一个基于循环神经网络的工作记忆模型,使用强化学习
Mengyuan Wang1, Yihong Wang1, Xuying Xu1
1Institute for Cognitive Neurodynamics, Center for Intelligent Computing, School of Mathematics, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237 China.
Cognitive neurodynamics
|November 18, 2024
概括
这项研究使用一个使用强化学习训练的循环神经网络来建模空间工作记忆. 模型 模型的模型
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.
- 机器学习 机器学习
背景情况:
- 前额叶皮层 (PFC) 神经元表现出动态的单个单元活动,但稳定的人口编码工作记忆.
- 在工作记忆中这种PFC活动背后的神经计算机制仍然不清楚.
- 了解神经网络动态对于破译工作记忆过程至关重要.
研究的目的:
- 通过一种新的计算方法探索工作记忆的神经计算机制.
- 使用循环神经网络 (RNN) 模型模拟空间工作记忆任务.
- 研究RNN活动模式与前额叶皮层神经元活动的关系.
主要方法:
- 使用强化学习训练了一种具有决策和基线网络的循环神经网络模型.
- 该模型学习了一个空间工作记忆任务,涉及刺激信息维护.
- 分析单位和人口活动动态,包括时间动态和低维编码.
主要成果:
- RNN模型成功地执行了空间工作记忆任务.
- 模型单元活动显示时间动态和偏好的方向选择性,反映了PFC神经元活动.
- 人口活动稳定地在低维子空间中编码了刺激信息,随着学习而改善.
结论:
- 开发的RNN模型为空间工作记忆任务提供了可行的模拟.
- 该模型提供了关于PFC神经元如何通过人口动态实现稳定的信息表示的见解.
- 这种方法增强了对工作记忆和PFC功能的神经计算的理解.
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