College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China.
Comput Intell Neurosci. 2021 Nov 24;2021:8592824. doi: 10.1155/2021/8592824. eCollection 2021.
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.
作为一种新的基于大脑的人工神经网络计算模型，尖峰神经网络通过精确计时的尖峰序列来传输和处理信息。构建有效的学习方法是尖峰神经网络的一个重要研究领域。在本文中，我们提出了一种用于多层前馈尖峰神经网络的监督学习算法；所有神经元都可以在所有层中发射多个尖峰。前馈网络由受生物启发的长期记忆尖峰响应模型控制的尖峰神经元组成，其中早期尖峰对不应期的影响不容忽视，以纳入适应效应。梯度下降法用于推导学习尖峰序列的突触权重更新规则。该算法在时空模式学习问题上进行了测试和验证，包括一组尖峰序列学习任务和四个 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。
