Cooperative Stochastic Processes in Reduced Neuron Models

摘要

We consider a noisy bistable single neuron model in the presence of periodicexternal modulation. The modulation introduces a correlated switching between states driven by the noise. The information flow through the system from the modulation, to the output switching events, leads to a succession of strong peaks in the power spectrum. The signal-to-noise ratio (SNR) obtained from this power spectrum is a measure of the information content in the neuron response. With increasing noise intensity, the SNR passes through a maximum, an effect which has been called stochastic resonance, and which was first advanced as a possible explanation of the observed periodicity in the recurrence of the earth's ice ages. We treat the problem with the framework of a recently developed approximate theory, valid in the limits of weak noise intensity, weak periodic forcing and low forcing frequency, for both additive and multiplicative noise. Our simple noisy nonlinear model is then used to discuss the two basic symmetries endemic to bistable systems. The probability density of escape times corresponding to one of these symmetries exhibits all the substantive features of experimental inter-spike-interval histograms recorded from real, periodically forced sensory neurons. The analysis elucidates the critical role of noise in the transmission of sensory information in the nervous system.