在基于地图的神经元模型中,危机分支附近的噪音引起的爆发
Irina Bashkirtseva1, Lev Ryashko1, Miguel A Prado-Reynoso2
1Ural Federal University, Institute of Natural Sciences and Mathematics, 620000, Lenina, 51, Ekaterinburg, Russia.
Physical review. E
|August 19, 2025
概括
这项研究揭示了神经元模型中的突发振荡如何在两叉附近出现. 噪音可以令人惊地诱导爆发和其他复杂的动态,提供对神经网络行为的洞察力.
科学领域:
- 非线性动力学是一种非线性动力学.
- 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:
- 爆发振荡是神经元模型,电路和激光器中看到的复杂动态.
- 了解破裂的启动机制在非线性动态中仍然是一个挑战.
研究的目的:
- 在危机分叉附近的奇亚尔沃神经元模型中调查爆发的出现.
- 分析确定性和随机性影响对爆发动态的影响.
主要方法:
- 基于Chialvo地图的神经元模型的分析.
- 探索危机分叉附近的决定性动态.
- 应用随机影响和灵敏度分析.
主要成果:
- 确定性爆发突然出现,有权力规律的爆发间隔和混乱的瞬态.
- 随机影响引发了诸如噪声引起的爆发和连贯共振等现象.
- 随机灵敏度分析准确地预测噪音诱导的过渡值.
结论:
- 随机波动与分叉结构相互作用,产生复杂的破裂模式.
- 研究结果提供了有关神经网络的爆发机制的见解.
- 该研究强调了复杂系统中噪音和决定性动态之间的相互作用.
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