电子学报2016，Vol.44Issue(12)：2877-2886,10.DOI:10.3969/j.issn.0372-2112.2016.12.010
基于脉冲序列核的脉冲神经元监督学习算法
A New Supervised Learning AIgorithm for Spiking Neurons Based on Spike Train KerneIs
摘要
Abstract
The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit arbitrary spike trains in response to given synaptic inputs.However,due to the discontinuity in the spike process,the formula-tion of efficient supervised learning algorithms for spiking neurons is difficult and remains an important problem in the re-search area.Based on the definition of kernel functions for spike trains,this paper proposes a new supervised learning algo-rithm for spiking neurons with temporal encoding.The learning rule for synapses is developed by constructing the multiple spikes error function using spike train kernels,and its learning rate is adaptively adjusted according to the actual firing rate of spiking neurons during learning.The proposed algorithm is successfully applied to various spike trains learning tasks,in which the desired spike trains are encoded by Poisson process or linear method.Furthermore,the effect of different kernels on the performance of the learning algorithm is also analyzed.The experiment results show that our proposed method has higher learning accuracy and flexibility than the existing learning methods,so it is effective for solving complex spatio-tem-poral spike pattern learning problems.关键词
脉冲神经元/监督学习/脉冲序列核/内积/脉冲序列学习Key words
spiking neuron/supervised learning/spike train kernel/inner product/spike train learning分类
信息技术与安全科学引用本文复制引用
蔺想红,王向文,党小超..基于脉冲序列核的脉冲神经元监督学习算法[J].电子学报,2016,44(12):2877-2886,10.基金项目
国家自然科学基金（No．61165002，No．61363059）；甘肃省自然科学基金（No．1506RJZA127）；甘肃省高等学校科研项目 （）
