{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T02:42:12Z","timestamp":1776739332248,"version":"3.51.2"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T00:00:00Z","timestamp":1766793600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T00:00:00Z","timestamp":1766793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroinform"],"DOI":"10.1007\/s12021-025-09761-2","type":"journal-article","created":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T06:57:29Z","timestamp":1766818649000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Synthetic Data Generation for Classifying Electrophysiological and Morpho-Electrophysiological Neurons from Mouse Visual Cortex"],"prefix":"10.1007","volume":"24","author":[{"given":"Xavier","family":"Vasques","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura","family":"Cif","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,27]]},"reference":[{"key":"9761_CR1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1701.07875","author":"M Arjovsky","year":"2017","unstructured":"Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN (Version 3). ArXiv. https:\/\/doi.org\/10.48550\/ARXIV.1701.07875","journal-title":"ArXiv"},{"key":"9761_CR2","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1016\/S0925-2312(00)00272-1","volume":"32\u201333","author":"GA Ascoli","year":"2000","unstructured":"Ascoli, G. A., & Krichmar, J. L. (2000). L-neuron: A modeling tool for the efficient generation and parsimonious description of dendritic morphology. Neurocomputing, 32\u201333, 1003\u20131011. https:\/\/doi.org\/10.1016\/S0925-2312(00)00272-1","journal-title":"Neurocomputing"},{"issue":"5","key":"9761_CR3","doi-asserted-by":"publisher","first-page":"677","DOI":"10.15252\/embr.201744010","volume":"18","author":"S Berlin","year":"2017","unstructured":"Berlin, S., & Isacoff, E. Y. (2017). Synapses in the spotlight with synthetic optogenetics. EMBO Reports, 18(5), 677\u2013692. https:\/\/doi.org\/10.15252\/embr.201744010","journal-title":"EMBO Reports"},{"key":"9761_CR4","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2103.09396","author":"A Borji","year":"2021","unstructured":"Borji, A. (2021). Pros and cons of GAN evaluation measures: New developments (Version 3). ArXiv. https:\/\/doi.org\/10.48550\/ARXIV.2103.09396","journal-title":"ArXiv"},{"key":"9761_CR5","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1608.06019","author":"K Bousmalis","year":"2016","unstructured":"Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., & Erhan, D. (2016). Domain separation networks (Version 1). ArXiv. https:\/\/doi.org\/10.48550\/ARXIV.1608.06019","journal-title":"ArXiv"},{"issue":"2","key":"9761_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2907070","volume":"49","author":"P Branco","year":"2017","unstructured":"Branco, P., Torgo, L., & Ribeiro, R. P. (2017). A survey of predictive modeling on imbalanced domains. ACM Computing Surveys, 49(2), 1\u201350. https:\/\/doi.org\/10.1145\/2907070","journal-title":"ACM Computing Surveys"},{"key":"9761_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10904-1","author":"C Bulut","year":"2024","unstructured":"Bulut, C., & Arslan, E. (2024). Comparison of the impact of dimensionality reduction and data splitting on classification performance in credit risk assessment. Artificial Intelligence Review. https:\/\/doi.org\/10.1007\/s10462-024-10904-1","journal-title":"Artificial Intelligence Review"},{"issue":"7628","key":"9761_CR8","doi-asserted-by":"publisher","DOI":"10.1038\/nature20118","volume":"539","author":"M Capogrosso","year":"2016","unstructured":"Capogrosso, M., Milekovic, T., Borton, D., Wagner, F., Moraud, E. M., Mignardot, J.-B., Buse, N., Gandar, J., Barraud, Q., Xing, D., Rey, E., Duis, S., Jianzhong, Y., Ko, W. K. D., Li, Q., Detemple, P., Denison, T., Micera, S., Bezard, E., & Courtine, G. (2016). A brain\u2013spine interface alleviating gait deficits after spinal cord injury in primates. Nature, 539(7628), Article 7628. https:\/\/doi.org\/10.1038\/nature20118","journal-title":"Nature"},{"key":"9761_CR9","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority Over-sampling technique. Journal of Artificial Intelligence Research, 16, 321\u2013357. https:\/\/doi.org\/10.1613\/jair.953","journal-title":"Journal of Artificial Intelligence Research"},{"issue":"10","key":"9761_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/nn.2401","volume":"12","author":"G Courtine","year":"2009","unstructured":"Courtine, G., Gerasimenko, Y., Van Den Brand, R., Yew, A., Musienko, P., Zhong, H., Song, B., Ao, Y., Ichiyama, R. M., Lavrov, I., Roy, R. R., Sofroniew, M. V., & Edgerton, V. R. (2009). Transformation of nonfunctional spinal circuits into functional states after the loss of brain input. Nature Neuroscience, 12(10), Article 10. https:\/\/doi.org\/10.1038\/nn.2401","journal-title":"Nature Neuroscience"},{"issue":"8","key":"9761_CR11","doi-asserted-by":"publisher","first-page":"e1000877","DOI":"10.1371\/journal.pcbi.1000877","volume":"6","author":"H Cuntz","year":"2010","unstructured":"Cuntz, H., Forstner, F., Borst, A., & H\u00e4usser, M. (2010). One rule to grow them all: A general theory of neuronal branching and its practical application. PLoS Computational Biology, 6(8), e1000877. https:\/\/doi.org\/10.1371\/journal.pcbi.1000877","journal-title":"PLoS Computational Biology"},{"key":"9761_CR12","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1706.02633","author":"C Esteban","year":"2017","unstructured":"Esteban, C., Hyland, S. L., & R\u00e4tsch, G. (2017). Real-valued (Medical) time series generation with recurrent conditional GANs. arXiv. https:\/\/doi.org\/10.48550\/arXiv.1706.02633","journal-title":"arXiv"},{"issue":"2","key":"9761_CR13","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1038\/nbt.3443","volume":"34","author":"J Fuzik","year":"2016","unstructured":"Fuzik, J., Zeisel, A., M\u00e1t\u00e9, Z., Calvigioni, D., Yanagawa, Y., Szab\u00f3, G., Linnarsson, S., & Harkany, T. (2016). Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes. Nature Biotechnology, 34(2), 175\u2013183. https:\/\/doi.org\/10.1038\/nbt.3443","journal-title":"Nature Biotechnology"},{"issue":"S3","key":"9761_CR14","doi-asserted-by":"publisher","first-page":"S56","DOI":"10.1038\/nmeth.1436","volume":"7","author":"N Gehlenborg","year":"2010","unstructured":"Gehlenborg, N., O\u2019Donoghue, S. I., Baliga, N. S., Goesmann, A., Hibbs, M. A., Kitano, H., Kohlbacher, O., Neuweger, H., Schneider, R., Tenenbaum, D., & Gavin, A.-C. (2010). Visualization of omics data for systems biology. Nature Methods, 7(S3), S56\u2013S68. https:\/\/doi.org\/10.1038\/nmeth.1436","journal-title":"Nature Methods"},{"key":"9761_CR15","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1406.2661","author":"IJ Goodfellow","year":"2014","unstructured":"Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Networks (No. arXiv: 1406.2661). arXiv. https:\/\/doi.org\/10.48550\/arXiv.1406.2661","journal-title":"arXiv"},{"issue":"7","key":"9761_CR16","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1038\/s41593-019-0417-0","volume":"22","author":"NW Gouwens","year":"2019","unstructured":"Gouwens, N. W., Sorensen, S. A., Berg, J., Lee, C., Jarsky, T., Ting, J., Sunkin, S. M., Feng, D., Anastassiou, C. A., Barkan, E., Bickley, K., Blesie, N., Braun, T., Brouner, K., Budzillo, A., Caldejon, S., Casper, T., Castelli, D., Chong, P., & Koch, C. (2019). Classification of electrophysiological and morphological neuron types in the mouse visual cortex. Nature Neuroscience, 22(7), 1182\u20131195. https:\/\/doi.org\/10.1038\/s41593-019-0417-0","journal-title":"Nature Neuroscience"},{"key":"9761_CR17","doi-asserted-by":"publisher","DOI":"10.1101\/2020.02.03.932244","author":"NW Gouwens","year":"2020","unstructured":"Gouwens, N. W., Sorensen, S. A., Baftizadeh, F., Budzillo, A., Lee, B. R., Jarsky, T., Alfiler, L., Arkhipov, A., Baker, K., Barkan, E., Berry, K., Bertagnolli, D., Bickley, K., Bomben, J., Braun, T., Brouner, K., Casper, T., Crichton, K., Daigle, T. L., & Zeng, H. (2020). Toward an integrated classification of neuronal cell types: Morphoelectric and transcriptomic characterization of individual GABAergic cortical neurons. Cold Spring Harbor Laboratory. https:\/\/doi.org\/10.1101\/2020.02.03.932244","journal-title":"Cold Spring Harbor Laboratory"},{"issue":"3","key":"9761_CR18","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s12021-008-9030-1","volume":"6","author":"M Halavi","year":"2008","unstructured":"Halavi, M., Polavaram, S., Donohue, D. E., Hamilton, G., Hoyt, J., Smith, K. P., & Ascoli, G. A. (2008). NeuroMorpho.Org implementation of digital neuroscience: Dense coverage and integration with the NIF. Neuroinformatics, 6(3), 241. https:\/\/doi.org\/10.1007\/s12021-008-9030-1","journal-title":"Neuroinformatics"},{"key":"9761_CR19","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1806.01875","author":"KG Hartmann","year":"2018","unstructured":"Hartmann, K. G., Schirrmeister, R. T., & Ball, T. (2018). EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals. arXiv. https:\/\/doi.org\/10.48550\/arXiv.1806.01875","journal-title":"arXiv"},{"key":"9761_CR20","doi-asserted-by":"publisher","first-page":"1303993","DOI":"10.3389\/fninf.2024.1303993","volume":"18","author":"VR Haynes","year":"2024","unstructured":"Haynes, V. R., Zhou, Y., & Crook, S. M. (2024). Discovering optimal features for neuron-type identification from extracellular recordings. Frontiers in Neuroinformatics, 18, 1303993. https:\/\/doi.org\/10.3389\/fninf.2024.1303993","journal-title":"Frontiers in Neuroinformatics"},{"key":"9761_CR21","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2006.11239","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models (Version 2). arXiv. https:\/\/doi.org\/10.48550\/ARXIV.2006.11239","journal-title":"arXiv"},{"issue":"5","key":"9761_CR22","doi-asserted-by":"publisher","first-page":"1831","DOI":"10.1152\/jn.00680.2018","volume":"121","author":"X Jia","year":"2019","unstructured":"Jia, X., Siegle, J. H., Bennett, C., Gale, S. D., Denman, D. J., Koch, C., & Olsen, S. R. (2019). High-density extracellular probes reveal dendritic backpropagation and facilitate neuron classification. Journal of Neurophysiology, 121(5), 1831\u20131847. https:\/\/doi.org\/10.1152\/jn.00680.2018","journal-title":"Journal of Neurophysiology"},{"issue":"7936","key":"9761_CR23","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-022-05385-7","volume":"611","author":"C Kathe","year":"2022","unstructured":"Kathe, C., Skinnider, M. A., Hutson, T. H., Regazzi, N., Gautier, M., Demesmaeker, R., Komi, S., Ceto, S., James, N. D., Cho, N., Baud, L., Galan, K., Matson, K. J. E., Rowald, A., Kim, K., Wang, R., Minassian, K., Prior, J. O., Asboth, L., & Courtine, G. (2022). The neurons that restore walking after paralysis. Nature, 611(7936), Article 7936. https:\/\/doi.org\/10.1038\/s41586-022-05385-7","journal-title":"Nature"},{"key":"9761_CR24","doi-asserted-by":"publisher","unstructured":"Kobyzev, I., Prince, S. J. D., & Brubaker, M. A. (2019). Normalizing Flows: An Introduction and Review of Current Methods. https:\/\/doi.org\/10.48550\/ARXIV.1908.09257","DOI":"10.48550\/ARXIV.1908.09257"},{"issue":"3","key":"9761_CR25","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s12021-009-9052-3","volume":"7","author":"RA Koene","year":"2009","unstructured":"Koene, R. A., Tijms, B., van Hees, P., Postma, F., de Ridder, A., Ramakers, G. J. A., van Pelt, J., & van Ooyen, A. (2009). NETMORPH: A framework for the stochastic generation of large scale neuronal networks with realistic neuron morphologies. Neuroinformatics, 7(3), 195\u2013210. https:\/\/doi.org\/10.1007\/s12021-009-9052-3","journal-title":"Neuroinformatics"},{"key":"9761_CR26","doi-asserted-by":"publisher","first-page":"e67490","DOI":"10.7554\/eLife.67490","volume":"10","author":"EK Lee","year":"2021","unstructured":"Lee, E. K., Balasubramanian, H., Tsolias, A., Anakwe, S. U., Medalla, M., Shenoy, K. V., & Chandrasekaran, C. (2021). Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex. eLife, 10, e67490. https:\/\/doi.org\/10.7554\/eLife.67490","journal-title":"eLife"},{"key":"9761_CR27","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1711.10337","author":"M Lucic","year":"2017","unstructured":"Lucic, M., Kurach, K., Michalski, M., Gelly, S., & Bousquet, O. (2017). Are GANs Created Equal? A Large-Scale Study (Version 4). arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1711.10337","journal-title":"arXiv"},{"issue":"6559","key":"9761_CR28","doi-asserted-by":"publisher","first-page":"eabg7285","DOI":"10.1126\/science.abg7285","volume":"373","author":"L Luo","year":"2021","unstructured":"Luo, L. (2021). Architectures of neuronal circuits. Science, 373(6559), eabg7285. https:\/\/doi.org\/10.1126\/science.abg7285","journal-title":"Science"},{"key":"9761_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-14018-z","author":"M Marouf","year":"2020","unstructured":"Marouf, M., Machart, P., Bansal, V., Kilian, C., Magruder, D. S., Krebs, C. F., & Bonn, S. (2020). Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nature Communications. https:\/\/doi.org\/10.1038\/s41467-019-14018-z","journal-title":"Nature Communications"},{"issue":"13","key":"9761_CR30","doi-asserted-by":"publisher","first-page":"R497","DOI":"10.1016\/j.cub.2004.06.035","volume":"14","author":"RH Masland","year":"2004","unstructured":"Masland, R. H. (2004). Neuronal cell types. Current Biology, 14(13), R497\u2013R500. https:\/\/doi.org\/10.1016\/j.cub.2004.06.035","journal-title":"Current Biology"},{"key":"9761_CR31","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1803.00338","author":"M Molano-Mazon","year":"2018","unstructured":"Molano-Mazon, M., Onken, A., Piasini, E., & Panzeri, S. (2018). Synthesizing realistic neural population activity patterns using generative adversarial networks (Version 2). ArXiv. https:\/\/doi.org\/10.48550\/ARXIV.1803.00338","journal-title":"ArXiv"},{"issue":"10","key":"9761_CR32","doi-asserted-by":"publisher","first-page":"3536","DOI":"10.1016\/j.celrep.2020.02.027","volume":"30","author":"CP Mosher","year":"2020","unstructured":"Mosher, C. P., Wei, Y., Kami\u0144ski, J., Nandi, A., Mamelak, A. N., Anastassiou, C. A., & Rutishauser, U. (2020). Cellular classes in the human brain revealed in vivo by Heartbeat-Related modulation of the extracellular action potential waveform. Cell Reports, 30(10), 3536\u20133551e6. https:\/\/doi.org\/10.1016\/j.celrep.2020.02.027","journal-title":"Cell Reports"},{"issue":"6","key":"9761_CR33","doi-asserted-by":"publisher","first-page":"111176","DOI":"10.1016\/j.celrep.2022.111176","volume":"40","author":"A Nandi","year":"2022","unstructured":"Nandi, A., Chartrand, T., Van Geit, W., Buchin, A., Yao, Z., Lee, S. Y., Wei, Y., Kalmbach, B., Lee, B., Lein, E., Berg, J., S\u00fcmb\u00fcl, U., Koch, C., Tasic, B., & Anastassiou, C. A. (2022). Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types. Cell Reports, 40(6), 111176. https:\/\/doi.org\/10.1016\/j.celrep.2022.111176","journal-title":"Cell Reports"},{"key":"9761_CR34","doi-asserted-by":"publisher","unstructured":"Kingma, D. P., & Welling, M. (2013). Auto-Encoding Variational Bayes (Version 11). arXiv. https:\/\/doi.org\/10.48550\/ARXIV.1312.6114","DOI":"10.48550\/ARXIV.1312.6114"},{"key":"9761_CR35","doi-asserted-by":"publisher","unstructured":"Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., & Lakshminarayanan, B. (2019). Normalizing Flows for Probabilistic Modeling and Inference. https:\/\/doi.org\/10.48550\/ARXIV.1912.02762","DOI":"10.48550\/ARXIV.1912.02762"},{"issue":"7879","key":"9761_CR36","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1038\/s41586-020-2907-3","volume":"598","author":"F Scala","year":"2021","unstructured":"Scala, F., Kobak, D., Bernabucci, M., Bernaerts, Y., Cadwell, C. R., Castro, J. R., Hartmanis, L., Jiang, X., Laturnus, S., Miranda, E., Mulherkar, S., Tan, Z. H., Yao, Z., Zeng, H., Sandberg, R., Berens, P., & Tolias, A. S. (2021). Phenotypic variation of transcriptomic cell types in mouse motor cortex. Nature, 598(7879), 144\u2013150. https:\/\/doi.org\/10.1038\/s41586-020-2907-3","journal-title":"Nature"},{"issue":"7729","key":"9761_CR37","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1038\/s41586-018-0654-5","volume":"563","author":"B Tasic","year":"2018","unstructured":"Tasic, B., Yao, Z., Graybuck, L. T., Smith, K. A., Nguyen, T. N., Bertagnolli, D., Goldy, J., Garren, E., Economo, M. N., Viswanathan, S., Penn, O., Bakken, T., Menon, V., Miller, J., Fong, O., Hirokawa, K. E., Lathia, K., Rimorin, C., Tieu, M., & Zeng, H. (2018). Shared and distinct transcriptomic cell types across neocortical areas. Nature, 563(7729), 72\u201378. https:\/\/doi.org\/10.1038\/s41586-018-0654-5","journal-title":"Nature"},{"issue":"4\u20136","key":"9761_CR38","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1016\/j.neucom.2007.02.016","volume":"71","author":"B Torben-Nielsen","year":"2008","unstructured":"Torben-Nielsen, B., Tuyls, K., & Postma, E. (2008). EvOL-Neuron: Neuronal morphology generation. Neurocomputing, 71(4\u20136), 963\u2013972. https:\/\/doi.org\/10.1016\/j.neucom.2007.02.016","journal-title":"Neurocomputing"},{"issue":"18","key":"9761_CR39","doi-asserted-by":"publisher","first-page":"2973","DOI":"10.1016\/j.cub.2019.07.051","volume":"29","author":"C Trainito","year":"2019","unstructured":"Trainito, C., Von Nicolai, C., Miller, E. K., & Siegel, M. (2019). Extracellular spike waveform dissociates four functionally distinct cell classes in primate cortex. Current Biology, 29(18), 2973-2982.e5. https:\/\/doi.org\/10.1016\/j.cub.2019.07.051","journal-title":"Current Biology"},{"issue":"6085","key":"9761_CR40","doi-asserted-by":"publisher","DOI":"10.1126\/science.1217416","volume":"336","author":"R Van Den Brand","year":"2012","unstructured":"Van Den Brand, R., Heutschi, J., Barraud, Q., DiGiovanna, J., Bartholdi, K., Huerlimann, M., Friedli, L., Vollenweider, I., Moraud, E. M., Duis, S., Dominici, N., Micera, S., Musienko, P., & Courtine, G. (2012). Restoring voluntary control of locomotion after paralyzing spinal cord injury. Science, 336(6085), Article 6085. https:\/\/doi.org\/10.1126\/science.1217416","journal-title":"Science"},{"key":"9761_CR41","doi-asserted-by":"publisher","unstructured":"Vasques, X., Vanel, L., Villette, G., & Cif, L. (2016). Morphological neuron classification using machine learning. Frontiers in Neuroanatomy, 10. https:\/\/doi.org\/10.3389\/fnana.2016.00102","DOI":"10.3389\/fnana.2016.00102"},{"issue":"1","key":"9761_CR42","doi-asserted-by":"publisher","first-page":"11541","DOI":"10.1038\/s41598-023-38558-z","volume":"13","author":"X Vasques","year":"2023","unstructured":"Vasques, X., Paik, H., & Cif, L. (2023). Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M-type classification. Scientific Reports, 13(1), 11541. https:\/\/doi.org\/10.1038\/s41598-023-38558-z","journal-title":"Scientific Reports"},{"issue":"7729","key":"9761_CR43","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0649-2","volume":"563","author":"FB Wagner","year":"2018","unstructured":"Wagner, F. B., Mignardot, J.-B., Le Goff-Mignardot, C. G., Demesmaeker, R., Komi, S., Capogrosso, M., Rowald, A., Se\u00e1\u00f1ez, I., Caban, M., Pirondini, E., Vat, M., McCracken, L. A., Heimgartner, R., Fodor, I., Watrin, A., Seguin, P., Paoles, E., Van Den Keybus, K., Eberle, G., & Courtine, G. (2018). Targeted neurotechnology restores walking in humans with spinal cord injury. Nature, 563(7729), Article 7729. https:\/\/doi.org\/10.1038\/s41586-018-0649-2","journal-title":"Nature"},{"key":"9761_CR44","unstructured":"Wanhainen, E., & Adamsson, J. (2021). Generating Realistic Neuronal Morphologies in 3D using a Generative Adversarial Network [Royal Institute of Technology, School of Electrical Engineering and Computer Science]. https:\/\/www.diva-portal.org\/smash\/get\/diva2:1593469\/FULLTEXT01.pdf"},{"issue":"2","key":"9761_CR45","doi-asserted-by":"publisher","DOI":"10.1038\/nm.4025","volume":"22","author":"N Wenger","year":"2016","unstructured":"Wenger, N., Moraud, E. M., Gandar, J., Musienko, P., Capogrosso, M., Baud, L., Le Goff, C. G., Barraud, Q., Pavlova, N., Dominici, N., Minev, I. R., Asboth, L., Hirsch, A., Duis, S., Kreider, J., Mortera, A., Haverbeck, O., Kraus, S., Schmitz, F., & Courtine, G. (2016). Spatiotemporal neuromodulation therapies engaging muscle synergies improve motor control after spinal cord injury. Nature Medicine, 22(2), Article 2. https:\/\/doi.org\/10.1038\/nm.4025","journal-title":"Nature Medicine"},{"issue":"12","key":"9761_CR46","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1038\/s41593-020-0685-8","volume":"23","author":"R Yuste","year":"2020","unstructured":"Yuste, R., Hawrylycz, M., Aalling, N., Aguilar-Valles, A., Arendt, D., Arma\u00f1anzas, R., Ascoli, G. A., Bielza, C., Bokharaie, V., Bergmann, T. B., Bystron, I., Capogna, M., Chang, Y., Clemens, A., De Kock, C. P. J., DeFelipe, J., Santos, D., Dunville, S. E., Feldmeyer, K., & Lein, D., E (2020). A community-based transcriptomics classification and nomenclature of neocortical cell types. Nature Neuroscience, 23(12), 1456\u20131468. https:\/\/doi.org\/10.1038\/s41593-020-0685-8","journal-title":"Nature Neuroscience"},{"issue":"15","key":"9761_CR47","doi-asserted-by":"publisher","first-page":"2739","DOI":"10.1016\/j.cell.2022.06.031","volume":"185","author":"H Zeng","year":"2022","unstructured":"Zeng, H. (2022). What is a cell type and how to define it? Cell, 185(15), 2739\u20132755. https:\/\/doi.org\/10.1016\/j.cell.2022.06.031","journal-title":"Cell"},{"issue":"1","key":"9761_CR48","doi-asserted-by":"publisher","first-page":"7291","DOI":"10.1038\/s41598-021-86780-4","volume":"11","author":"T Zhang","year":"2021","unstructured":"Zhang, T., Zeng, Y., Zhang, Y., Zhang, X., Shi, M., Tang, L., Zhang, D., & Xu, B. (2021). Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks. Scientific Reports, 11(1), 7291. https:\/\/doi.org\/10.1038\/s41598-021-86780-4","journal-title":"Scientific Reports"},{"key":"9761_CR49","doi-asserted-by":"publisher","DOI":"10.3892\/wasj.2025.315","author":"V Zogopoulos","year":"2025","unstructured":"Zogopoulos, V., Tsotra, I., Spandidos, D., Iconomidou, V., & Michalopoulos, I. (2025). Single\u2011cell RNA sequencing data dimensionality reduction (Review). World Academy of Sciences Journal. https:\/\/doi.org\/10.3892\/wasj.2025.315","journal-title":"World Academy of Sciences Journal"}],"container-title":["Neuroinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-025-09761-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12021-025-09761-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-025-09761-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T02:07:12Z","timestamp":1776737232000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12021-025-09761-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,27]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["9761"],"URL":"https:\/\/doi.org\/10.1007\/s12021-025-09761-2","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-7545694\/v1","asserted-by":"object"}]},"ISSN":["1559-0089"],"issn-type":[{"value":"1559-0089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,27]]},"assertion":[{"value":"5 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"2"}}