Single Neuron Dynamics: Noise-Enhanced Signal Processing

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, or signal, 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 within 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 model should be of interest in situations where a single inherently noisy neuron is the receptor of a periodic signal, which is itself noisy, either from the network or from an external source.