在海马网络中通过诱导偏差和学习的对称性破坏来平衡稳定性和流量
bioRxiv : the preprint server for biology
|February 23, 2026
概括
海马通过混合对称的神经网络制度平衡记忆稳定性和顺序动态. 这种结构是由学习和生物学上可信的可塑性驱动的,可以实现高效的重复和预测.
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.
- 机器学习 机器学习
背景情况:
- 海马对于记忆和顺序处理至关重要,但平衡稳定性和动态的神经机制仍然难以捉摸.
- 了解海马回路如何产生序列动力学,如重播和移动波,是解读记忆功能的关键.
研究的目的:
- 研究反复性神经网络如何实现稳定的记忆表示和动态序列处理.
- 探索对称性破裂在神经网络动态中对于海马体计算的作用.
主要方法:
- 在连续预测任务上训练经常性神经网络.
- 分析训练网络的动态模式和连接配置文件.
- 调查对称性突破生物可信的尖峰时间依赖可塑性 (STDP) 规则的出现.
主要成果:
- 在预测任务上训练的网络汇聚到混合对称的动态模式.
- 这种模式的特点是稳定性的主导对称的复发和弱的反对称组件,用于定向流.
- 从STDP中出现了对称性破坏,创建了一个倾斜的墨西哥帽子连接配置文件.
- 类似CA3的连接性倾向于向这种有益的模式学习,提高效率和性能.
结论:
- 神经网络中的部分对称性破坏提供了一个平衡记忆稳定性和顺序动态的原则.
- 海马的计算可能源于电路结构和任务驱动学习之间的相互作用.
- 这个框架提供了关于神经重复,预测和海马体中波浪的洞察.
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