三层鲁尔科夫神经元网络的完整同步,由电气和化学突触相结合
Penghe Ge1, Libo Cheng2, Hongjun Cao3
1Department of Mathematics, School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, People's Republic of China.
Chaos (Woodbury, N.Y.)
|April 8, 2024
概括
这项研究探讨了三层鲁尔科夫神经元网络中的完整同步. 定期同步分组使完全同步成为可能,与混乱的不同,揭示了网络动态.
科学领域:
- 计算神经科学是一种神经科学.
- 复杂的系统复杂的系统.
背景情况:
- 神经网络模型对于理解大脑功能至关重要.
- 电气和化学突触在神经通信中起着不同的作用.
- 复杂网络中的完整同步是关键现象.
研究的目的:
- 分析混合合的三层鲁尔科夫神经元网络中的完整同步.
- 为非拉普拉斯网络开发和应用主稳定性函数方法.
- 研究同步分流体的条件和动态.
主要方法:
- 主稳定函数 (MSF) 方法适用于非拉普拉斯合.
- 对同步的不变多元体动态的分析.
- 莱普诺夫指数计算以确定同步稳定性.
- 在干扰下模拟网络行为.
主要成果:
- 建立了具有非线性化学合的同步分组的存在条件.
- 在非对称行为之前,观察到过渡的混乱和周期性窗口.
- 周期性同步变频带来了完全的同步;混沌的不.
- 模拟了小扰动下的网络动态.
结论:
- 在这种鲁尔科夫神经元网络模型中,在特定条件下,完全同步是可以实现的.
- 在非拉普拉斯合网络中分析同步时,MSF方法是有效的.
- 同步多重体的性质 (周期性与混乱性) 决定了同步结果.
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