| |
Abstract:
We study here a simple stochastic single neuron model with
delayed self--feedback capable of generating spike trains. The
model is investigated numerically and found that its spike trains
show a peculiar resonant behavior between "noise" and "delay". In
order to gain the insight in this resonance, we abstract the model
and study a stochastic binary element whose transition probability
depends on its state at a fixed interval in the past. With this
abstracted model we can analytically capture the time interval
histograms between spikes and how the resonance between noise and
delay arises. The resonance is also observed when such stochastic
binary elements are coupled through delayed interaction.
|
