A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection
A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection
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DOI：
10.1016/j.neunet.2009.04.003
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发表时间：
2009-12-01
期刊：
影响因子：
7.8
通讯作者：
Adeli, Hojjat
中科院分区：
文献类型：
--
作者：
Ghosh-Dastidar, Samanwoy;Adeli, Hojjat
提出了一种新的多峰神经网络模型，在该模型中，一个神经元的信息通过多个突触以多个峰的形式传递给下一个神经元。为了训练MuSpiNN，提出了一种新的监督学习算法，称为多SpikeProp算法。该模型和学习算法采用了作者在最近的一篇论文中提出的启发式规则和最优参数值，将原始的单峰脉冲神经网络(SNN)模型的效率提高了两个数量级。使用三个日益复杂的问题来评估MuSpiNN和多SpikeProp的分类精度：异或问题、Fisher虹膜分类问题以及癫痫和癫痫发作检测(EEG分类)问题。研究发现，与单峰SNN模型相比，MuSpiNN学习XOR问题所需的历元数是单峰SNN模型的两倍，但只需要四分之一的突触数量。对于虹膜和脑电信号的分类问题，采用模块化结构，将每个3类分类问题归结为3个2类分类问题，提高了分类精度。对于复杂的脑电分类问题，分类正确率在90.7%-94.8%之间，显著高于使用单峰SNN和SpikeProp的分类正确率82%。(C)2009爱思唯尔有限公司。保留所有权利。
Anew Multi-Spiking Neural Network (MuSpiNN) model is presented in which information from one neuron is transmitted to the next in the form of multiple spikes via multiple synapses. A new supervised learning algorithm, dubbed Multi-SpikeProp, is developed for training MuSpiNN. The model and learning algorithm employ the heuristic rules and optimum parameter values presented by the authors in a recent paper that improved the efficiency of the original single-spiking Spiking Neural Network (SNN) model by two orders of magnitude. The classification accuracies of MuSpiNN and Multi-SpikeProp are evaluated using three increasingly more complicated problems: the XOR problem, the Fisher iris classification problem, and the epilepsy and seizure detection (EEG classification) problem. It is observed that MuSpiNN learns the XOR problem in twice the number of epochs compared with the single-spiking SNN model but requires only one-fourth the number of synapses. For the iris and EEG classification problems, a modular architecture is employed to reduce each 3-class classification problem to three 2-class classification problems and improve the classification accuracy. For the complicated EEG classification problem a classification accuracy in the range of 90.7%-94.8% was achieved, which is significantly higher than the 82% classification accuracy obtained using the single-spiking SNN with SpikeProp. (C) 2009 Elsevier Ltd. All rights reserved.