Brain-like Computation with Percolating Networks of Nanoparticles

Abstract

Self-assembled networks of nanoparticles have emerged as important candidate systems for brain-like (or neuromorphic) information processing. The essence of the approach is to take advantage of the intrinsic dynamical properties of these networks to implement brain-inspired approaches to computation. Both the structural and dynamical properties of the networks have been shown to be brain-like and, in particular, avalanches of neuron-like spiking events have been shown to be critical. Criticality is a key feature of the biological brain that has been related to optimal information processing capability. We have explored brain-like computation with PNNs in two regimes, beginning with simulations that allow us to understand the processes and refine parameters, and then moving to experimental demonstrations . At low voltages, we focus on reservoir computation and we have successfully demonstrated time series prediction, non-linear transformation and spoken digit recognition. In the high voltage regime, the neuron-like spiking behaviour has been exploited to perform Boolean logic and hand written digit classification , and, most recently, optimization tasks such as integer factorisation. Current work is focused on
achieving neuron-like and synapse-like behaviour simultaneously, in order to demonstrate new kinds of learning behaviour.