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

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

© 2024 The Authors. Advanced Materials published by Wiley‐VCH GmbH.

Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Deerfield Beach, Fla.). - 1998. - 36(2024), 29 vom: 27. Juli, Seite e2402319
1. Verfasser: Mallinson, Joshua B (VerfasserIn)
Weitere Verfasser: Steel, Jamie K, Heywood, Zachary E, Studholme, Sofie J, Bones, Philip J, Brown, Simon A
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2024
Zugriff auf das übergeordnete Werk:Advanced materials (Deerfield Beach, Fla.)
Schlagworte:Journal Article nanoparticle networks neuromorphic computing percolation reservoir computing self‐assembly
Beschreibung
Zusammenfassung:© 2024 The Authors. Advanced Materials published by Wiley‐VCH GmbH.
The complex self-assembled network of neurons and synapses that comprises 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 experiments are performed 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, nonlinear transformation, and memory capacity). For each task, relevant literature results are compiled and it is shown that the performance of the PNNs compares favorably to that previously reported from nanoelectronic reservoirs. It is then demonstrated experimentally that PNNs can be used for spoken digit recognition with state-of-the-art accuracy. Finally, a parallel reservoir architecture is emulated, which increases the dimensionality and richness of the reservoir outputs and results in further improvements in performance across all tasks
Beschreibung:Date Revised 18.07.2024
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1521-4095
DOI:10.1002/adma.202402319