双节律的Hindmarsh-Rose神经元模型中的分叉和共振,具有不同的尖峰
1Shaanxi Normal University, School of Mathematics and Statistics, Xi'an 710119, People's Republic of China.
Physical review. E
|August 1, 2025
概括
这项研究探讨了双节奏的Hindmarsh-Rose模型,揭示了噪音如何影响神经元动态. 它识别了随机分叉和共振,提供了对神经元活动和神经疾病的见解.
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.
背景情况:
- 欣德马什-罗斯模型是神经元中爆发电活动的数学模型.
- 双节律模型表现出具有不同尖端模式的极限周期,这对于理解复杂的神经元行为至关重要.
研究的目的:
- 在随机影响下调查双节律的Hindmarsh-Rose模型中的分叉和共振.
- 分析噪音如何影响系统的动态,包括概率密度和利亚普诺夫指数.
- 探索噪音引起的现象,如随机共振及其对神经元信号处理的影响.
主要方法:
- 两叉分析以确定关键参数值.
- 随机灵敏度分析以量化噪声的影响.
- 计算最大的利亚普诺夫指数以检测动态变化.
- 对变化系数和信号与噪声比率进行分析,以描述共振现象.
主要成果:
- 静态概率密度对噪声水平和系统参数敏感,表明随机现象学分叉.
- 增加的噪音导致随机动态分叉,由最大Liapunov指数的变化证明.
- 确定了反相干共振和相干共振,它们由噪声强度调制.
- 观察到响应波信号的噪声诱导抑制和随机共振.
结论:
- 噪音在塑造双节奏的Hindmarsh-Rose神经元模型的动态方面发挥着重要作用.
- 了解这些随机效应对于精确模拟神经元活动至关重要,特别是在神经疾病背景下.
- 这些发现有助于更深入地了解神经元如何在固有生物噪声的存在下处理信息.
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