产生简单的循环记忆神经网络电路,具有可控制的多滚动吸引器和多变量振幅控制
概括
这项研究介绍了一种新的记忆神经网络电路,能够产生复杂的多滚动混乱吸引器. 该设计为神经形态计算应用提供了一个更简单,更易于实施的替代方案.
科学领域:
- 神经科学是一个神经科学.
- 混沌理论 混沌理论
- 电子工程 电子工程
背景情况:
- 记忆器表现出突触性质,使它们适合神经形态电路,如神经网络.
- 现有的复杂的动态神经网络经常存在高维度和过度连接问题,阻碍了实际实施.
研究的目的:
- 设计一个简单的循环记忆神经网络 (SCMNN) 电路,能够产生复杂的多滚动混乱吸引器.
- 为了克服高维神经网络电路的实施挑战.
主要方法:
- 在一个简单的循环神经网络 (SCNN) 中集成一个接函数memristor.
- 使用外部刺激电流来产生多滚动吸引器.
- 激活memristor等效电路中的开关,以控制卷轴的数量.
主要成果:
- 设计的SCMNN成功地产生了多滚动的混乱吸引器.
- 卷轴数量随着激活开关而扩大,可以通过参数变化来控制.
- 该电路展示了抗干扰能力,并实现了多变量振幅控制.
- 使用微控制器和示波器的实验验证证证了吸引子的产生.
结论:
- 拟议的SCMNN电路为生成复杂的混乱动态提供了一种可行和可实施的方法.
- 该电路能够产生可控制的多滚动吸引器,这为先进的神经形态应用提供了潜力.
- NIST测试验证了电路在工程中的应用价值.
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