examined together with their possible implications in the features of interspike-interval histograms (ISIHs) which are ubiquitous in neurophysiological data. Our approach provides a very simple and elegant interpretation of the ISIHs and elucidates the potentially constructive role of background noise.

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  1. A. R. Bulsara
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Editors and Affiliations

  1. The James Franck Institute, University of Chicago, 5640 South Ellis Avenue, Chicago, IL, 60637, USA

    Mark Millonas

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Bulsara, A.R. (1996). Noise and Nonlinearity in Neuron Modeling. In: Millonas, M. (eds) Fluctuations and Order. Institute for Nonlinear Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-3992-5_23

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  • DOI: https://doi.org/10.1007/978-1-4612-3992-5_23

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