基于键阈值的脉冲神经网络

2017 Second Russia and Pacific Conference on Computer Technology and Applications (RPC) Pub Date : 2017-09-01 DOI:10.1109/RPC.2017.8168069

A. Gavrilov, Alexandr A. Maliavko, A. Yakimenko

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引用次数: 2

摘要

本文提出了一种新的基于键阈值的峰值神经网络模型。该神经网络由准神经元组成，用于识别在时间(峰值序列)或空间(突触)上分布的任何键峰值。每个神经元的目标是识别存储在其记忆中的键(峰值模板)，并在成功检测到时通过输出峰值做出反应。提出了该模型的软件实现方案。讨论了学习、实现和使用该模型的可能方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Key-Threshold based spiking neural network

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.

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来源期刊

2017 Second Russia and Pacific Conference on Computer Technology and Applications (RPC)

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