Neural Comput - Supervised learning in multilayer spiking neural networks.

Tópicos

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Resumo

We introduce a supervised learning algorithm for multilayer spiking neural networks. The algorithm overcomes a limitation of existing learning algorithms: it can be applied to neurons firing multiple spikes in artificial neural networks with hidden layers. It can also, in principle, be used with any linearizable neuron model and allows different coding schemes of spike train patterns. The algorithm is applied successfully to classic linearly nonseparable benchmarks such as the XOR problem and the Iris data set, as well as to more complex classification and mapping problems. The algorithm has been successfully tested in the presence of noise, requires smaller networks than reservoir computing, and results in faster convergence than existing algorithms for similar tasks such as SpikeProp.

Resumo Limpo

introduc supervis learn algorithm multilay spike neural network algorithm overcom limit exist learn algorithm can appli neuron fire multipl spike artifici neural network hidden layer can also principl use lineariz neuron model allow differ code scheme spike train pattern algorithm appli success classic linear nonsepar benchmark xor problem iri data set well complex classif map problem algorithm success test presenc nois requir smaller network reservoir comput result faster converg exist algorithm similar task spikeprop

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