Supervised learning with spiking neural networks

We derive a supervised learning algorithm for a spiking neural network which encodes information in the timing of spike trains. This algorithm is similar to the classical error backpropagation algorithm for sigmoidal neural network but the learning parameter is adaptively changed. The algorithm is applied to a complex nonlinear classification problem and the results show that the spiking neural network is capable of performing nonlinearly separable classification tasks. Several issues concerning the spiking neural network are discussed.

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