Supervised Learning Algorithm for Multilayer Spiking Neural Networks with Long-Term Memory Spike Response Model.
Supervised Learning Algorithm for Multilayer Spiking Neural Networks with Long-Term Memory Spike Response Model.
复制标题
具有长期记忆尖峰响应模型的多层尖峰神经网络的监督学习算法
DOI：
10.1155/2021/8592824
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发表时间：
2021
影响因子：
--
通讯作者：
Wang X
中科院分区：
文献类型：
--
作者：
Lin X;Zhang M;Wang X
作为一种新的人工神经网络计算模型，脉冲神经网络通过精确定时的脉冲序列来传输和处理信息。构造有效的学习方法是脉冲神经网络的一个重要研究方向。在本文中，我们提出了一个监督学习算法的多层前馈脉冲神经网络，所有的神经元可以发射多个尖峰在所有层。前馈网络由尖峰神经元由生物学上合理的长期记忆尖峰反应模型，其中早期尖峰对不应性的影响不能忽略，以纳入适应效应。采用梯度下降法推导神经元突触权重更新规则，用于神经元锋电位序列的学习。该算法在时空模式学习问题上进行了测试和验证，包括一组尖峰训练学习任务和四个UCI数据集上的非线性模式分类问题。仿真结果表明，与其他监督学习算法相比，该算法可以提高学习精度。
As a new brain-inspired computational model of artificial neural networks, spiking neural networks transmit and process information via precisely timed spike trains. Constructing efficient learning methods is a significant research field in spiking neural networks. In this paper, we present a supervised learning algorithm for multilayer feedforward spiking neural networks; all neurons can fire multiple spikes in all layers. The feedforward network consists of spiking neurons governed by biologically plausible long-term memory spike response model, in which the effect of earlier spikes on the refractoriness is not neglected to incorporate adaptation effects. The gradient descent method is employed to derive synaptic weight updating rule for learning spike trains. The proposed algorithm is tested and verified on spatiotemporal pattern learning problems, including a set of spike train learning tasks and nonlinear pattern classification problems on four UCI datasets. Simulation results indicate that the proposed algorithm can improve learning accuracy in comparison with other supervised learning algorithms.
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影响因子：
--
作者：
Fu SY;Yang GS;Kuai XK
通讯作者：
Kuai XK
影响因子：
7.8
作者：
Ghosh-Dastidar, Samanwoy;Adeli, Hojjat
通讯作者：
Adeli, Hojjat
影响因子：
3.1
作者：
Li X;Yi H;Luo S
通讯作者：
Luo S
影响因子：
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作者：
Cyr, Andre;Theriault, Frederic
通讯作者：
Theriault, Frederic
影响因子：
7.8
作者：
GERSTNER, W;VANHEMMEN, JL
通讯作者：
VANHEMMEN, JL