  We report a detailed study of neuromorphic switching behaviour in inherently
complex percolating networks of self-assembled metal nanoparticles. We show
that variation of the strength and duration of the electric field applied to
this network of synapse-like atomic switches allows us to control the switching
dynamics. Switching is observed for voltages above a well-defined threshold,
with higher voltages leading to increased switching rates. We demonstrate two
behavioral archetypes and show how the switching dynamics change as a function
of duration and amplitude of the voltage stimulus. We show that the state of
each synapse can influence the activity of the other synapses, leading to
complex switching dynamics. We further demonstrate the influence of the
morphology of the network on the measured device properties, and the
constraints imposed by the overall network conductance. The correlated
switching dynamics, device stability over long periods, and the simplicity of
the device fabrication provide an attractive pathway to practical
implementation of on-chip neuromorphic computing.
