接近爆炸性同步决定了神经和经济危机中的网络崩和恢复轨迹
UnCheol Lee1,2, Hyoungkyu Kim1, Minkyung Kim1
1Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109.
概括
像大脑和接近爆炸同步 (ES) 的市场这样的复杂系统是不稳定的. 一个新的物理框架预测了系统崩和恢复,这对于建立弹性网络至关重要.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络科学 网络科学
- 统计物理 统计物理
背景情况:
- 复杂的系统,包括大脑和金融市场,往往在临界附近运行,以实现最佳功能.
- 扰动可以导致快速偏差和缓慢从关键性恢复,特别是在接近第一阶段过渡的系统中.
- 现有的方法无法经验性地确定一个系统的接近第一阶转变,限制了崩和恢复动态的预测.
研究的目的:
- 开发一个基于物理的框架来估计网络与爆炸性同步 (ES) 之间的距离.
- 为了证明这种近距离测量可以预测扰动下的关键动态,崩和恢复模式.
- 通过计算模型和现实世界的实证数据验证框架.
主要方法:
- 利用修改后的斯图尔特-兰多振荡器网络来建模接近临界值的系统动态.
- 引入了一种新的基于物理的框架来量化接近第一阶段过渡的近距离 (爆炸性同步).
- 在麻醉期间使用人脑电图 (EEG) 数据和2008年危机期间全球股市指数的验证结果.
主要成果:
- 基于接近ES而出现不同的关键动态,影响系统稳定性.
- 该ES近距离测量准确地预测了网络崩和干扰后恢复的时间模式.
- 来自大脑活动和金融市场的经验数据证实了快速崩和缓慢恢复的计算预测.
结论:
- 开发的框架提供了一种方法来经验性地评估网络接近爆炸性同步的程度.
- 这种测量可以预测关键的过渡模式,从而能够预测系统崩和恢复.
- 研究结果为设计更有弹性的复杂系统提供了洞察力,特别是在神经科学和金融领域.
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