Abstract
A method to solve the classification task using a spiking neural network with encoding the input by patterns of spike times along with Spike-Timing-Dependent Plasticity learning is proposed. Input data is encoded using Gaussian receptive fields. The method is tested on Fisher’s Iris dataset. As the result, after learning a neuron responds with less latency to patterns encoding samples of the class on which it was trained, in comparison to the classes it was not trained on.
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