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The verification's criterion of learning algorithm

机译:学习算法的验证准则

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摘要

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.
机译:如今,对于脉冲神经网络而言,最需要构建有效的实时学习算法。学习算法应在网络正常工作时收集来自网络输入的数据并将其转换为PNN存储器。在具有一定数量的经常性连接(如Hebbian电池组件(HCA)或synfire链(SFC))的PNN学习期间,在后台保持异步状态是一个很大的挑战。本文提出了可用于在相对较短的仿真时间内拒绝或接受所研究的学习算法的方法,并为我们的研究方向提供了建议。

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