神经和行为合的信息理论测量预测了代表性漂移
Kristine Heiney1,2,3,4, Mónika Józsa1, Michael E Rule1,5
1Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
PLoS computational biology
|February 17, 2026
概括
神经群的调稳定性,称为代表性漂移,与神经元共享信息的数量有关. 更大的信息共享 (冗余) 与随着时间的推移更稳定的神经表征相关.
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.
- 系统神经科学 系统神经科学
背景情况:
- 代表性漂移描述了神经群体调节在几天到几周的逐渐变化.
- 异质性存在于一个群体内的单个神经元的调稳定性.
- 在调稳定性中这种异质性的驱动因素仍然不清楚.
研究的目的:
- 为了研究神经元的调稳定性及其与其他神经元共享的可变性之间的关系.
- 了解神经群体中个人神经元调稳定性的因素.
主要方法:
- 利用了两种已发表的神经数据集,分别来自后壁皮质皮质和视觉皮质.
- 采用部分信息分解来量化双对神经相互作用.
- 分析了个体神经元及其相互作用对刺激或行为编码的贡献.
主要成果:
- 发现神经元的调稳定性与其与人群的平均对联冗余性之间存在正相关性.
- 具有更高调稳定的神经元表现出更强的冗余信息共享与其他神经元.
结论:
- 神经调稳定性与神经元之间共享信息 (冗余) 的程度有关.
- 这种稳定性-冗余性关系可能会增强用于脑机界面等应用的纵向神经解码.
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