Reservoir computing, a machine learning framework used for modeling the
brain, can predict temporal data with little observations and minimal
computational resources. However, it is difficult to accurately reproduce the
long-term target time series because the reservoir system becomes unstable.
This predictive capability is required for a wide variety of time-series
processing, including predictions of motor timing and chaotic dynamical
systems. This study proposes oscillation-driven reservoir computing (ODRC) with
feedback, where oscillatory signals are fed into a reservoir network to
stabilize the network activity and induce complex reservoir dynamics. The ODRC
can reproduce long-term target time series more accurately than conventional
reservoir computing methods in a motor timing and chaotic time-series
prediction tasks. Furthermore, it generates a time series similar to the target
in the unexperienced period, that is, it can learn the abstract generative
rules from limited observations. Given these significant improvements made by
the simple and computationally inexpensive implementation, the ODRC would serve
as a practical model of various time series data. Moreover, we will discuss
biological implications of the ODRC, considering it as a model of neural
oscillations and their cerebellar processors.

通过振荡驱动储层计算的反馈机制，本研究提出一种能够稳定网络活动且诱导复杂储层动力学的储层计算模型，即振荡驱动储层计算（ODRC）。ODRC 在运动定时和混沌时间序列预测任务中与传统储层计算方法相比能更准确地复现长期目标时间序列，并能从有限观测中学习到抽象生成规则。基于这种简单且计算廉价的实现所获得的显著改进，ODRC 可作为各种时间序列数据的实用模型，并可作为神经震荡及其小脑处理器的模型进行生物学探讨。