简单的经常性网络是交互式的交互式网络
James S Magnuson1,2,3, Sahil Luthra4
1BCBL, Basque Center on Cognition Brain and Language, Donostia-San Sebastián, Spain. james.magnuson@uconn.edu.
Psychonomic bulletin & review
|November 13, 2024
概括
简单的循环网络 (SRN) 不是前系统,与一些说法相反. 它们的循环结构允许关键的反,影响认知科学学习和处理理论.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 简单循环网络 (SRN) 是认知科学的基础计算模型.
- 30多年来,SRN一直被用来建模学习,开发和处理.
- 关于SRN是否作为前或互动系统的功能,目前仍在进行辩论.
研究的目的:
- 解决关于简单循环网络 (SRN) 是否是前系统的争论.
- 澄清SRN的计算性质及其对认知理论的影响.
- 通过其架构特性来证明SRN的交互性.
主要方法:
- 简单循环网络 (SRN) 网络架构的分析.
- 检查SRN内部的信息流和计算,包括反循环.
- 将SRN架构与前网络的定义进行比较 (环形图).
主要成果:
- 隐藏单位之间的SRN具有反复连接 (循环),将它们分类为循环图.
- 与声称相反,SRN不是前系统.
- 在SRN中,自下而上的输入本质上是通过反与之前的内部计算混合在一起的.
结论:
- 由于它们的反复反循环,SRNs基本上是交互式系统.
- SRN的交互性质对理解认知过程具有重要的理论意义.
- 将SRN重新归类为交互式系统需要重新评估它们在认知建模中的作用.
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