Markov analysis of stochastic resonance in a periodically driven integrate-fire neuron

摘要

We model the dynamics of the leaky integrate-fire neuron under periodicstimulation as a Markov process with respect to the stimulus phase. This avoidsthe unrealistic assumption of a stimulus reset after each spike made in earlierwork and thus solves the long-standing reset problem. The neuron exhibitsstochastic resonance, both with respect to input noise intensity and stimulusfrequency. The latter resonance arises by matching the stimulus frequency tothe refractory time of the neuron. The Markov approach can be generalized toother periodically driven stochastic processes containing a reset mechanism.