Single neuron dynamics: noise-enhanced signal processing

The authors consider a noisy bistable single neuron model in the presence of periodic external 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. The problem is treated 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. The proposed model should be of interest in situations where a single inherently noisy neutron is the receptor of a periodic signal, which is itself noisy, either from the network or from an external source