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在子神经网络中远近记忆电磁感应的动态行为

Qiang Lai¹, Yudi Xu¹

¹School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 3300113, People's Republic of China.

Chaos (Woodbury, N.Y.)

|August 20, 2024

概括

这项研究介绍了一种新的神经网络模型,探索电磁感应.

科学领域:

神经科学是一个神经科学.
复杂的系统复杂的系统.
电气工程电气工程

背景情况:

神经网络对于理解大脑功能至关重要.
电磁感应在神经动力学中的作用尚未完全理解.
现有的模型缺乏对局部感应效应的探索.

研究的目的:

提出一种新的神经网络模型,采用电磁感应.
研究近距离电磁感应对神经网络动态的影响.
探索网络动态和记忆现象之间的关系.

主要方法:

开发一个具有直接和间接连接的特殊子神经网络.
使用高压触角记忆器构建近感应网络 (NINN) 和远感应网络.
吸引力结构,混乱参数和共存动态的分析.
用于验证电磁感应效应的电路实现.

主要成果:

NINN展示了更复杂的吸引力和更广泛的混乱参数.
观察到异质的共存吸引力 (极限周期和混乱).
在不改变混乱特征的情况下控制振荡幅度.
与远方感应相比,近距离感应对与记忆相关的动态有更大的影响.

结论:

接近电磁感应显著影响神经网络动态和吸引器复杂性.
拟议的模型提供了对记忆清晰度和遗忘机制的见解.
该研究通过实践电路实现来验证研究结果.

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