神经网络模拟两个输入的短期记忆,具有不同的共同点
Eric Raman1, Larry Shupe2, Ryan Eaton2
1Department of Medicine, University of Washington Medical School, Seattle, WA 98195.
bioRxiv : the preprint server for biology
|November 28, 2024
概括
神经网络模拟灵长类大脑的短期记忆. 具有样本和保留 (SAH) 功能的动态神经网络为具有共同组件的感官信号提供了对联想记忆的洞察.
科学领域:
- 计算神经科学是一种计算神经科学.
- 认知神经科学是一种认知神经科学.
- 神经网络建模神经网络建模
背景情况:
- 灵长类大脑的神经活动和连接性基本行为是复杂的.
- 子的短期记忆可能涉及持续的神经活动.
- 有样本和保留 (SAH) 函数的动态神经网络模拟单变量短期记忆任务.
研究的目的:
- 扩展SAH网络模型以计算两个连续变量输入的内存.
- 调查关联短期记忆的计算机制,用于常用模式组件的感觉信号.
- 检查SAH网络中的吸引状态及其决定因素.
主要方法:
- 使用具有SAH功能的动态神经网络.
- 训练有素的网络计算两个连续变量输入的SAH,具有不同的常态信号.
- 在延迟期后分析隐藏单元活动和吸引力状态.
主要成果:
- 网络成功计算了两个输入的SAH,提供了对关联记忆的洞察力.
- 训练有素网络中的隐藏单元活动在记忆任务中模仿了灵长类大脑皮层神经元活动.
- 吸引器状态是由隐藏单元的输入-输出函数决定的,而不是网络架构.
结论:
- SAH网络为关联式短期记忆提供了一个可行的模型,特别是对于具有共享组件的感觉信号.
- 这些发现表明,单个神经元 (隐藏单元) 的特性在记忆形成和回忆中起着至关重要的作用.
- 网络架构对吸引器状态的确定不那么重要,而其组件的内在特性更重要.
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