An online supervised learning algorithm based on triple spikes for spiking neural networks
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Using precise times of every spike, spiking supervised learning has more effects on complex spatial-temporal pattern than that only through neuronal firing rates. The purpose of spiking supervised learning after temporal encoding is to make neural networks emit desired spike trains with precise firing time. Existing algorithms of spiking supervised learning have excellent performances, but mechanisms of them are most in an offline pattern or still have some problems. Based on an online regulative mechanism of biological neuronal synapses, this paper proposes an online supervised learning algorithm of multiple spike trains for spiking neural networks. The proposed algorithm can make a regulation of weights as soon as firing time of an output spike is obtained. Relationship among desired output, actual output and input spike trains is firstly analyzed and synthesized to select spikes simply and correctly for a direct regulation, and then a computational method is constructed based on simple triple spikes using this direct regulation. Results of experiments show that this online supervised algorithm improves learning performance obviously compared with offline pattern and has higher learning accuracy and efficiency than other learning algorithms.
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