Experimental Demonstration of Reservoir Computing with Self‐Assembled Percolating Networks of Nanoparticles

• DOI: 10.1002/adma.202402319
• Publication Date: 2024-04-01T11:03:55Z
• Authors: Joshua B. Mallinson; Jamie K. Steel; Zachary E. Heywood; Sofie J. Studholme; Philip J. Bones; Simon A. Brown
• Keywords: Reservoir computing; Neuromorphic engineering; Benchmark (surveying)

The complex self-assembled network of neurons and synapses that comprises the biological brain enables natural information processing with remarkable efficiency. Percolating networks nanoparticles (PNNs) are nanoscale systems have been shown to possess many promising brain-like attributes which therefore appealing for neuromorphic computation. Here experiments performed show PNNs can be utilized as physical reservoirs within a nanoelectronic reservoir computing framework demonstrate successful computation several benchmark tasks (chaotic time series prediction, nonlinear transformation, memory capacity). For each task, relevant literature results compiled it is performance compares favorably previously reported from reservoirs. It then demonstrated experimentally used spoken digit recognition state-of-the-art accuracy. Finally, parallel architecture emulated, increases dimensionality richness outputs in further improvements across all tasks.