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.

我们的研究引入了一种新型的双存储器 RC 系统，通过基于 WOx 的忆阻器实现 16 个不同状态的编码和基于 TiOx 的忆阻器实现长期记忆单元，实现了处理时间数据集的能力。通过对孤立语音数字识别和 Mackey-Glass 时间序列预测的基准任务的验证，该系统在数字识别中达到了 98.84% 的准确率，在时间序列预测任务中保持了较低的规范化均方根误差（NRMSE）0.036，突出了其能力。本研究揭示了基于忆阻器的 RC 系统在处理复杂时间挑战方面的能力，为神经形态计算的进一步创新奠定了基础。