Achieving liquid processors by colloidal suspensions for reservoir computing

Fortulan, Raphael; Kheirabadi, Noushin Raeisi; Chiolerio, Alessandro; Adamatzky, Andrew

Abstract

The increasing use of machine learning, with its significant computational and environmental costs, has motivated the exploration of unconventional computing substrates. Liquid substrates, such as colloids, are of particular interest due to their ability to conform to various shapes while exhibiting complex dynamics resulting from the collective behaviour of the constituent colloidal particles. This study explores the potential of using a PEDOT:PSS colloidal suspension as a physical reservoir for reservoir computing in spoken digit recognition. Reservoir computing uses high-dimensional dynamical systems to perform tasks with different substrates, including physical ones. Here, a physical reservoir is implemented that encodes temporal data by exploiting the rich dynamics inherent in colloidal suspensions, thus avoiding reliance on conventional computing hardware. The reservoir processes audio input encoded as spike sequences, which are then classified using a trained readout layer to identify spoken digits. Evaluation across different speaker scenarios shows that the colloidal reservoir achieves high accuracy in classification tasks, demonstrating its viability as a physical reservoir substrate.

• Journal Article Type: Article
• Acceptance Date: Sep 23, 2024
• Online Publication Date: Sep 28, 2024
• Publication Date: Sep 28, 2024
• Deposit Date: Nov 27, 2024
• Publicly Available Date: Nov 27, 2024
• Journal: Communications Materials
• Print ISSN: 2662-4443
• Electronic ISSN: 2662-4443
• Publisher: Nature Research
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 1
• Article Number: 199
• DOI: https://doi.org/10.1038/s43246-024-00653-7
• Public URL: https://uwe-repository.worktribe.com/output/13235664
• Publisher URL: https://www.nature.com/articles/s43246-024-00653-7
• Additional Information: Received: 29 March 2024; Accepted: 23 September 2024; First Online: 28 September 2024; : The authors declare no competing interests.