简单的循环记忆神经网络具有共存的吸引器和大规模的振幅控制
1School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China.
Chaos (Woodbury, N.Y.)
|July 27, 2023
概括
有循环连接的记忆神经网络 (MNN) 呈现出复杂的动态,包括共存的吸引器和振幅控制. 电路实现验证了这些发现对于先进的生物设备.
科学领域:
- 电子 电子 电子 电子 电子 电子 电子
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 记忆器提供独特的记忆和非挥发性特性,使它们适合人工神经突触.
- 现有的记忆神经网络 (MNN) 可以进一步开发为更复杂的动态.
研究的目的:
- 用通用memristors作为突触来构建简单的循环连接和复杂的动态的MNNs.
- 研究记忆性突触位置对网络动态的影响.
- 在MNN中探索丰富的共存吸引子和依赖参数的振幅控制.
主要方法:
- 设计了三个MNN具有相同的循环合,但不同的记忆突触位置.
- 分析复杂的动态,包括混乱,分叉和共存的吸引力,使用数值模拟 (分叉图,相位图).
- 研究的平衡点及其稳定性.
- 实现了基于微控制器的硬件电路,以验证数值和实验结果.
主要成果:
- 拟议的MNN表现出复杂的动态,包括混乱和周期翻倍的分叉.
- 观察到大量的共存吸引子和大规模的依赖参数的振幅控制.
- 该研究确定了平衡点的数量和稳定性.
- 硬件电路实现证实了网络的行为,并验证了数值发现.
结论:
- 开发的MNN展示了丰富的复杂动态和控制能力,超过了一些现有的模型.
- 记忆器集成到简单的循环网络中,可以实现复杂的生物功能.
- 硬件验证证实了拟议的MNN在人工突触应用中的实际可行性和准确性.
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