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Building artificial neural networks (ANNs) with robust connections and dynamic plasticity is essential for their implementation. Nevertheless, most existing ANNs rely on solid\u2010state semiconductors, where electrons or holes serve as information carriers. Here, we present a novel ion\u2010based nanofluidic memristor operating in aqueous solutions, which can be integrated to create densely connected ionic neural networks (INNs). The nanofluidic memristor exhibits tunable ionic memory and diverse synaptic plasticity that can be modulated by electrical, optical, or combination signals. We demonstrate that the ionic memory originates from non\u2010trivial hysteretic ion\u2010charged surface interactions in confined nanochannels, supported by experiments and numerical calculations. Utilizing the tunable states of the nanofluidic memristor, we implement reservoir computing to classify static patterns and dynamical motions, achieving ultrahigh accuracies of 91% and 97% on the standard MNIST datasets and homemade moving\u2010particle library, respectively. Multiple nanofluidic memristors are integrated to construct a practical INN capable of real\u2010time logic computation and in\u2010sensor computing, verifying the robustness of nanofluidic computation systems. This ion\u2010based nanofluidic platform offers a promising pathway towards fully connected INNs and advances neuromorphic computing beyond solid\u2010state electronics.<\/jats:p>","DOI":"10.1002\/adma.74080","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:32:26Z","timestamp":1784201546000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optoelectronic Nanofluidic Neural Networks for Ionic Computing"],"prefix":"10.1002","volume":"38","author":[{"given":"Yaxin","family":"Huang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering Beijing Institute of Technology  Beijing P. R. China"},{"name":"Department of Chemistry The University of Hong Kong  Hong Kong P. R. 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Can","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering The University of Hong Kong  Hong Kong P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0051-148X","authenticated-orcid":false,"given":"Jinyao","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Chemistry The University of Hong Kong  Hong Kong P. R. China"},{"name":"State Key Laboratory of Synthetic Chemistry The University of Hong Kong  Hong Kong P. R. China"},{"name":"HKU\u2010CAS Joint Laboratory on New Materials and Department of Chemistry  Hong Kong P. R. China"},{"name":"Materials Innovation Institute for Life Sciences and Energy (MILES) The University of Hong Kong  Hong Kong P. R. 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