Key-Threshold Based Spiking Neural Network

被引：0

作者：

Gavrilov, Andrey V. ^{[1
]}

Maliavko, Alexandr A. ^{[1
]}

Yakimenko, Alexandr A. ^{[1
]}

机构：

[1] Novosibirsk State Tech Univ, Dept Comp Engn, Novosibirsk, Russia

来源：

2017 SECOND RUSSIA AND PACIFIC CONFERENCE ON COMPUTER TECHNOLOGY AND APPLICATIONS (RPC 2017) | 2017年

基金：

俄罗斯基础研究基金会;

关键词：

neural networks; spiking neural networks; machine learning; pattern recognition;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the paper a novel model of Key-Threshold based Spiking Neural Network (KTSNN) is proposed. This neural network consists of quasi-neurons oriented to recognize any key spikes distributed in time (sequence of spikes) or in space (in synapses). Every neuron aims to recognize key (template of spikes) stored in its memory and to react by output spike at successfully detection. Software implementation of this model is suggested. Possible methods of learning, implementation and usage of this model are discussed.

引用

页码：64 / 67

页数：4

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