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Noise and Nonlinearity in Neuron Modeling

  • A. R. Bulsara
Part of the Institute for Nonlinear Science book series (INLS)

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.

Keywords

Firing Rate Neuron Modeling Noise Variance Stochastic Resonance Firing Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • A. R. Bulsara

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