Brain-inspired computing with fluidic iontronic nanochannels.

Autor:	Kamsma TM; Institute for Theoretical Physics, Department of Physics, Utrecht University, Utrecht 3584, The Netherlands.; Mathematical Institute, Department of Mathematics, Utrecht University, Utrecht 3584, The Netherlands., Kim J; Department of Mechanical Engineering, Sogang University, Seoul 04107, Republic of Korea., Kim K; Department of Mechanical Engineering, Sogang University, Seoul 04107, Republic of Korea., Boon WQ; Institute for Theoretical Physics, Department of Physics, Utrecht University, Utrecht 3584, The Netherlands., Spitoni C; Mathematical Institute, Department of Mathematics, Utrecht University, Utrecht 3584, The Netherlands., Park J; Department of Mechanical Engineering, Sogang University, Seoul 04107, Republic of Korea., van Roij R; Institute for Theoretical Physics, Department of Physics, Utrecht University, Utrecht 3584, The Netherlands.
Jazyk:	angličtina
Zdroj:	Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2024 Apr 30; Vol. 121 (18), pp. e2320242121. Date of Electronic Publication: 2024 Apr 24.
DOI:	10.1073/pnas.2320242121
Abstrakt:	The brain's remarkable and efficient information processing capability is driving research into brain-inspired (neuromorphic) computing paradigms. Artificial aqueous ion channels are emerging as an exciting platform for neuromorphic computing, representing a departure from conventional solid-state devices by directly mimicking the brain's fluidic ion transport. Supported by a quantitative theoretical model, we present easy-to-fabricate tapered microchannels that embed a conducting network of fluidic nanochannels between a colloidal structure. Due to transient salt concentration polarization, our devices are volatile memristors (memory resistors) that are remarkably stable. The voltage-driven net salt flux and accumulation, that underpin the concentration polarization, surprisingly combine into a diffusionlike quadratic dependence of the memory retention time on the channel length, allowing channel design for a specific timescale. We implement our device as a synaptic element for neuromorphic reservoir computing. Individual channels distinguish various time series, that together represent (handwritten) numbers, for subsequent in silico classification with a simple readout function. Our results represent a significant step toward realizing the promise of fluidic ion channels as a platform to emulate the rich aqueous dynamics of the brain. Competing Interests: Competing interests statement:The authors declare no competing interest.
Databáze:	MEDLINE
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