分数顺序记忆神经元网络中的同步过渡 Hindmarsh-Rose神经元
Sheida Ansarinasab1, Fahimeh Nazarimehr1, Farnaz Ghassemi1
1Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran.
Journal of theoretical biology
|May 15, 2025
概括
分数级神经元比整数级神经元更快,更突然地同步,特别是在无尺度网络中. 记忆效应和网络结构极大地影响神经元同步动态.
科学领域:
- 计算神经科学是一种神经科学.
- 非线性动力学是一种非线性动力学.
- 复杂的系统复杂的系统.
背景情况:
- 分数顺序神经元模型记忆效应对于理解神经动力学至关重要.
- 神经元同步至关重要,但其过渡动态,特别是在电磁辐射等外部影响下,仍未得到充分研究.
- 记忆性Hindmarsh-Rose神经元模型提供了一个探索复杂神经元行为的平台.
研究的目的:
- 研究分数顺序和整数顺序记忆神经元网络中的同步过渡.
- 为了比较小世界和无规模网络拓中的同步行为.
- 阐明分数顺序动态和网络结构对同步的影响.
主要方法:
- 模拟分数顺序和整数顺序的记忆性Hindmarsh-Rose神经元.
- 构建小世界和无尺度的神经网络.
- 通过不同的合强度和网络参数分析了同步过渡.
主要成果:
- 与整数级神经元相比,分数级神经元在较低的合强度实现了同步.
- 在分数级神经元网络中,同步过渡更为突然,特别是在无尺度拓中.
- 网络拓显著影响了同步动态,无规模网络表现出不同的行为.
结论:
- 分数级神经元固有的记忆效应加快和加敏同步过渡.
- 网络拓,特别是无尺度结构,在调节同步方面发挥着至关重要的作用.
- 这项研究强调了考虑分数动态和网络架构对于现实的神经系统建模的重要性.
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