Recent advances in fluidic neuromorphic computing

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dc.contributor.authorLaw, C.S.
dc.contributor.authorWang, J.
dc.contributor.authorNielsch, K.
dc.contributor.authorAbell, A.D.
dc.contributor.authorBisquert, J.
dc.contributor.authorSantos, A.
dc.date.issued2025
dc.description.abstractHuman brain is capable of optimizing information flow and processing without energy-intensive data shuttling between processor and memory. At the core of this unique capability are billions of neurons connected through trillions of synapses—basic processing units of the brain. The action potentials or “spikes” based temporal processing using the regulated flow of ions across ion channels in neuron cells allows sparse and efficient transmission of data in the brain. Emerging systems based on confined fluidic systems have provided a framework for a new type of neuromorphic computing with lower energy consumption, hardware-level plasticity, and multiple information carriers that emulate natural processes and mechanisms of human brain. These systems mimic neuronal architectures by harnessing and modulating ion transport along artificial channels. The spikes-induced ion-to-surface interactions within these fluidic systems enables the control of ionic conductivity to achieve synaptic plasticity for the realization of brain-inspired functionalities such as memory effect and signal transmission. Herein, this review provides an overview of recent advances in fluidic devices such as memristors and other computing components, covering their basic operations, materials and architectures, as well as applications in neuromorphic computing. The review concludes with a brief outline of the challenges that these emerging technologies face and an outlook for the development of fluidic-based brain-inspired computing.
dc.description.statementofresponsibilityCheryl Suwen Law, Juan Wang, Kornelius Nielsch, Andrew D. Abell, Juan Bisquert, and Abel Santos
dc.identifier.citationApplied Physics Reviews, 2025; 12(2):021309-1-021309-30
dc.identifier.doi10.1063/5.0235267
dc.identifier.issn1931-9401
dc.identifier.issn1931-9401
dc.identifier.orcidLaw, C.S. [0000-0002-3276-8052]
dc.identifier.orcidAbell, A.D. [0000-0002-0604-2629]
dc.identifier.orcidSantos, A. [0000-0002-5081-5684]
dc.identifier.urihttps://hdl.handle.net/2440/147413
dc.language.isoen
dc.publisherAmerican Institute of Physics
dc.relation.granthttp://purl.org/au-research/grants/arc/DP220102857
dc.rights© 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/). https://doi.org/10.1063/5.0235267
dc.source.urihttps://doi.org/10.1063/5.0235267
dc.subjectMemristor; Neuromorphic engineering; Signal processing; Electrolytes; Ionic conductivity; Microfluidics; Action potential; Energy consumption
dc.titleRecent advances in fluidic neuromorphic computing
dc.typeJournal article
pubs.publication-statusPublished online

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