记忆性鲁尔科夫神经元的简化复合体中的同步
Mahtab Mehrabbeik1, Sajad Jafari1,2, Matjaž Perc3,4,5,6,7
1Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Frontiers in computational neuroscience
|September 18, 2023
概括
这项研究揭示了神经网络中的更高阶相互作用如何使用Rulkov模型影响同步. 整合这些复杂的连接可以更有效地同步网络,甚至可以实现集群同步.
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
- 数学建模的数学建模
背景情况:
- 在复杂网络中,特别是神经元系统中,高阶相互作用至关重要.
- 简化复合体为模拟这些多元素相互作用提供了一个框架.
- 以前的研究表明,这些相互作用在生物系统中的意义.
研究的目的:
- 研究高阶交互对网络同步的影响.
- 分析同步在一个memristive鲁尔科夫模型网络与混合突触.
- 为了比较网络中的同步模式与对对对比较高阶连接.
主要方法:
- 使用主稳定函数来分析同步.
- 模拟具有纯配对混合 (电气和化学) 突触的网络.
- 探索具有双节点电气和多节点化学连接的网络.
- 研究高阶合参数对神经元动态的影响.
主要成果:
- 高阶交互与双向突触相比,在较低的多节点化学合值下同步网络.
- 更高阶合参数的改变会影响同步状态内的神经元动态.
- 增加网络大小通过减少所需的合参数来提高同步性.
- 集群同步在更高的电合强度下观察到,与完全同步不同.
结论:
- 高阶相互作用显著改变并经常增强神经网络中的同步.
- 具有简化的复杂网络的Rulkov模型提供了对复杂系统动态的洞察.
- 网络拓和合类型极大地影响新出现的同步模式.
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