Computation via Neuron-like Spiking in Percolating Networks of Nanoparticles

• DOI: 10.1021/acs.nanolett.3c03551
• Publication Date: 2023-11-13T13:22:12Z
• Authors: Sofie J. Studholme; Zachary E. Heywood; Joshua B. Mallinson; Jamie K. Steel; Philip J. Bones; Matthew D. Arnold; Simon A. Brown
• Keywords: Neuromorphic engineering; Unconventional computing; Natural computing; Reservoir computing

The biological brain is a highly efficient computational system in which information processing performed via electrical spikes. Neuromorphic computing systems that work on similar principles could support the development of next generation artificial intelligence and, particular, enable low-power edge computing. Percolating networks nanoparticles (PNNs) have previously been shown to exhibit critical spiking behavior, with promise for natural computation. Here we employ rate coding scheme show PNNs can perform Boolean operations and image classification. Near perfect accuracy achieved both tasks by manipulating activity using certain control voltages. We demonstrate key successful computation nanoscale tunnel gaps within percolating transform input data through powerful modulus-like nonlinearity. These results provide basis implementation further schemes exploit brain-like criticality these networks.