{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:01:41Z","timestamp":1792785701333,"version":"4.5.0"},"reference-count":48,"publisher":"IOP Publishing","issue":"5","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2023YFF1203804"],"award-info":[{"award-number":["2023YFF1203804"]}]},{"name":"National Natural Science Foundation of China","award":["82471196"],"award-info":[{"award-number":["82471196"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    This study aimed to develop and evaluate a compact spike decoding framework for classifying visual stimulus from retinal multi-channel electrode array (MEA) recordings that may have the problem of poor electrode-tissue coupling. Specifically, we sought to establish a convolution-attention framework that can extract spike features and capture longer-range temporal dependencies in visually evoked retinal activities.\n                    <jats:italic>Approach.<\/jats:italic>\n                    MEA recordings from\n                    <jats:italic>ex vivo<\/jats:italic>\n                    mouse retinas were acquired under controlled color and shape stimulation paradigms. Spike trains were converted into spike-count sequences and used to train an artificial neural network framework combining convolutional neural network layers with temporal multi-head attention modules. Convolutional layers extracted local temporal features, while the attention modules facilitated broader temporal relationships across the response sequence.\n                    <jats:italic>Main results.<\/jats:italic>\n                    The proposed convolution-attention framework achieved good performance across both color and shape tasks. The proposed framework reached 93.250% accuracy in color classification and 73.161% accuracy in the shape classification task. These results show that the proposed framework can decode visually evoked retinal spike responses under different stimulus conditions and retains stable performance across tasks with different levels of complexity.\n                    <jats:italic>Significance.<\/jats:italic>\n                    This study presents a compact decoding framework for retinal spike trains and provides a potential computational backbone for spike-based neural decoding in bio-electronic interfaces. By improving stimulus classification from retinal neural activity, the proposed model may provide a cornerstone to support future visual neuroengineering and bio-digital convergence.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/aea4f6","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T22:52:43Z","timestamp":1788994363000},"page":"056039","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Recognition of\n                    <i>ex vivo<\/i>\n                    spike response of retina using a convolution-attention combined framework"],"prefix":"10.1088","volume":"23","author":[{"given":"Junchi","family":"Wang","sequence":"first","affiliation":[{"name":"Tianjin University"},{"name":"Hebei University of Technology"},{"name":"Tianjin University"},{"name":"Tianjin University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Pengxiang","family":"Li","sequence":"additional","affiliation":[{"name":"Tianjin University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hebei University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8268-5781","authenticated-orcid":true,"given":"Wenwei","family":"Shao","sequence":"additional","affiliation":[{"name":"Tianjin University"},{"name":"Tianjin University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7623-5787","authenticated-orcid":true,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tianjin University"},{"name":"Tianjin University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Liqun","family":"Chen","sequence":"additional","affiliation":[{"name":"Tianjin University"},{"name":"Tianjin University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"266","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"jneaea4f6bib1","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1146\/annurev-vision-100820-114239","type":"journal-article","article-title":"Retinal encoding of natural scenes","volume":"8","author":"Karamanlis","year":"2022","journal-title":"Annu. Rev. Vis. Sci."},{"key":"jneaea4f6bib2","doi-asserted-by":"publisher","first-page":"2742","DOI":"10.1016\/j.neuron.2023.06.007","type":"journal-article","article-title":"Interpreting the retinal neural code for natural scenes: from computations to neurons","volume":"111","author":"Maheswaranathan","year":"2023","journal-title":"Neuron"},{"key":"jneaea4f6bib3","doi-asserted-by":"publisher","first-page":"11003","DOI":"10.1523\/JNEUROSCI.3305-05.2005","type":"journal-article","article-title":"Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model","volume":"25","author":"Pillow","year":"2005","journal-title":"J. Neurosci."},{"key":"jneaea4f6bib4","doi-asserted-by":"publisher","first-page":"378","DOI":"10.3389\/fnins.2020.00378","type":"journal-article","article-title":"Characterizing retinal ganglion cell responses to electrical stimulation using generalized linear models","volume":"14","author":"Sekhar","year":"2020","journal-title":"Front. Neurosci."},{"key":"jneaea4f6bib5","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/0014-4827(72)90481-8","type":"journal-article","article-title":"A miniature microelectrode array to monitor the bioelectric activity of cultured cells","volume":"74","author":"Thomas","year":"1972","journal-title":"Exp. Cell Res."},{"key":"jneaea4f6bib6","doi-asserted-by":"publisher","DOI":"10.1016\/j.mex.2023.102543","type":"journal-article","article-title":"Improvements for recording retinal function with microelectrode arrays","volume":"12","author":"Rathbun","year":"2024","journal-title":"MethodsX"},{"key":"jneaea4f6bib7","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2023.1245082","type":"journal-article","article-title":"Exploring retinal ganglion cells encoding to multi-modal stimulation using 3d microelectrodes arrays","volume":"11","author":"Zhang","year":"2023","journal-title":"Front. Bioeng. Biotechnol."},{"key":"jneaea4f6bib8","doi-asserted-by":"publisher","first-page":"4844","DOI":"10.1039\/D5LC00058K","type":"journal-article","article-title":"Advances in large-scale electrophysiology with high-density microelectrode arrays","volume":"25","author":"Schr\u00f6ter","year":"2025","journal-title":"Lab Chip"},{"key":"jneaea4f6bib9","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1167\/tvst.12.1.4","type":"journal-article","article-title":"Assessment of murine retinal acuity ex vivo using multielectrode array recordings","volume":"12","author":"Babino","year":"2023","journal-title":"Trans. Vis. Sci. Technol."},{"key":"jneaea4f6bib10","doi-asserted-by":"publisher","DOI":"10.1088\/2516-1091\/ac6b96","type":"journal-article","article-title":"Spike sorting: new trends and challenges of the era of high-density probes Prog","volume":"4","author":"Buccino","year":"2022","journal-title":"Biomed. Eng."},{"key":"jneaea4f6bib11","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.conb.2019.02.007","type":"journal-article","article-title":"Continuing progress of spike sorting in the era of big data","volume":"55","author":"Carlson","year":"2019","journal-title":"Curr. Opin. Neurobiol."},{"key":"jneaea4f6bib12","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2022.851024","type":"journal-article","article-title":"From end to end: gaining, sorting, and employing high-density neural single unit recordings","volume":"16","author":"Bod","year":"2022","journal-title":"Front. Neuroinform."},{"key":"jneaea4f6bib13","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.1093\/bib\/bbaa355","type":"journal-article","article-title":"Deep learning approaches for neural decoding across architectures and recording modalities","volume":"22","author":"Livezey","year":"2021","journal-title":"Brief. Bioinform."},{"key":"jneaea4f6bib14","doi-asserted-by":"publisher","first-page":"0044","DOI":"10.34133\/cbsystems.0044","type":"journal-article","article-title":"Neural decoding for intracortical brain-computer interfaces","volume":"4","author":"Dong","year":"2023","journal-title":"Cyborg Bionic Syst."},{"key":"jneaea4f6bib15","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/7741735","type":"journal-article","article-title":"A systematic review of using deep learning technology in the steady-state visually evoked potential-based brain-computer interface applications: current trends and future trust methodology","volume":"2023","author":"Albahri","year":"2023","journal-title":"Int. J. Telemed. Appl."},{"key":"jneaea4f6bib16","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.667907","type":"journal-article","article-title":"Deep learning-based approaches for decoding motor intent from peripheral nerve signals","volume":"15","author":"Luu","year":"2021","journal-title":"Front. Neurosci."},{"key":"jneaea4f6bib17","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103300","type":"journal-article","article-title":"Deep learning-based classification of multichannel bio-signals using directedness transfer learning","volume":"72","author":"Bahador","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"jneaea4f6bib18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110726","type":"journal-article","article-title":"A novel hybrid decoding neural network for EEG signal representation","volume":"155","author":"Ji","year":"2024","journal-title":"Pattern Recognit."},{"key":"jneaea4f6bib19","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-77923-4","type":"journal-article","article-title":"A hybrid local-global neural network for visual classification using raw eeg signals","volume":"14","author":"Xue","year":"2024","journal-title":"Sci. Rep."},{"key":"jneaea4f6bib20","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105931","type":"journal-article","article-title":"Deep transfer learning-based SSVEP frequency domain decoding method","volume":"89","author":"Xiong","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"jneaea4f6bib21","doi-asserted-by":"publisher","DOI":"10.1101\/2024.01.05.574379","type":"other","article-title":"Interpretable deep learning for deconvolutional analysis of neural signals bioRxiv 2024.01.05.574379","author":"Tolooshams","year":"2024"},{"key":"jneaea4f6bib22","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2022.1006763","type":"journal-article","article-title":"Status of deep learning for EEG-based brain\u2013computer interface applications","volume":"16","author":"Hossain","year":"2023","journal-title":"Front. Comput. Neurosci."},{"key":"jneaea4f6bib23","doi-asserted-by":"publisher","first-page":"e2065","DOI":"10.7717\/peerj-cs.2065","type":"journal-article","article-title":"A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications","volume":"10","author":"Ma","year":"2024","journal-title":"PeerJ Comput. Sci."},{"key":"jneaea4f6bib24","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ad8b6c","type":"journal-article","article-title":"Deep learning-based spike sorting: a survey","volume":"21","author":"Meyer","year":"2024","journal-title":"J. Neural Eng."},{"key":"jneaea4f6bib25","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c","type":"journal-article","article-title":"EEGNet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng."},{"key":"jneaea4f6bib26","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1012297","type":"journal-article","article-title":"Decoding dynamic visual scenes across the brain hierarchy","volume":"20","author":"Chen","year":"2024","journal-title":"PLoS Comput. Biol."},{"key":"jneaea4f6bib27","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2025.107863","type":"journal-article","article-title":"Decoding natural visual scenes via learnable representations of neural spiking sequences","volume":"192","author":"Peng","year":"2025","journal-title":"Neural Netw."},{"key":"jneaea4f6bib28","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065724500709","type":"journal-article","article-title":"Decoding continuous tracking eye movements from cortical spiking activity","volume":"35","author":"Noneman","year":"2025","journal-title":"Int. J. Neural Syst."},{"key":"jneaea4f6bib29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1013335","type":"journal-article","article-title":"Decoding decision-making behavior from sparse neural spiking activity","volume":"21","author":"Zhang","year":"2025","journal-title":"PLoS Comput. Biol."},{"key":"jneaea4f6bib30","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.3390\/s25051293","type":"journal-article","article-title":"Transformers in EEG analysis: a review of architectures and applications in motor imagery, seizure, and emotion classification","volume":"25","author":"Vafaei","year":"2025","journal-title":"Sensors"},{"key":"jneaea4f6bib31","doi-asserted-by":"publisher","first-page":"5814","DOI":"10.1016\/j.cell.2024.08.051","type":"journal-article","article-title":"Decoding the brain: from neural representations to mechanistic models","volume":"187","author":"Mathis","year":"2024","journal-title":"Cell"},{"key":"jneaea4f6bib32","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.neunet.2023.04.045","type":"journal-article","article-title":"A transformer-based deep neural network model for SSVEP classification","volume":"164","author":"Chen","year":"2023","journal-title":"Neural Netw."},{"key":"jneaea4f6bib33","doi-asserted-by":"publisher","first-page":"0379","DOI":"10.34133\/cbsystems.0379","type":"journal-article","article-title":"Augmenting electroencephalogram transformer for steady-state visually evoked potential-based brain\u2013computer interfaces","volume":"6","author":"Yue","year":"2025","journal-title":"Cyborg Bionic Syst."},{"key":"jneaea4f6bib34","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-16219-7","type":"journal-article","article-title":"Temporal convolutional transformer for EEG based motor imagery decoding","volume":"15","author":"Altaheri","year":"2025","journal-title":"Sci. Rep."},{"key":"jneaea4f6bib35","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1109\/TNSRE.2022.3230250","type":"journal-article","article-title":"EEG conformer: convolutional transformer for EEG decoding and visualization","volume":"31","author":"Song","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneaea4f6bib36","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-86294-3","type":"journal-article","article-title":"Flexible patched brain transformer model for EEG decoding","volume":"15","author":"Klein","year":"2025","journal-title":"Sci. Rep."},{"key":"jneaea4f6bib37","doi-asserted-by":"publisher","DOI":"10.1101\/2021.01.16.426955","type":"other","article-title":"Representation learning for neural population activity with neural data transformers bioRxiv","volume":"vol 5","author":"Ye","year":"2021"},{"key":"jneaea4f6bib38","doi-asserted-by":"publisher","DOI":"10.1101\/2023.09.18.558113","type":"journal-article","article-title":"Neural data transformer 2: multi-context pretraining for neural spiking activity bioRxiv 2023.09.18.558113","author":"Ye","year":"2023"},{"key":"jneaea4f6bib39","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1038\/s41586-024-08212-3","type":"journal-article","article-title":"Nonlinear receptive fields evoke redundant retinal coding of natural scenes","volume":"637","author":"Karamanlis","year":"2025","journal-title":"Nature"},{"key":"jneaea4f6bib40","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1014157","type":"journal-article","article-title":"Modeling spatial contrast sensitivity in responses of primate retinal ganglion cells to natural movies","volume":"22","author":"Sridhar","year":"2026","journal-title":"PLoS Comput. Biol."},{"key":"jneaea4f6bib41","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003344","type":"journal-article","article-title":"Transformation of stimulus correlations by the retina","volume":"9","author":"Simmons","year":"2013","journal-title":"PLoS Comput. Biol."},{"key":"jneaea4f6bib42","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1038\/nature16468","type":"journal-article","article-title":"The functional diversity of retinal ganglion cells in the mouse","volume":"529","author":"Baden","year":"2016","journal-title":"Nature"},{"key":"jneaea4f6bib43","doi-asserted-by":"publisher","first-page":"E016","DOI":"10.1017\/S0952523821000158","type":"journal-article","article-title":"Classification of pseudocalcium visual responses from mouse retinal ganglion cells","volume":"38","author":"Shabani","year":"2021","journal-title":"Vis. Neurosci."},{"key":"jneaea4f6bib44","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.38841","type":"journal-article","article-title":"Receptive field center-surround interactions mediate context-dependent spatial contrast encoding in the retina","volume":"7","author":"Turner","year":"2018","journal-title":"eLife"},{"key":"jneaea4f6bib45","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.61834","type":"journal-article","article-title":"SpikeInterface, a unified framework for spike sorting","volume":"9","author":"Buccino","year":"2020","journal-title":"eLife"},{"key":"jneaea4f6bib46","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.55167","type":"journal-article","article-title":"SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters","volume":"9","author":"Magland","year":"2020","journal-title":"eLife"},{"key":"jneaea4f6bib47","doi-asserted-by":"publisher","first-page":"2083","DOI":"10.3390\/s25072083","type":"journal-article","article-title":"Optimizing depression classification using combined datasets and hyperparameter tuning with optuna","volume":"25","author":"tefana","year":"2025","journal-title":"Sensors"},{"key":"jneaea4f6bib48","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2021.100350","type":"journal-article","article-title":"Unraveling neural coding of dynamic natural visual scenes via convolutional recurrent neural networks","volume":"2","author":"Zheng","year":"2021","journal-title":"Patterns"}],"container-title":["Journal of Neural Engineering"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aea4f6","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aea4f6\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aea4f6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aea4f6\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aea4f6\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aea4f6\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aea4f6\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aea4f6\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T08:16:49Z","timestamp":1790583409000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aea4f6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,28]]},"references-count":48,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,9,28]]},"published-print":{"date-parts":[[2026,10,1]]}},"URL":"https:\/\/doi.org\/10.1088\/1741-2552\/aea4f6","relation":{},"ISSN":["1741-2560","1741-2552"],"issn-type":[{"value":"1741-2560","type":"print"},{"value":"1741-2552","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,28]]},"assertion":[{"value":"Recognition of\n                      ex vivo\n                      spike response of retina using a convolution-attention combined framework","name":"article_title","label":"Article Title"},{"value":"Journal of Neural Engineering","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-11-27","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-09","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-28","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}