Achieving liquid processors by colloidal suspensions for reservoir computing

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. Reservoir computing is a neural network framework suitable for processing temporal and sequential information. Here, a polymeric colloidal suspension is investigated as a physical reservoir for reservoir computing in spoken digit recognition.