当神经活动未能揭示因果贡献时
Kayson Fakhar1, Shrey Dixit1, Fatemeh Hadaeghi1
1Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Germany.
bioRxiv : the preprint server for biology
|June 19, 2023
概括
神经活动模式可能无法可靠地表明一个单元在行为中的因果作用. 新的方法表明,网络转换可以掩盖真正的因果贡献,突出解释神经数据的局限性.
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:
- 神经科学家分析神经活动模式,以了解大脑功能.
- 观察到的神经活动与神经元对行为的因果影响之间的关系尚不清楚.
研究的目的:
- 开发一个框架来评估神经元件因果贡献的时间变化.
- 调查神经活动是否可靠地反映网络内的因果贡献.
主要方法:
- 开发了一个系统的多站点扰动框架.
- 将框架应用于玩具示例和人工神经网络.
主要成果:
- 神经活动模式可能不会准确地反映由于网络转换而导致的因果贡献.
- 证明记录的活动可能对单位的影响产生误导性.
结论:
- 仅从神经活动中推断因果机制具有显著的局限性.
- 拟议的框架有助于通过干扰严格识别因果神经贡献.
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