Abstract
A generalized model of spiking neuron is proposed as a non-stationary stochastic spike sequences processing unit. The generalized spiking neuron model is described with a conditional spike generation probability distribution and with a state evolution operator. An information theory language is used for the convenient neuron’s learning tasks description. The problem of spiking neuron learning with the teacher is solved using information entropy minimization algorithm. A particular implementation of generalized model using stochastic Spike Response Model with alpha-functions set is provided. A task of time delay maintenance between input and output spikes and a task of detecting of a spiking pattern in a noisy stream of pulse signals are considered using extended SRM neuron. It is shown that after the using of the proposed learning method spiking neuron became capable to detect a spatial-temporal pulse pattern and to serve as an adaptive delay unit.
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Sinyavskiy, O.Y., Kobrin, A.I. Generalized stochastic spiking neuron model and extended spike response model in spatial-temporal pulse pattern detection task. Opt. Mem. Neural Networks 19, 300–309 (2010). https://doi.org/10.3103/S1060992X10040077
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DOI: https://doi.org/10.3103/S1060992X10040077