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Noise in Integrate-and-Fire Neurons: From Stochastic Input to Escape Rates

MPI für Strömungsforschung, D-37073 Göttingen, Germany

Wulfram Gerstner

MANTRA Center for Neuromimetic Systems, Swiss Federal Institute of Technology, EPFL/DI, CH-1015 Lausanne, Switzerland

We analyze the effect of noise in integrate-and-fire neurons driven by time-dependent input and compare the diffusion approximation for the membrane potential to escape noise. It is shown that for time-dependent subthreshold input, diffusive noise can be replaced by escape noise with a hazard function that has a gaussian dependence on the distance between the (noise-free) membrane voltage and threshold. The approximation is improved if we add to the hazard function a probability current proportional to the derivative of the voltage. Stochastic resonance in response to periodic input occurs in both noise models and exhibits similar characteristics.

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