{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T08:02:24Z","timestamp":1789632144313,"version":"build-2803163510"},"reference-count":68,"publisher":"The Royal Society","issue":"1958","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation AI Institute in Dynamic Systems","award":["2112085"],"award-info":[{"award-number":["2112085"]}]},{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","award":["FA9550-24-1-0141"],"award-info":[{"award-number":["FA9550-24-1-0141"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Machine learning algorithms are affording new opportunities for building bio-inspired and data-driven models characterizing neural activity. Critical to understanding decision-making and behaviour is quantifying the relationship between the activity of neuronal population codes and individual neurons. We leverage a SHallow REcurrent Decoder (SHRED) architecture for mapping the dynamics of population codes to individual neurons and other proxy measures of neural activity and behaviour. SHRED is constructed from a temporal sequence model, which encodes the temporal dynamics of limited sensor data in multiple scenarios, and a shallow decoder, which reconstructs the corresponding high-dimensional neuronal and\/or behavioural states. It is a robust and flexible sensing strategy which allows for decoding the diversity of neural measurements with only a few sensor measurements. Thus, estimates of whole-brain activity, behaviour and individual neurons can be constructed with only a few neural time-series recordings. Several examples in this article further highlight the potential of leveraging non-invasive or minimally invasive measurements to estimate large-scale brain dynamics. We empirically demonstrate the capabilities of the method on a number of model organisms including Caenorhabditis elegans, mouse, zebrafish and human biolocomotion.<\/jats:p>\n                  <jats:p>This article is part of the discussion meeting issue \u2018Digital healthcare for the management of functional neurological disorders\u2019.<\/jats:p>","DOI":"10.1098\/rstb.2024.0461","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T07:07:38Z","timestamp":1789628858000},"source":"Crossref","is-referenced-by-count":0,"title":["Shallow recurrent decoders for neural and behavioural dynamics"],"prefix":"10.1098","volume":"381","author":[{"given":"Amy","family":"Rude","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics, University of Washington 1 , Seattle, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6004-2275","authenticated-orcid":false,"given":"J. 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