  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.
