.png){kind=icon}
返回
A sequential learning algorithm for a spiking neural classifier
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2015.06.062.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a biologically inspired, sequential learning spiking neural classifier (SLSNC) for pattern classification problems. It consists of a two layered neural network and a separate decision block which estimates the predicted class label. Inspired by observations in the neuroscience literature, the input layer employs a new neuron model which converts real valued stimuli into spikes with varying amplitudes and firing times. The intermediate layer neurons are modeled as integrate-and-fire spiking neurons. The decision block identifies that intermediate neuron which fires first and returns the class label associated with that neuron as the predicted class label. The sequential learning algorithm for the spiking neural network automatically determines the network structure from the training samples and adapts its synaptic weights by long term potentiation and long term depression. Performance of SLSNC has been evaluated using a number of benchmark classification problems and the results have been compared with other well-known spiking neural network classifiers in the literature as well as with the standard support vector machine (SVM) with a Gaussian kernel and the fast learning Extreme Learning Machine (ELM) classifiers. The results clearly indicate that the described spiking neural network produces similar or better generalization performance with a smaller network. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Spiking neural network
Sequential learning
Pattern classification
2-Dimensional coding
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
Productivity enhancement of solar still by PCM and Nanoparticles miscellaneous basin absorbing materials.png)
Desalination
OAAI
DesalinationIF0
OAAI分享收藏Fast and adaptive network of spiking neurons for multi-view visual pattern recognitionNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment在EEG数据上训练的NeuCube尖峰神经网络模型中的连通性分析，以了解大脑的功能变化: 鸦片依赖治疗的案例研究NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data poolingNeuroImageOAAI
NeuroImageIF0
OAAI分享收藏分享收藏
查看更多内容