一个FHN-HR神经网络与一个新的局部活跃memristor及其DSP实现相结合
IEEE transactions on cybernetics
|October 9, 2024
概括
这项研究引入了一种新型的memristor模型,可以将FitzHugh-Nagumo和Hindmarsh-Rose神经元结合起来. 合系统表现出复杂的动态和状态转换,在安全通信中具有潜在的应用.
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:
- 结合不同的神经元模型对于理解复杂的神经网络至关重要.
- 记忆器模型为模拟神经动态提供了独特的特性.
研究的目的:
- 设计和分析一个新的局部活性memristor (LAM) 模型.
- 使用LAM模型将FitzHugh-Nagumo (FHN) 和Hindmarsh-Rose (HR) 神经元结合起来.
- 研究结合FHN-HR神经元网络的动态特征和潜在应用.
主要方法:
- 对LAM模型的详细描述.
- 结合FHN-HR神经元网络的构建.
- 分析平衡点,相位图,时间序列,分叉图和利亚普诺夫指数谱 (LEs).
- 评估光谱 (SE) 复杂性和序列随机性.
- 对振幅和偏移调制的几何控制的应用.
- 数字信号处理 (DSP) 实现的可行性.
主要成果:
- 结合的FHN-HR模型表现出复杂的动态,包括混乱和周期性吸引子.
- 观察到多种类型的吸引子共存和状态过渡现象.
- 几何控制有效调节吸引器和神经元发射信号.
- DSP的实施证实了FHN-HR模型的数字电路可行性.
结论:
- 新的LAM模型成功地将FHN和HR神经元结合在一起,揭示了复杂的动态行为.
- 结合系统显示了安全通信和加密应用的潜力.
- 这项研究验证了这种复杂的神经模型的数字电路可行性.
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