  Recent advancements in reservoir computing research have created a demand for
analog devices with dynamics that can facilitate the physical implementation of
reservoirs, promising faster information processing while consuming less energy
and occupying a smaller area footprint. Studies have demonstrated that dynamic
memristors, with nonlinear and short-term memory dynamics, are excellent
candidates as information-processing devices or reservoirs for temporal
classification and prediction tasks. Previous implementations relied on
nominally identical memristors that applied the same nonlinear transformation
to the input data, which is not enough to achieve a rich state space. To
address this limitation, researchers either diversified the data encoding
across multiple memristors or harnessed the stochastic device-to-device
variability among the memristors. However, this approach requires additional
pre-processing steps and leads to synchronization issues. Instead, it is
preferable to encode the data once and pass it through a reservoir layer
consisting of memristors with distinct dynamics. Here, we demonstrate that
ion-channel-based memristors with voltage-dependent dynamics can be
controllably and predictively tuned through voltage or adjustment of the ion
channel concentration to exhibit diverse dynamic properties. We show, through
experiments and simulations, that reservoir layers constructed with a small
number of distinct memristors exhibit significantly higher predictive and
classification accuracies with a single data encoding. We found that for a
second-order nonlinear dynamical system prediction task, the varied memristor
reservoir experimentally achieved a normalized mean square error of 0.0015
using only five distinct memristors. Moreover, in a neural activity
classification task, a reservoir of just three distinct memristors
experimentally attained an accuracy of 96.5%.
