在最小的维度下神经元组的稳定性,尽管存在动力表示漂移
bioRxiv : the preprint server for biology
|February 12, 2026
概括
研究人员开发了一种监督方法,从神经数据中提取稳定的运动表示. 这种方法解释了神经元组的长期稳定性,并确定了编码地图作为运动皮质漂移的主要来源.
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.
- 动态系统 动态系统
背景情况:
- 运动生成通常使用潜在动态系统进行建模.
- 神经群活动和潜伏变量表现出非静止的关系.
- 潜伏轨迹形成低维的,看似稳定的多元体随着时间的推移.
研究的目的:
- 开发一个框架来提取标记的隐性变量与移动参数的固定关系.
- 为了研究潜伏多元体的维度及其与运动皮质电路的联系.
- 为了解释神经元组的长期稳定性.
主要方法:
- 提出了一个监督的框架来提取标记的潜在变量.
- 将该方法应用于一个二维的伸手任务.
- 分析了编码图 (神经元到潜伏) 和潜伏图 (潜伏到行为) 的稳定性.
主要成果:
- 监督方法在线解码任务中表现优于常用方法.
- 提取的隐性变量捕获了总变异的小百分比 (3-7%),但解释了多重稳定性.
- 编码地图的变化是漂移的主要来源,而潜伏地图多年来一直保持稳定.
结论:
- 编码地图的可塑性,而不是潜伏地图,驱动运动皮层漂移.
- 任务复杂性限制了神经结构在二维任务中,限制了神经多元体的洞察力.
- 未来的研究应该专注于神经计算和行为之间的因果关系.
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