时间延迟和连接拓对多延迟惯性神经系统的影响
1Department of Mathematics and Information Science, Henan University of Economics and Law, Zhengzhou, 450046 China.
Cognitive neurodynamics
|November 18, 2024
概括
这项研究揭示了时间延迟和连接重量如何影响多延迟的人工神经网络. 研究人员发现,这些参数显著影响网络动态和稳定性,为网络优化提供了洞察力.
科学领域:
- 复杂的系统复杂的系统.
- 计算神经科学是一种神经科学.
- 网络动态 网络动态
背景情况:
- 现实的网络建模需要考虑多个延迟和连接拓.
- 有惯性合的人工神经模型对于理解复杂的网络行为至关重要.
研究的目的:
- 研究时间延迟和连接权重对具有惯性合的多延迟人工神经模型的影响.
- 分析各种奇点的条件,包括静态,Hopf和叉-Hopf双叉.
- 探索由自我连接重量和合延迟相互作用产生的丰富动态.
主要方法:
- 对超级特征方程的分析,以确定奇点的条件.
- 用非减少顺序技术应用参数扰动.
- 使用自连接重量和合延迟作为调整参数.
主要成果:
- 建立了足够的条件来实现静态,Hopf和叉-Hopf的分叉.
- 观察到丰富的动态,包括共存的吸引力,如非碎的平衡点和周期轨道.
- 自连的重量会影响平衡点,而时间延迟会导致不稳定,限制循环生成.
结论:
- 这项研究提供了一种用于分析多延迟惯性神经系统的新方法,以减少计算.
- 这些发现为控制和优化复杂网络提供了重要的理论指导.
- 了解参数的影响是预测和管理网络行为的关键.
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