基于EMR的记忆力Chaivlo神经元的发射动态和合同步,使用同等能量方法
Bin Liu1, Muning Li2, Zhijun Li1
1School of Computer Science and School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China.
Chaos (Woodbury, N.Y.)
|November 1, 2024
概括
这项研究介绍了一种新的Chaivlo神经元模型,它结合了电磁辐射 (EMR) 来分析离散神经元发射动态. 研究结果显示,EMR和合强度可以增强神经网络同步.
科学领域:
- 计算神经科学是一种神经科学.
- 神经系统启发的计算
- 记忆系统 记忆系统
背景情况:
- 了解神经信息处理需要分析神经元发射动态和能量特性.
- 与连续模型相比,离散神经元能量分析发展不足.
- 记忆器提供了新的方法来模拟复杂的神经行为.
研究的目的:
- 介绍和分析一款模拟电磁辐射 (EMR) 的新型记忆力Chaivlo神经元模型的发射动力学.
- 为了研究由EMR和注射电流引起的能量特性和发射行为.
- 探索网络中合的Chaivlo神经元的同步能力.
主要方法:
- 开发了一个Chaivlo神经元地图,使用流量控制的memristor来模拟EMR.
- 应用了同等的哈密尔顿能量方法来进行详细的火动力学分析.
- 构建了一个由两个合的记忆神经元组成的Chaivlo网络,并通过实验评估了同步.
主要成果:
- 鉴定了EMR和电流诱导的各种基于能量的发射行为 (峰值,共存,混合模式,混乱爆发).
- 证明EMR和注射电流显著影响神经元发射模式.
- 在Chaivlo网络中使用同步因子和能量差异进行量化同步.
结论:
- 查伊洛神经元模型有效地捕捉了受EMR影响的复杂发射动态.
- 电磁波和合强度都是促进神经网络同步的关键因素.
- 这项研究促进了对离散神经元能量动态和网络同步的理解.
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