Neuromorphic devices have attracted 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 networks
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 carbon atoms and hydrogen ions, we can effectively manipulate
the electric conductivity of graphene transistors, resulting in a
high on/off resistance ratio, a well-defined set/reset voltage, and
a prolonged retention time. Overall, the on-demand switching of neuromorphic
functions in a single graphene transistor provides a promising opportunity
for developing adaptive neural networks for the upcoming era of artificial
intelligence and machine learning.