Memristive reservoirs draw inspiration from a novel class of neuromorphic hardware known as nanowire networks. These systems display emergent brain-like dynamics, with optimal performance demonstrated at dynamical phase transitions. In these networks, a limited number of electrodes are available to modulate system dynamics, in contrast to the global controllability offered by neuromorphic hardware through random access memories. We demonstrate that the learn-to-learn framework can effectively address this challenge in the context of optimization. Using the framework, we successfully identify the optimal hyperparameters for the reservoir. This finding aligns with previous research, which suggests that the optimal performance of a memristive reservoir occurs at the `edge of formation' of a conductive pathway. Furthermore, our results show that these systems can mimic membrane potential behavior observed in spiking neurons, and may serve as an interface between spike-based and continuous processes.

翻译：忆阻储层受一类新型神经形态硬件——纳米线网络的启发。这些系统展现出类似大脑的涌现动力学特性，并在动态相变点处达到最优性能。在这类网络中，只有有限的电极可用于调节系统动力学，这与神经形态硬件通过随机存取存储器实现的全局可控性形成对比。我们证明，学习-学习框架能有效应对优化中的这一挑战。利用该框架，我们成功识别出储层的最优超参数。这一发现与先前研究一致，表明忆阻储层的最优性能出现在导电通路的“形成边缘”。此外，我们的结果显示，这些系统能够模拟脉冲神经元中观察到的膜电位行为，并可能作为基于脉冲和连续过程之间的接口。