学习算法的验证准则

Tymoteusz Bilyk
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引用次数: 1

摘要

目前脉冲神经网络最需要的是构建一种有效的实时学习算法。学习算法需要在网络工作时收集网络输入的数据并将其转换为PNN存储器。在学习具有一定数量的循环连接(如Hebbian cell assemblies (HCA)或synfire chains (SFC))的PNN时,在后台保持异步状态是一个很大的挑战。本文提出了在较短的仿真时间内拒绝或接受被检验的学习算法的方法,并对我们的研究方向提出了建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The verification's criterion of learning algorithm
Construction of an effective real time learning algorithm is mostly needed for the pulsed neural network nowadays. The learning algorithm should collect and convert the data from networks inputs into a PNN memory while network's working. Keeping of an asynchronous state in the background is a big challenge during learning PNN with a certain number of recurrent connections like Hebbian cell assemblies (HCA) or synfire chains (SFC). This paper presents methods which are usable for refusing or accepting the examined learning algorithm in the relatively short time of simulation and it gives us advice about direction of our research.
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