具有短暂轨迹的循环神经网络解释了工作记忆编码机制
Chenghao Liu1, Shuncheng Jia1,2, Hongxing Liu1,2
1Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Communications biology
|January 28, 2025
概括
本研究介绍了基于短暂轨迹的循环神经网络 (TRNN),可以更好地模拟工作记忆 (WM) 动态. TRNN在记忆任务中表现出卓越的性能,支持WM编码的短暂活动理论.
科学领域:
- 计算神经科学是一种计算神经科学.
- 认知建模认知建模
- 神经网络研究神经网络研究
背景情况:
- 工作记忆 (WM) 编码的神经机制,特别是持久与短暂的活动,仍然是争论的主题.
- 现有的循环神经网络 (RNN) 模型通常只模拟一个活动模式,缺乏对记忆能力的直接比较.
研究的目的:
- 开发和比较基于过渡轨迹的RNN (TRNN) 与用于模拟WM的传统RNN.
- 提供支持WM编码的短暂活动理论的计算证据.
主要方法:
- 构建具有生物可信特征的TRNNs:自我抑制,稀疏连接性和层次拓.
- 在模拟工作记忆任务上,TRNN与香草RNN的比较.
- 分析神经活动模式和任务执行.
主要成果:
- TRNN表现出类似于动物研究中的实验记录的活动模式.
- 在处理可变编码时间方面,TRNN表现出更强的多功能性.
- 在延迟选择和空间记忆强化学习任务中,TRNN的表现优于香草RNN.
结论:
- 这些发现支持过渡性活动理论来解释工作记忆机制.
- TRNN为研究WM动态和认知功能提供了一个有前途的建模框架.
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