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：储备池计算提供了一种神经形态框架，尤其适用于处理时空信号。该技术以其在处理时间序列方面的卓越能力著称，与传统循环神经网络相比，可显著降低训练成本。其硬件部署的关键在于生成动态储备池状态的能力。本研究提出一种新型双存储储备池计算系统：通过基于WOx材料的忆阻器实现短期记忆，可编码4比特的16种不同状态；同时在读出层采用基于TiOx材料的忆阻器作为长期记忆组件。我们对两种忆阻器进行了全面分析，并利用该储备池计算系统处理时间序列数据集。通过两个基准任务验证了所提系统的性能：含不完整输入的孤立口语数字识别与Mackey-Glass时间序列预测。该系统在数字识别任务中达到98.84%的惊人准确率，在时间序列预测任务中保持0.036的低归一化均方根误差，充分展现了其处理能力。本研究揭示了基于忆阻器的储备池计算系统在应对复杂时间序列挑战方面的卓越能力，为神经形态计算的进一步创新奠定了基础。