# Synaptic dynamics in complex self-assembled nanoparticle networks

## TL;DR

This study investigates neuromorphic switching in complex nanoparticle networks, demonstrating controllable synapse-like behavior influenced by electric stimuli, network morphology, and inter-synapse interactions, with implications for neuromorphic computing.

## Contribution

It provides a detailed analysis of switching dynamics in self-assembled nanoparticle networks, highlighting how electric stimuli and network structure influence neuromorphic behavior.

## Key findings

- Switching occurs above a voltage threshold with rates increasing at higher voltages.
- Two distinct behavioral archetypes of switching are identified.
- Network morphology and conductance constrain device properties.

## Abstract

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.