取样偏差纠正用于精确测量冗余,独特和协同信息的神经测量
Loren Koçillari1,2, Gabriel Matías Lorenz1,3,4, Nicola Marie Engel1
1Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
bioRxiv : the preprint server for biology
|June 19, 2024
概括
神经活动的部分信息分解 (PID) 由有限的采样产生偏差,特别是在协同作用方面. 新的方法纠正了这种偏见,改善了大脑区域 (如听觉皮层) 的信息分析.
科学领域:
- 神经科学是一个神经科学.
- 信息理论 信息理论
- 计算神经科学是一种神经科学.
背景情况:
- 香农信息理论量化了认知变量的神经编码.
- 部分信息分解 (PID) 将信息分解成独特,协同和冗余的组件.
- 神经活动测量中的有限抽样偏差是一个已知的问题,但在PID中基本上没有解决.
研究的目的:
- 调查神经活动的PID中有限的抽样偏差.
- 量化不同PID组件 (独特,协同,冗余) 的偏差.
- 开发和验证PID的偏差校正方法.
主要方法:
- 从代表神经尖端的离散概率来对PID进行模拟.
- 估计分析扩展以了解偏差属性.
- 将偏差校正程序应用于大型神经数据集.
主要成果:
- PID表现出显著的,不均的偏差,协同效应是最受影响的.
- 协同偏差与神经响应复杂性成正方位; 唯一/冗余偏差是线性/亚线性.
- 开发了有效的偏差校正程序,改进了PID估计.
结论:
- 有限的抽样偏差显著影响神经信息的PID,特别是协同作用.
- 新的偏差校正方法提供了更准确的PID估计.
- 对小鼠大脑区域应用了修正后的PID,揭示了不同区域之间协同作用和冗余性的变化.
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