Noise and Nonlinearity in Neuron Modeling

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