伊希基维奇神经元网络的主稳定功能
Raul P Aristides1, Hilda A Cerdeira2
1São Paulo State University (UNESP), Instituto de Física Teórica, Rua Dr. Bento Teobaldo Ferraz 271, Bloco II, Barra Funda, 01140-070 São Paulo, Brazil.
Physical review. E
|May 17, 2024
概括
这项研究分析了复杂的大脑网络中的同步状态,使用Izhikevich神经元和主稳定功能. 结果表明,结合电气和化学合可以导致不稳定的同步状态,影响大脑功能和功能障碍.
科学领域:
- 神经科学是一个神经科学.
- 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:
- 同步对于大脑功能和功能障碍至关重要.
- 复杂的网络被用来建模大脑活动.
- 伊希克维奇神经元是神经元动态的一个常见模型.
研究的目的:
- 分析伊希基维奇神经元网络中同步状态的线性稳定性.
- 应用主稳定函数 (MSF) 和盐化矩阵来计算利亚普诺夫指数.
- 研究电气和化学合对同步的影响.
主要方法:
- 使用主稳定函数 (MSF) 进行稳定性分析.
- 采用盐化矩阵来处理Izhikevich神经元模型中的不连续性.
- 具有电气和化学合的模拟网络 (全球和集群同步).
主要成果:
- 计算了MSF的Lyapunov指数,考虑了伊希克维奇神经元尖端断续.
- 将MSF计算与模拟中的同步错误测量进行了比较.
- 确定了电气和化学合的组合可以创建的吸引力盆地,增加对干扰的敏感性.
结论:
- 盐化矩阵为分析伊希基维奇神经元网络中的同步稳定性提供了强大的方法.
- 网络合类型显著影响同步状态的稳定性.
- 了解同步稳定性是理解大脑功能和功能障碍的关键.
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