NOISE ADAPTATION IN INTEGRATE-AND-FIRE NEURONS

The statistical spiking response of an ensemble of identically prepare d stochastic integrate-and-fire neurons to a rectangular input current plus gaussian white noise is analyzed. It is shown that, on average, integrate- and-fire neurons adapt to the root-mean-square noise level of their input. This phenomenon is referred to as noise adaptation. No ise adaptation is characterized by a decrease in the average neural fi ring rate and an accompanying decrease in the average value of the gen erator potential, both of which can be attributed to noise-induced res ets of the generator potential mediated by the integrate-and-fire mech anism. A quantitative theory of noise adaptation in stochastic integra te-and-fire neurons is developed. It is shown that integrate-and-fire neurons, on average, produce transient spiking activity whenever there is an increase in the level of their input noise. This transient nois e response is either reduced or eliminated over time, depending on the parameters of the model neuron. Analytical methods are used to prove that nonleaky integrate-and-fire neurons totally adapt to any constant input noise level, in the sense that their asymptotic spiking rates a re independent of the magnitude of their input noise. For leaky integr ate-and-fire neurons, the long-run noise adaptation is not total, but the response to noise is partially eliminated. Expressions for the pro bability density function of the generator potential and the first two moments of the potential distribution are derived for the particular case of a nonleaky neuron driven by gaussian white noise of mean zero and constant variance. The functional significance of noise adaptation for the performance of networks comprising integrate-and-fire neurons is discussed.