We study two models of neuronal spiking driven by amplitude modulated and white noise. For both non-dynamical threshold neuron model and modified Ornstein-Uhlenbeck diffusion model, we found a regime called "phantom stochastic resonance" in which an optimum level of noise maximizes the number of neural spikes spaced by time intervals that are equal to the modulation period. Hence both models, as well as the experiments in literature, indicate an essentially nonlinear phenomenon extracting the relevant sensory information from these stimuli.
Phantom Stochastic Resonance
GIRAUDO, Maria Teresa
2008-01-01
Abstract
We study two models of neuronal spiking driven by amplitude modulated and white noise. For both non-dynamical threshold neuron model and modified Ornstein-Uhlenbeck diffusion model, we found a regime called "phantom stochastic resonance" in which an optimum level of noise maximizes the number of neural spikes spaced by time intervals that are equal to the modulation period. Hence both models, as well as the experiments in literature, indicate an essentially nonlinear phenomenon extracting the relevant sensory information from these stimuli.
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