一种先进的方法,用于离散分数级生物物理神经网络模型的电反应及其动态反应
Yu-Ming Chu1, Taher Alzahrani2, Saima Rashid3,4
1School of Science, Hunan City University, Yiyang, 413000, People's Republic of China.
Scientific reports
|October 24, 2023
概括
这项研究引入了分数级神经细胞模型,以探索复杂的神经动态. 分数计算揭示了神经元连接和记忆如何影响尖端模式和网络同步.
科学领域:
- 计算神经科学是一种神经科学.
- 分数微积分的计算.
- 神经网络动态 神经网络动态
背景情况:
- 神经元活动表现出多样化的尖端爆发模式.
- 了解这些复杂的动态对于神经系统机制至关重要.
- 现有的模型可能无法完全捕捉到记忆和遗传特征的影响.
研究的目的:
- 通过使用离散的分数顺序神经细胞模型,研究复杂相互作用对神经元发射的影响.
- 分析神经网络中的尖端爆发变化和同步现象.
- 探索在神经信息处理中分数顺序动态的作用.
主要方法:
- 开发了一个离散的分数顺序神经细胞框架,使用卡普托类型的分数差运算符.
- 研究了二维和三维的莫里斯-莱卡尔神经元模型,使用分数顺序.
- 采用稳定性和分叉分析,以及用于同步研究的Erdös-Rényi网络建设.
主要成果:
- 分数顺序模型有效地捕捉了复杂的尖端爆发行为和振荡.
- 分数衍生品影响波动的规律性,增强延迟,并影响同步.
- 网络连接和系统内存是对神经活动分数顺序影响的关键决定因素.
结论:
- 分数式微积分为建模复杂的神经元动态提供了一个强大的工具.
- 该模型展示了分数属性如何影响神经网络同步和信息处理.
- 研究结果强调了在神经系统行为中考虑记忆和连接的重要性.
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