  Neuromorphic devices have gained significant attention as potential building
blocks for the next generation of computing technologies owing to their ability
to emulate the functionalities of biological nervous systems. The essential
components in artificial neural network such as synapses and neurons are
predominantly implemented by dedicated devices with specific functionalities.
In this work, we present a gate-controlled transition of neuromorphic functions
between artificial neurons and synapses in monolayer graphene transistors that
can be employed as memtransistors or synaptic transistors as required. By
harnessing the reliability of reversible electrochemical reactions between C
atoms and hydrogen ions, the electric conductivity of graphene transistors can
be effectively manipulated, resulting in high on/off resistance ratio,
well-defined set/reset voltage, and prolonged retention time. Overall, the
on-demand switching of neuromorphic functions in a single graphene transistor
provides a promising opportunity to develop adaptive neural networks for the
upcoming era of artificial intelligence and machine learning.
