.png){kind=icon}
返回
A pulse-based reinforcement algorithm for learning continuous functions
.png){kind=icon}
PRE
AI
DOI：10.1016/S0925-2312(96)00034-3.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
An algorithm is presented which allows continuous functions to be learned by a neural network using spike-based reinforcement learning, Both the mean and the variance of the weights are changed during training; the latter is accomplished by manipulating the lengths of the spike trains used to represent real-valued quantifies, The method is here applied to the probabilistic RAM (pRAM) model, but it may be adapted for use with any pulse-based stochastic model in which individual weights behave as random variables.
Keyword:
pRAM
continuous-output reinforcement
pulse-coding
stochastic computing
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
暂无机构信息
引用论文
Cross-linkable Polymer Matrix for Enhanced Thermal Stability of Succinonitrile-based Polymer Electrolyte in Lithium Rechargeable Batteries可交联聚合物基质，用于增强锂可充电电池中基于丁二腈的聚合物电解质的热稳定性
OAAI
OAAI分享收藏The origins of higher-order thinking lie in children's spontaneous talk across the pre-school years.png)
Cognition
OAAI
CognitionIF0
OAAI分享收藏
查看更多内容