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The supervised learning methods for spiking neurons based on temporal encoding are important foundation for the development of spiking neural networks. During the learning process, the synaptic weights of a spiking neuron are adjusted to make the neuron emit a specific spike train. Because various learning methods use the information of input spikes to calculate the adjustment of synaptic weights, how many input spikes participated in the calculation is a critical factor that can influence learning performance. This paper chooses an important category of learning methods as the research object to study the factor. The input spikes participated in weight adjustment are contained in a time interval. An optimal time interval that contains the most appropriate number of input spikes is proposed based on the characteristic of the category of learning methods. The length of the optimal time interval is determined by comprehensive consideration of desired and actual output spikes. The results of a lot of experiments show that the optimal time interval can obtain the highest learning performance under various experimental settings. If other time intervals are longer than the optimal time interval, an overlapping problem of input spikes will occur and the learning performance will decline a lot. The learning accuracy of the optimal time interval can be about 55% higher than the learning accuracy of an other longer time interval. If other time intervals are shorter than the optimal time interval, the input spikes contained in them will be insufficient to adjust synaptic weights and the learning performance will also decline. The learning accuracy of the optimal time interval can be about 8% higher than the learning accuracy of an other shorter time interval. In addition, the optimal time interval can also improve the generalization ability and pattern storage capability of the category of learning methods.
This article was published in the following journal.
Name: Neural networks : the official journal of the International Neural Network Society
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