| When is an Integrate-and-fire Neuron like a Poisson Neuron? (1996) | |||||||||||||||||
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| In the Poisson neuron model, the output is a rate-modulated Poisson process (Snyder and Miller, 1991); the time varying rate parameter r(t) is an instantaneous function G[:] of the stimulus, r(t) = G[s(t)]. In a Poisson neuron, then, r(t) gives the instantaneous firing rate---the instantaneous probability of firing at any instant t---and the output is a stochastic function of the input. In part because of its great simplicity, this model is widely used (usually with the addition of a refractory period), especially in in vivo single unit electrophysiological studies, where s(t) is usually taken to be the value of some sensory stimulus. In the integrate-and-fire neuron model, by contrast, the output is a filtered and thresholded function of the input: the input is passed through a low-pass filter (determined by the membrane time constant ΓΈ ) and integrated until the membrane potential v(t) reaches threshold `, at which point v(t) is reset to its initial value. By contrast with the Po... | |||||||||||||||||
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