  Brain-inspired neuromorphic computing which consist neurons and synapses,
with an ability to perform complex information processing has unfolded a new
paradigm of computing to overcome the von Neumann bottleneck. Electronic
synaptic memristor devices which can compete with the biological synapses are
indeed significant for neuromorphic computing. In this work, we demonstrate our
efforts to develop and realize the graphene oxide (GO) based memristor device
as a synaptic device, which mimic as a biological synapse. Indeed, this device
exhibits the essential synaptic learning behavior including analog memory
characteristics, potentiation and depression. Furthermore,
spike-timing-dependent-plasticity learning rule is mimicked by engineering the
pre- and post-synaptic spikes. In addition, non-volatile properties such as
endurance, retentivity, multilevel switching of the device are explored. These
results suggest that Ag/GO/FTO memristor device would indeed be a potential
candidate for future neuromorphic computing applications.
  Keywords: RRAM, Graphene oxide, neuromorphic computing, synaptic device,
potentiation, depression
