神经元同步在时间变化的高阶网络中
Md Sayeed Anwar1, Dibakar Ghosh1
1Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India.
Chaos (Woodbury, N.Y.)
|July 6, 2023
概括
神经网络中的时间上层交互,以简化复合体的形式建模,比对对交互更有效地促进神经元同步. 这种同步是由群体交互密度增强,甚至可以发生在时间变化的连接.
科学领域:
- 计算神经科学是一种神经科学.
- 网络科学 网络科学
- 复杂的系统复杂的系统.
背景情况:
- 了解大脑的集体动态,特别是神经元同步,至关重要.
- 时间和更高阶相互作用对这些动态的影响仍然是一个感兴趣的领域.
研究的目的:
- 研究通过简化复合体建模的时间上层相互作用如何影响神经元同步.
- 为了比较时间高阶网络中的同步与时间双向和静态多体相互作用.
主要方法:
- 通过使用时间上层网络 (简单复合体) 建模具有间隙结相互作用的神经元组合.
- 采用主稳定函数方法来分析同步解决方案的局部稳定性.
- 进行数值模拟,观察神经元完全同步的出现.
主要成果:
- 与时间对联或静态多体相互作用相比,在时间高阶网络中,同步的关键突触强度较低.
- 神经元同步只能从更高阶的,时间变化的相互作用中出现.
- 在时间上层结构中增强的同步与群体相互作用的密度相关.
结论:
- 时间上层相互作用显著促进神经元同步.
- 群体互动的密度在加强这些网络内的同步方面发挥着关键作用.
- 来自主稳定性函数的分析条件与数值发现一致,验证了模型.
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