FAST TEMPORAL ENCODING AND DECODING WITH SPIKING NEURONS

We propose a simple theoretical structure of interacting integrate-and -fire neurons that can handle fast information processing and may acco unt for the fact that only a few neuronal spikes suffice to transmit i nformation in the brain. Using integrate-and-fire neurons that are sub jected to individual noise and to a common external input, we calculat e their first passage time (FPT), or interspike interval. We suggest u sing a population average for evaluating the FPT that represents the d esired information. Instantaneous lateral excitation among these neuro ns helps the analysis. By employing a second layer of neurons with var iable connections to the first layer, we represent the strength of the input by the number of output neurons that fire, thus decoding the te mporal information. Such a model can easily lead to a logarithmic rela tion as in Weber's law. The latter follows naturally from information maximization if the input strength is statistically distributed accord ing to an approximate inverse law.