间歇性集群同步在一个单向环的爆发神经元爆发的神经元
R Sree Ardhanareeswaran1, S Sudharsan2, M Senthilvelan1
1Bharathidasan University, Department of Nonlinear Dynamics, Tiruchirappalli 620024, Tamil Nadu, India.
Physical review. E
|February 20, 2025
概括
神经网络中的极端事件来自间歇性集群同步,这是阶段同步之前的新机制. 这一发现为复杂系统中新出现的现象提供了洞察力.
科学领域:
- 神经科学是一个神经科学.
- 复杂的系统复杂的系统.
- 非线性动力学是一种非线性动力学.
背景情况:
- 欣德马什-罗斯爆发神经元是神经元电活动的模型.
- 合强度和初始条件显著影响网络动态.
- 在各种复杂系统中观察到极端事件,其特点是类似于龙王的分布.
研究的目的:
- 为了确定一个新的机制,极端事件的出现在一个网络的印度沼泽-爆神经元.
- 调查间歇性集群同步在产生这些极端事件中的作用.
- 分析出现的极端事件的统计性质.
主要方法:
- 模拟Hindmarsh-Rose的单向环网络,通过化学突触激发神经元.
- 分析不同合强度的相锁定和相同步过渡.
- 调查间歇性集群同步作为极端事件的前体.
- 使用概率-概率和量子力-量子力图谱对集体可观测的统计分析.
主要成果:
- 具有龙王类分布的极端事件通过间歇性集群同步出现.
- 间歇性集群同步发生在特定的合强度区域,在全相同步之前.
- 局部最大值的分布遵循长尾非高斯分布,事件间隔遵循韦布尔分布.
- 间歇阶段同步的频率随着初始条件集群数量的增加而减少.
结论:
- 间歇性集群同步是神经网络中出现的极端事件的新型前体.
- 这些发现对于化学和扩散合都是可靠的.
- 极端事件的统计性质通过非高斯分布和韦布尔分布得到了很好的描述.
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