在前运动皮层的选择动态和几何结构
Mikhail Genkin1, Krishna V Shenoy2,3, Chandramouli Chandrasekaran4,5,6,7
1Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Nature
|June 25, 2025
概括
科学家发现大脑中的神经群体编码了动态的认知变量, 这揭示了大脑计算的统一机制.
科学领域:
- 神经科学
- 认知科学
- 计算神经科学
背景情况:
- 神经群体通过协调活动和单个神经元调曲线编码感官信息.
- 由于神经活动的复杂时间动态,这种人口编码原理对动态认知变量 (如决策) 的应用仍然在很大程度上未被探索.
- 内部认知过程在单个试验中表现出独特的时间过程,通常反映在异质神经群体的不规则的尖端模式中.
研究的目的:
- 调查神经群体是否使用类似的几何编码原理来对动态认知变量进行编码,就像对感觉变量一样.
- 探索灵长类大脑中选择形成的神经机制.
- 开发一种新的计算方法,同时推断人口动态和单个神经元调功能.
主要方法:
- 开发了一个新的计算框架,同时推断出人口动态和与人口状态相关的单个神经元调节功能.
- 在决策任务中从灵长类前运动皮层记录的电生理数据中应用该模型.
- 使用先进的统计推断技术来分析神经群体的活动并确定编码原理.
主要成果:
- 证明了灵长类前运动皮层中选择形成的动态.
- 发现神经元群体编码了一个共同的动态变量,
- 显示不同的神经元对这个共同的决策变量产生异质的触发速度.
- 推断人口动态与决定计算的底层吸引力机制一致.
结论:
- 建立了一个统一的神经编码的几何原理,适用于感官和动态认知变量.
- 提供了吸引力网络动态作为大脑决策计算机制的证据.
- 进一步了解神经群体如何代表和处理复杂的认知信息.
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