  In conventional digital computers, data and information are represented in
binary form and encoded in the steady states of transistors. They are then
processed in a quasi-static way. However, with transistors approaching their
physical limits and the von Neumann bottleneck, the rate of improvement in
computing efficiency has slowed down. Therefore, drawing inspiration from the
dynamic and adaptive properties of biological systems, research in neural
morphology computing has great potential for development. Memristors, a class
of nonlinear resistors with memory function, naturally embody dynamics through
their internal electrical processes. This enables nonconventional computing
paradigms with enhanced capability and energy efficiency, such as reservoir
computing. In this paper, we propose a volatile memristor made of LiNbO3 with
nonlinear I-V characteristics and short-term memory function. This memristor is
well-suited to be used as a nonlinear node in the storage layer of reservoir
computing. With this system, we can achieve the same functionality as
traditional reservoir computing with a single device, instead of a large number
of interconnected nodes. The collective states of memristors after the
application of trains of pulses to the respective memristors are unique for
each combination of pulse patterns. This provides a more reliable and efficient
way for subsequent output layer classification processing. The system was
successfully used to recognize images of Arabic numerals 0-9. This work not
only broadens the application scope of materials such as lithium niobate in
neural morphology computing but also provides new ideas for developing more
efficient neural morphology devices and systems.
