CogniGron Seminar: Simon Brown (University of Canterbury) - "Brain-like Computation with Percolating Networks of Nanoparticles"

Self-assembled networks of nanoparticles and nanowires have recently 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.

Our percolating networks of nanoparticles (PNNs) are self-assembled via simple deposition processes that are completely CMOS compatible, making them attractive for integration. The properties of the networks are dominated by tunnel gaps between groups of particles, which turn out to have neuron-like properties. Both the structural and dynamical properties of PNNs have been shown to be brain-like. Hence, PNNs can be viewed as networks of neurons.

The networks exhibit critical avalanches: criticality is a key feature of the biological brain that has been related to optimal information processing capability. More generally, electrical signals from the PNNs exhibit correlations and complexity that are similar to biological neural networks, suggesting their utility for all-electronic implementations of various brain-like computational schemes. I will discuss both simulations of information processing and experimental demonstrations of time series prediction and classification, and highlight key practical issues that make PNNs different to standard ‘reservoirs’ or artificial neural networks.

Simon A. Brown - MacDiarmid Institute, School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand