使用相位稳定框架对振荡器网络动态的洞察力
R Nicks1, R Allen1, S Coombes1
1School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, United Kingdom.
Chaos (Woodbury, N.Y.)
|January 25, 2024
概括
本研究介绍了合非线性振荡器的相位稳定网络方程. 这种先进的方法准确地捕捉了新出现的网络动态和分叉,超过了标准的相位减小技术.
科学领域:
- 非线性动力学是一种非线性动力学.
- 网络科学 网络科学
- 计算神经科学是一种神经科学.
背景情况:
- 结合的非线性振荡器表现出复杂的新兴行为.
- 标准的相位减小方法无法捕捉某些网络动态和分叉.
- 稳定坐标为振荡器动态提供了更全面的描述.
研究的目的:
- 为了将相位稳定框架扩展到任意数量的合相同振荡器.
- 导出相锁状态的稳定性条件,包括同步.
- 为了比较相位稳定方程与更高阶相位减小方程的准确性.
主要方法:
- 为N合振荡器开发相位稳定网络方程.
- 对相锁状态的稳定性条件的分析.
- 使用复杂的金兹堡-兰道方程,与更高阶相减法进行比较.
- 对全球线性合的莫里斯-莱卡神经元模型的应用.
主要成果:
- 相隔稳定网络方程准确地捕捉了相锁状态中的分叉.
- 相比较于更高阶相位减少,相位稳定框架显示出更高的精度.
- 对于莫里斯-莱卡网络的模拟和相位稳定描述之间观察到的定性对应.
- 该方法捕捉了小型和大型网络中第一阶段描述错过的动态.
结论:
- 相位稳定框架提供了比标准相位减速更准确的合振荡器网络描述.
- 这种方法对于分析各种网络大小的复杂动态,包括同步和分叉,是有效的.
- 该研究验证了相异稳坐标对于理解合动态系统中新出现的现象的实用性.
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