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Method for Training a Spiking Neuron to Associate Input-Output Spike Trains

Abstract : We propose a novel supervised learning rule allowing the training of a precise input-output behavior to a spiking neuron. A single neuron can be trained to associate (map) different output spike trains to different multiple input spike trains. Spike trains are transformed into continuous functions through appropriate kernels and then Delta rule is applied. The main advantage of the method is its algorithmic simplicity promoting its straightforward application to building spiking neural networks (SNN) for engineering problems. We experimentally demonstrate on a synthetic benchmark problem the suitability of the method for spatio-temporal classification. The obtained results show promising efficiency and precision of the proposed method.
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Ammar Mohemmed, Stefan Schliebs, Satoshi Matsuda, Nikola Kasabov. Method for Training a Spiking Neuron to Associate Input-Output Spike Trains. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.219-228, ⟨10.1007/978-3-642-23957-1_25⟩. ⟨hal-01571331⟩



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