通过空间扩散的输入信号控制人类连接体
Richard Betzel1,2,3,4,5, Maria Grazia Puxeddu6, Caio Seguin6
1Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA. rbetzel@umn.edu.
Communications biology
|March 1, 2026
概括
研究人员开发了一种新的大脑控制模型,该模型使用空间扩展的输入,显著减少大脑状态转换所需的能量,并且需要更少的输入.
科学领域:
- 神经科学是一个神经科学.
- 网络科学 网络科学
- 计算生物学 计算生物学
背景情况:
- 人类大脑表现出持续的动态活动,在各种大脑状态之间进行过渡.
- 网络控制理论为分析这些状态转换的能源成本提供了一个框架.
- 传统模型假设独立的节点输入,忽视大脑的空间连续性和有限的刺激特异性.
研究的目的:
- 调整网络控制模型以纳入空间扩展的输入.
- 调查现实的输入策略如何影响大脑状态转换所需的能量.
- 确定有效的控制策略及其神经生物学相关性.
主要方法:
- 调整了网络控制模型,以包括影响随距离呈指数递减的输入.
- 分析了空间扩展输入对状态转换的能源需求的影响.
- 确定了接近最佳的控制策略,并绘制了输入站点密度.
主要成果:
- 空间扩展的输入大大降低了大脑状态转换所需的能量.
- 接近最佳的控制策略显著减少所需输入的数量 (高达两倍).
- 最佳输入位密度的地图与独立的功能,代谢,遗传和神经化学地图保持一致.
结论:
- 整合空间扩展的输入为大脑控制提供了一个更现实的,更节能的框架.
- 这种方法利用大脑连接和活动的空间依赖.
- 这些发现为理解和控制大脑动态提供了一种基于神经生物学的方法.
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