  Reservoir computing is a machine learning paradigm that uses a structure
called a reservoir, which has nonlinearities and short-term memory. In recent
years, reservoir computing has expanded to new functions such as the autonomous
generation of chaotic time series, as well as time series prediction and
classification. Furthermore, novel possibilities have been demonstrated, such
as inferring the existence of previously unseen attractors. Sampling, in
contrast, has a strong influence on such functions. Sampling is indispensable
in a physical reservoir computer that uses an existing physical system as a
reservoir because the use of an external digital system for the data input is
usually inevitable. This study analyzes the effect of sampling on the ability
of reservoir computing to autonomously regenerate chaotic time series. We
found, as expected, that excessively coarse sampling degrades the system
performance, but also that excessively dense sampling is unsuitable. Based on
quantitative indicators that capture the local and global characteristics of
attractors, we identify a suitable window of the sampling frequency and discuss
its underlying mechanisms.
