流量控制的记忆突触和约瑟夫森结合特性在修改的莫里斯-莱卡尔神经元动态中的相互作用
Mohanasubha Ramasamy1,2, S Senthamizh Selvi3, Anitha Karthikeyan4
1Centre for Nonlinear and Complex Networks, SRM Institute of Science and Technology Ramapuram, Chennai, Tamil Nadu 600089 India.
Cognitive neurodynamics
|August 4, 2025
概括
这项研究考察了磁流和约瑟夫森结合效应对修改的莫里斯·莱卡尔神经元网络. 随机连接的网络通过较低的合实现同步,与需要更高强度的常规网络不同.
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:
- 修改后的莫里斯·莱卡尔 (mML) 模型是研究神经元动态的关键工具.
- 了解磁流和约瑟夫森结 (JJ) 属性的影响对于复杂的神经网络行为至关重要.
研究的目的:
- 为了研究磁流和约瑟夫森结合特性对mML神经元网络的影响.
- 在正规,小世界和随机网络拓中分析同步现象.
主要方法:
- 单个mML模型的分支分析和利亚普诺夫光谱计算.
- 模拟合的mML神经元网络,具有不同的重新连接概率 (0,0.5,1).
- 空间时间和复发图的分析,以评估集体行为.
主要成果:
- 单个mML模型表现出基于流量和JJ参数的周期性和超混沌动态.
- 与普通网络相比,随机网络需要较低的合强度来实现同步.
- 正规网络需要更高的合强度来实现连贯的神经元行为.
结论:
- 网络拓显著影响着mML神经元的同步动态.
- 磁流和JJ特性在调节神经网络行为的过程中起着至关重要的作用.
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