Learning Precise Spike Times in a Two-Variable Spiking Neural Model

S.E. Anderson (USA)


pulse-coupled neural network, spiking, reinforcement


We evaluate the ability of reinforcement comparison learn ing to induce multispike patterns with sub-millisecond precision in a two-variable spiking neural model. We assume that a single reinforcement signal derived from the fit of the produced spike pattern with a target pattern is communicated with the neural model following pro duction of all spikes of the pattern. We find that arbitrary multispike patterns can be learned with a precision of 0.2 msec. Patterns of one to five spikes can be learned with a probability of success ranging from 20% to 70%.

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