Computation via Neuron-like Spiking in Percolating Networks of Nanoparticles

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