Int J Neural Syst - Span: spike pattern association neuron for learning spatio-temporal spike patterns.

Tópicos

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Resumo

Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN - a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the precise timing of spikes. The idea of the proposed algorithm is to transform spike trains during the learning phase into analog signals so that common mathematical operations can be performed on them. Using this conversion, it is possible to apply the well-known Widrow-Hoff rule directly to the transformed spike trains in order to adjust the synaptic weights and to achieve a desired input/output spike behavior of the neuron. In the presented experimental analysis, the proposed learning algorithm is evaluated regarding its learning capabilities, its memory capacity, its robustness to noisy stimuli and its classification performance. Differences and similarities of SPAN regarding two related algorithms, ReSuMe and Chronotron, are discussed.

Resumo Limpo

spike neural network snn shown suitabl tool process spatiotempor inform howev due inher complex formul effici supervis learn algorithm snn difficult remain import problem research area articl present span spike neuron abl learn associ arbitrari spike train supervis fashion allow process spatiotempor inform encod precis time spike idea propos algorithm transform spike train learn phase analog signal common mathemat oper can perform use convers possibl appli wellknown widrowhoff rule direct transform spike train order adjust synapt weight achiev desir inputoutput spike behavior neuron present experiment analysi propos learn algorithm evalu regard learn capabl memori capac robust noisi stimuli classif perform differ similar span regard two relat algorithm resum chronotron discuss

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