首页> 外国专利> ARTIFICIAL NEURAL NETWORKS HAVING COMPETITIVE REWARD MODULATED SPIKE TIME DEPENDENT PLASTICITY AND METHODS OF TRAINING THE SAME

ARTIFICIAL NEURAL NETWORKS HAVING COMPETITIVE REWARD MODULATED SPIKE TIME DEPENDENT PLASTICITY AND METHODS OF TRAINING THE SAME

机译:具有竞争性奖励调制的尖峰时间相关可塑性的人工神经网络及其训练方法

摘要

A method of training an artificial neural network having a series of layers and at least one weight matrix encoding connection weights between neurons in successive layers. The method includes receiving, at an input layer of the series of layers, at least one input, generating, at an output layer of the series of layers, at least one output based on the at least one input, generating a reward based on a comparison of between the at least one output and a desired output, and modifying the connection weights based on the reward. Modifying the connection weights includes maintaining a sum of synaptic input weights to each neuron to be substantially constant and maintaining a sum of synaptic output weights from each neuron to be substantially constant.
机译:一种训练人工神经网络的方法,该人工神经网络具有一系列层和至少一个权重矩阵,该权重矩阵对连续层中神经元之间的连接权重进行编码。所述方法包括在所述一系列层的输入层处接收至少一个输入,在所述一系列层的输出层处基于所述至少一个输入产生至少一个输出,基于对一个所述层产生奖励。至少一个输出和期望输出之间的比较,以及基于奖励来修改连接权重。修改连接权重包括保持对每个神经元的突触输入权重之和基本恒定,以及保持对每个神经元的突触输出权重之和基本恒定。

著录项

  • 公开/公告号US2020133273A1

    专利类型

  • 公开/公告日2020-04-30

    原文格式PDF

  • 申请/专利权人 HRL LABORATORIES LLC;

    申请/专利号US201916661637

  • 申请日2019-10-23

  • 分类号G05D1;G06N3/04;G06N3/08;G05D1/02;G05D1/10;

  • 国家 US

  • 入库时间 2022-08-21 11:21:40

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