  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.
