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

Abstract

The complex self‐assembled network of neurons and synapses that comprizes the biological brain enables natural information processing with remarkable efficiency. Percolating Networks of Nanoparticles (PNNs) are complex self‐assembled nanoscale systems that have been shown to possess many promising brain‐like attributes and which are therefore appealing systems for neuromorphic computation. Here we perform experiments that show that PNNs can be utilized as physical reservoirs within a nanoelectronic reservoir computing framework and demonstrate successful computation for several benchmark tasks (chaotic time series prediction, non‐linear transformation and memory capacity). For each task we compile relevant literature results and show that the performance of the PNNs compares favourably to that previously reported from nanoelectronic reservoirs. We then demonstrate experimentally that PNNs can be used for spoken digit recognition with state‐of‐the‐art accuracy. Finally, we emulate a parallel reservoir architecture, which increases the dimensionality and richness of the reservoir outputs and results in further improvements in performance across all tasks.This article is protected by copyright. All rights reserved