状态空间动态伊辛模型揭示了在稀有活跃不平衡神经元动态中依赖任务的流
Ken Ishihara1,2, Hideaki Shimazaki3,4
1Graduate School of Life Science, Hokkaido University, Sapporo, Japan. ishihara.ken.n7@elms.hokudai.ac.jp.
Nature communications
|December 9, 2025
概括
我们开发了一个新的模型来测量神经元活动中的时间不对称性,揭示了大脑组织如何与计算有关. 性能更高的小鼠在任务中表现出更有效的神经通信.
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.
- 统计物理 统计物理
背景情况:
- 神经元组合活动表现出不平衡的特征,这对于维持组织至关重要.
- 在尖端神经活动中评估时间不对称性是具有挑战性的,特别是与非静止动态.
研究的目的:
- 为分析非静止和非平衡神经动态开发一种新的状态空间动态Ising模型.
- 估计神经元活动中的时间变化的流和因果合.
- 将神经计算的热力学原理与行为表现联系起来.
主要方法:
- 开发了一种状态空间动态伊辛模型,其中包含了平均场方法来估计流量.
- 将模型应用于小鼠视觉皮层数据,分析神经元发射率和合强度.
- 在不同行为状态期间量化时间变化的流和因果合.
主要成果:
- 在任务参与期间,在小鼠视觉皮层的因果合中发现了更大的可变性.
- 观察到神经元活动减少和任务执行期间稀疏性增加.
- 在任务参与期间,在表现较高的小鼠中发现了与合相关的流量增加.
结论:
- 状态空间动态Ising模型有效地捕捉了非静止神经系统中的不对称因果动态.
- 每个峰值的流量增加与更高效的神经计算和更好的行为表现相关.
- 这项工作为理解神经计算及其与行为的联系提供了一个热力学框架.
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