对于两个memristor相互连接的神经元的霍普菲尔德神经网络,平面共存行为
Fangyuan Li1, Wangsheng Qin2, Minqi Xi2
1School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, PR China; School of Electronic Information, Nanjing Vocational College of Information Technology, Nanjing, 210023, PR China.
概括
神经网络中的memristors可以充当自我和相互连接的突触权重,创造复杂的行为. 这项研究表明,在一个两个memristor的霍普菲尔德神经网络中,新的初始状态控制平面共存.
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.
- 材料科学 材料科学 材料科学
背景情况:
- 记忆器是神经形态工程中的关键组件,模仿突触可塑性.
- 现有的模型通常将memristor限制在特定的突触作用上.
- 大脑启发的计算需要先进的memristor功能来处理复杂的动态.
研究的目的:
- 提出一种新的两个memristor相互连接的神经网络 (TMIN-HNN) 模型.
- 为了调查独特的动态行为,包括平面共存,由双重memristor角色启用.
- 为了证明这些复杂行为的初始状态调节.
主要方法:
- 对平衡点稳定性的理论分析.
- 数字模拟用于研究分叉和平面共存.
- 使用STM32硬件板进行实验验证.
主要成果:
- TMIN-HNN 呈现出复杂的分叉现象.
- 确定了新的初始状态调节平面共存行为.
- 协同存在的吸引器可以通过改变初始的memristor状态来动态切换.
结论:
- 拟议的TMIN-HNN模型展示了复杂动态的先进的memristor功能.
- 最初的状态提供了一个强大的机制来控制网络行为和吸引器位置.
- 实验验证证证实了理论发现,为实际应用铺平了道路.
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