分形盆地作为灵活大脑的机制
Erik Bollt1,2, Jeremie Fish3,4, Anil Kumar3,4
1Department of Electrical and Computer Engineering, Clarkson University, 8 Clarkson Ave., Potsdam, NY, 13699, USA. bolltem@clarkson.edu.
Scientific reports
|November 27, 2023
概括
大脑表现出多重稳定性,通过嵌合体状态 (混合同步/不连贯) 在状态之间切换. 这项研究在脑网络模型中识别了这些状态,揭示了快速模式切换的碎形盆地边界.
科学领域:
- 神经科学是一个神经科学.
- 动态系统理论 动态系统理论
- 计算神经科学是一种神经科学.
背景情况:
- 大脑处理各种感官输入的能力依赖于上下文依赖的反应.
- 这种适应性体现了动态系统中的多稳定性,其中大脑在不同的活动模式之间切换.
- 了解这些状态过渡对于理解大脑功能至关重要.
研究的目的:
- 为了研究嵌合体状态,以混合的同步和不连贯为特征,在一个由大脑启发的动态系统中.
- 开发一种有效的方法来识别嵌合体状态并绘制它们的相关盆地结构.
- 阐明神经网络机制,这些机制是同时存在的大脑状态之间的快速模式切换的基础.
主要方法:
- 通过使用弱相互作用和混乱/周期性局部动态的网络构建了一个由大脑启发的动态系统模型.
- 合成时间序列数据在现实的人体解剖大脑网络上生成和分析,该网络来自扩散张力图像.
- 矢量模式状态 (VPS) 被引入作为一种新的指标,用于识别嵌合体状态和表征盆地结构.
主要成果:
- 在大脑网络模型中成功识别了表现出混合同步和不连贯的奇默状态.
- 矢量模式状态 (VPS) 在检测这些状态和绘制底层流域结构时被证明是有效的.
- 分析显示,复杂混合的碎形盆地边界对共存的吸引器,表明立即可达.
结论:
- 这些发现表明,嵌合体状态可以在受大脑启发的网络模型中出现,反映出复杂的大脑动态.
- 开发的VPS方法为分析神经系统中的多稳定性和状态转换提供了有效的工具.
- 碎形盆地边界的存在为大脑在认知或活动模式之间快速切换提供了潜在的神经机制.
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