雷米:在内在路径规划过程中重建情节性记忆
Zhaoze Wang1, Genela Morris2,3, Dori Derdikman4
1Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
bioRxiv : the preprint server for biology
|July 9, 2025
概括
动物使用网格细胞和位置细胞进行空间导航和记忆. 这项研究提出,这些系统通过允许从感官线索回忆目标位置来支持规划,从而使路径预测成为可能.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 认知科学 认知科学
背景情况:
- 介质内耳皮层 (MEC) 中的网格细胞形成三角形的空间代码.
- 海马 (HC) 中的位置细胞编码特定的位置和环境背景.
- 这两种空间表征的并行存在及其功能相互作用仍然不完全理解.
研究的目的:
- 提出一个理论框架,在这个框架中,网格和位置单元合作,以支持认知规划.
- 通过感官和动机线索,调查位置细胞如何回忆目标位置,通过网格细胞表示方便计划.
- 在神经网络模型中探索基础路径集成和路径预测的计算机制.
主要方法:
- 开发一个单层循环神经网络 (RNN) 模型.
- 同时建模海马 - 中介内皮层 (HC-MEC) 循环和规划子网络.
- 扩展现有的基于电网电池的规划理论框架.
主要成果:
- 证明当地过渡规则可以实现远距离路径预测.
- 证据表明,规划网络可以顺序更新网格单元状态以达到目标.
- 观察到中间网格活动可以触发位置细胞模式的完成,重建计划的路径.
结论:
- 拟议的模型将HC-MEC功能与规划能力相结合.
- 循环机制促进基于网格单元的规划,目标回忆由位置系统支持.
- 该框架产生可测试的预测关于空间规划和记忆的神经基础.
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