Reservoir computing (RC) offers a neuromorphic framework that is particularly effective for processing spatiotemporal signals. Known for its temporal processing prowess, RC significantly lowers training costs compared to conventional recurrent neural networks. A key component in its hardware deployment is the ability to generate dynamic reservoir states. Our research introduces a novel dual-memory RC system, integrating a short-term memory via a WOx-based memristor, capable of achieving 16 distinct states encoded over 4 bits, and a long-term memory component using a TiOx-based memristor within the readout layer. We thoroughly examine both memristor types and leverage the RC system to process temporal data sets. The performance of the proposed RC system is validated through two benchmark tasks: isolated spoken digit recognition with incomplete inputs and Mackey-Glass time series prediction. The system delivered an impressive 98.84% accuracy in digit recognition and sustained a low normalized root mean square error (NRMSE) of 0.036 in the time series prediction task, underscoring its capability. This study illuminates the adeptness of memristor-based RC systems in managing intricate temporal challenges, laying the groundwork for further innovations in neuromorphic computing.

翻译：储层计算（RC）提供了一种神经形态计算框架，特别适用于处理时空信号。以其时序处理能力著称的RC，相较于传统循环神经网络显著降低了训练成本。其硬件部署的关键在于生成动态储层状态的能力。本研究提出了一种新型双存储RC系统，通过基于WOx忆阻器的短期记忆组件实现4位编码的16种不同状态，并在读出层采用基于TiOx忆阻器的长期记忆组件。我们对两种忆阻器进行了深入研究，并利用该RC系统处理时序数据集。通过两个基准任务验证了所提RC系统的性能：不完整输入的孤立语音数字识别和Mackey-Glass时间序列预测。该系统在数字识别任务中达到98.84%的准确率，在时间序列预测任务中保持0.036的低归一化均方根误差（NRMSE），充分证明了其处理能力。本研究揭示了基于忆阻器的RC系统处理复杂时序挑战的卓越能力，为神经形态计算的进一步创新奠定了基础。