用物理电路模拟混沌系统，仅通过调节电压实现高效学习。
Learning Chaotic Dynamics with Neuromorphic Network Dynamics
- 用含忆阻元件的电路构建神经形态网络，以物理动力学实现计算。
- 输入电压使忆阻器动态覆盖全范围时，预测混沌时间序列效果最佳。
- 适合对硬件加速、类脑计算感兴趣的工程师与研究员。
本研究探讨了如何利用自身为动力系统的神经形态网络来学习和建模动力系统。所用神经形态网络基于包含忆阻元件的复杂电路，该电路对输入电信号产生类神经-突触的非线性响应。为探究如何利用底层系统的物理特性进行计算，该网络在储备池计算框架下被仿真并评估，用于多变量混沌时间序列的自主预测。通过仅调节输入电极和电压，发现当输入电压使忆阻组件内部动力学探索其模型的整个动态范围时，可获得最优的非线性动力学响应。增加输入电极覆盖范围有助于抑制不利于学习的其他非线性响应。这些结果为仅通过外部控制参数优化物理神经形态器件以学习复杂动力系统提供了重要启示。
原文摘要 · Abstract (English)
This study investigates how dynamical systems may be learned and modelled with a neuromorphic network which is itself a dynamical system. The neuromorphic network used in this study is based on a complex electrical circuit comprised of memristive elements that produce neuro-synaptic nonlinear responses to input electrical signals. To determine how computation may be performed using the physics of the underlying system, the neuromorphic network was simulated and evaluated on autonomous prediction of a multivariate chaotic time series, implemented with a reservoir computing framework. Through manipulating only input electrodes and voltages, optimal nonlinear dynamical responses were found when input voltages maximise the number of memristive components whose internal dynamics explore the entire dynamical range of the memristor model. Increasing the network coverage with the input electrodes was found to suppress other nonlinear responses that are less conducive to learning. These results provide valuable insights into how a physical neuromorphic network device can be feasibly optimised for learning complex dynamical systems using only external control parameters.
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