BPSpike II: A New Backpropagation Learning Algorithm for Spiking Neural Networks

被引：1

作者：

Matsuda, Satoshi ^{[1
]}

机构：

[1] Nihon Univ, 1-2-1 Izumi Cho, Narashino, Chiba 2758575, Japan

来源：

NEURAL INFORMATION PROCESSING, ICONIP 2016, PT II | 2016年 / 9948卷

关键词：

Spiking neural networks; Learning algorithm; Backpropagation;

D O I：

10.1007/978-3-319-46672-9_7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Using gradient descent, we propose a new backpropagation learning algorithm for spiking neural networks with multi-layers, multi-synapses between neurons, and multi-spiking neurons. It adjusts synaptic weights, delays, and time constants, and neurons' thresholds in output and hidden layers. It guarantees convergence to minimum error point, and unlike SpikeProp and its extensions, does not need a one-to-one correspondence between actual and desired spikes in advance. So, it is stably and widely applicable to practical problems.

引用

页码：56 / 65

页数：10

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