{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T19:02:28Z","timestamp":1779994948318,"version":"3.53.1"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032273130","type":"print"},{"value":"9783032273147","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-27314-7_38","type":"book-chapter","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T18:28:51Z","timestamp":1779992931000},"page":"410-425","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in Cortical Visual Prostheses: From Phosphene Encoding to\u00a0Closed-Loop Neuroprosthetic Vision"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1921-3044","authenticated-orcid":false,"given":"Alicia","family":"Aniorte","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6861-1596","authenticated-orcid":false,"given":"Marta","family":"Gea","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4397-6682","authenticated-orcid":false,"given":"Javier","family":"Garrig\u00f3s","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6971-8278","authenticated-orcid":false,"given":"Jos\u00e9 J.","family":"Mart\u00ednez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4613-6101","authenticated-orcid":false,"given":"Jos\u00e9 M.","family":"Ferr\u00e1ndez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"38_CR1","unstructured":"Clinical trial - a phase i feasibility study of an intracortical visual prosthesis (ICVP) for people with blindness (ICVP). https:\/\/clinicaltrials.gov\/study\/NCT04634383"},{"key":"38_CR2","unstructured":"Clinical trial - development of a cortical visual neuroprosthesis for the blind. https:\/\/www.trialx.com\/clinical-trials\/listings\/224479\/development-of-a-cortical-visual-neuroprosthesis-for-the-blind\/"},{"key":"38_CR3","unstructured":"Cortigent orion. https:\/\/www.cortigent.com\/orion"},{"key":"38_CR4","unstructured":"Elvis tech. https:\/\/elvis-tech.ru\/"},{"key":"38_CR5","unstructured":"Revision implant. https:\/\/www.revision-implant.com\/"},{"key":"38_CR6","unstructured":"Transparency for machine learning-enabled medical devices: Guiding principles. Tech. rep. (2024). https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/transparency-machine-learning-enabled-medical-devices-guiding-principles, https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/transparency-machine-learning-enabled-medical-devices-guiding-principles"},{"key":"38_CR7","doi-asserted-by":"publisher","unstructured":"Ayton, L.N., et al.: An update on retinal prostheses (2020). https:\/\/doi.org\/10.1016\/j.clinph.2019.11.029","DOI":"10.1016\/j.clinph.2019.11.029"},{"key":"38_CR8","doi-asserted-by":"publisher","unstructured":"Barbruni, G.L., et al.: A wearable real-time system for simultaneous wireless power and data transmission to cortical visual prosthesis. IEEE Trans. Biomed. Circuits Syst. 18(3), 580\u2013591 (2024). https:\/\/doi.org\/10.1109\/TBCAS.2024.3357626","DOI":"10.1109\/TBCAS.2024.3357626"},{"key":"38_CR9","doi-asserted-by":"publisher","unstructured":"Bourne, R.R., Steinmetz, J.D., Flaxman, S., Briant, P.S.: Trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the global burden of disease study. Lancet Global Health 9, e130\u2013e143 (2021). https:\/\/doi.org\/10.1016\/S2214-109X(20)30425-3","DOI":"10.1016\/S2214-109X(20)30425-3"},{"key":"38_CR10","doi-asserted-by":"crossref","unstructured":"Brindley, G.S., Lewin, W.S.: The sensations produced by electrical stimulation of the visual cortex. Tech. rep. (1968)","DOI":"10.1113\/jphysiol.1968.sp008519"},{"key":"38_CR11","doi-asserted-by":"publisher","unstructured":"Chen, X., Wang, F., Fernandez, E., Roelfsema, P.R.: Shape perception via a high-channel-count neuroprosthesis in monkey visual cortex. Science (2020). https:\/\/doi.org\/10.1126\/science.abd7435, http:\/\/science.sciencemag.org\/","DOI":"10.1126\/science.abd7435"},{"key":"38_CR12","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"38_CR13","doi-asserted-by":"publisher","unstructured":"Deng, J., Guo, J., Yang, J., Xue, N., Kotsia, I., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3087709","DOI":"10.1109\/TPAMI.2021.3087709"},{"key":"38_CR14","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I., Zafeiriou, S.: Retinaface: single-stage dense face localisation in the wild. arXiv:1905.00641 (2019)","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"38_CR15","doi-asserted-by":"crossref","unstructured":"Dobelle, W., Mladejovsky, M., Girvin, J.: Artificial vision for the blind: electrical stimulation of visual cortex offers hope for a functional prosthesis. Tech. rep. (1974)","DOI":"10.1126\/science.183.4123.440"},{"key":"38_CR16","doi-asserted-by":"publisher","unstructured":"Donati, E., Valle, G.: Neuromorphic hardware for somatosensory neuroprostheses. Nat. Commun. 15 (2024). https:\/\/doi.org\/10.1038\/s41467-024-44723-3","DOI":"10.1038\/s41467-024-44723-3"},{"key":"38_CR17","doi-asserted-by":"publisher","unstructured":"Downing, M., Peng, M., Granley, J., Beyeler, M., Bultan, T.: Fuzzing the brain: automated stress testing for the safety of ml-driven neurostimulation. J. Neural Eng. 23, 026004 (2026). https:\/\/doi.org\/10.1088\/1741-2552\/ae4927","DOI":"10.1088\/1741-2552\/ae4927"},{"key":"38_CR18","doi-asserted-by":"publisher","unstructured":"Eichler, G., Gilhotra, Y., Zeng, N., Kim, M., Shepard, K., Carloni, L.: Mindful: Safe, implantable, large-scale brain-computer interfaces from a system-level design perspective. In: Proceedings of the Annual International Symposium on Microarchitecture, MICRO, vol. Part of 213862, pp. 1672\u20131689. IEEE Computer Society (2025). https:\/\/doi.org\/10.1145\/3725843.3756032","DOI":"10.1145\/3725843.3756032"},{"key":"38_CR19","unstructured":"Fernandez, E., Normann, R.: Introduction to visual prosthetics. https:\/\/www.webvision.pitt.edu\/book\/part-xv-prosthetics\/introduction-to-visual-prostheses-by-eduardo-fernandez-and-richard-normann\/"},{"key":"38_CR20","doi-asserted-by":"publisher","unstructured":"Fernandez, E., Robles, J.A.: Advances and challenges in the development of visual prostheses. PLoS Biol. 22 (2024). https:\/\/doi.org\/10.1371\/journal.pbio.3002896","DOI":"10.1371\/journal.pbio.3002896"},{"key":"38_CR21","doi-asserted-by":"publisher","unstructured":"Fern\u00e1ndez, E., Alfaro, A., Gonz\u00e1lez-L\u00f3pez, P.: Toward long-term communication with the brain in the blind by intracortical stimulation: challenges and future prospects. Front. Neurosci. 14 (2020). https:\/\/doi.org\/10.3389\/fnins.2020.00681","DOI":"10.3389\/fnins.2020.00681"},{"key":"38_CR22","doi-asserted-by":"publisher","unstructured":"Fern\u00e1ndez, E., et al.: Visual percepts evoked with an intracortical 96-channel microelectrode array inserted in human occipital cortex. J. Clin. Invest. 131 (2021). https:\/\/doi.org\/10.1172\/JCI151331","DOI":"10.1172\/JCI151331"},{"key":"38_CR23","doi-asserted-by":"publisher","unstructured":"Fine, I., Boynton, G.M.: A virtual patient simulation modeling the neural and perceptual effects of human visual cortical stimulation, from pulse trains to percepts. Tech. rep. (2024). https:\/\/doi.org\/10.1038\/s41598-024-65337-1","DOI":"10.1038\/s41598-024-65337-1"},{"key":"38_CR24","unstructured":"Ghaffari, D.H.: Improving the resolution of prosthetic vision through stimulus parameter optimization. Ph. D. thesis (2021)"},{"key":"38_CR25","doi-asserted-by":"crossref","unstructured":"Grani, F., Soto-S\u00e1nchez, C., Doblado, A.R., Peco, R.L., Gonzalez-Lopez, P., Fernandez, E.: Neural correlates of phosphene perception in blind individuals: a step toward a bidirectional cortical visual prosthesis. Tech. rep. (2025). https:\/\/www.science.org","DOI":"10.1126\/sciadv.adv8846"},{"key":"38_CR26","doi-asserted-by":"publisher","unstructured":"van\u00a0der Grinten, M., et al.: Towards biologically plausible phosphene simulation for the differentiable optimization of visual cortical prostheses. eLife 13 (2024). https:\/\/doi.org\/10.7554\/eLife.85812","DOI":"10.7554\/eLife.85812"},{"key":"38_CR27","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. arXiv:1703.06870 (2018)","DOI":"10.1109\/ICCV.2017.322"},{"key":"38_CR28","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"38_CR29","doi-asserted-by":"publisher","unstructured":"Leong, F., Rahmani, B., Psaltis, D., Moser, C., Ghezzi, D.: An actor-model framework for visual sensory encoding. Nat. Commun. 15 (2024). https:\/\/doi.org\/10.1038\/s41467-024-45105-5","DOI":"10.1038\/s41467-024-45105-5"},{"key":"38_CR30","doi-asserted-by":"publisher","unstructured":"Lozano, A., Soto-S\u00e1nchez, C., Garrig\u00f3s, J., Mart\u00ednez, J.J., Ferr\u00e1ndez, J.M., Fern\u00e1ndez, E.: A 3D convolutional neural network to model retinal ganglion cell\u2019s responses to light patterns in mice. Int. J. Neural Syst. 28 (2018). https:\/\/doi.org\/10.1142\/S0129065718500430","DOI":"10.1142\/S0129065718500430"},{"key":"38_CR31","doi-asserted-by":"publisher","unstructured":"Lozano, A., et al.: Neurolight: a deep learning neural interface for cortical visual prostheses. Int. J. Neural Syst. 30 (2020). https:\/\/doi.org\/10.1142\/S0129065720500458","DOI":"10.1142\/S0129065720500458"},{"key":"38_CR32","unstructured":"M, S.S., Jadala, V.C., Vikkurty, S.: Design and evaluation of hybrid explainable ai interfaces for vision restoration in next-generation retinal prosthetics. J. Theor. Appl. Inf. Technol. 15 (2025). www.jatit.org"},{"key":"38_CR33","doi-asserted-by":"publisher","unstructured":"Mart\u00ednez, J.J., Toledo, F.J., Fern\u00e1ndez, E., Ferr\u00e1ndez, J.M.: A retinomorphic architecture based on discrete-time cellular neural networks using reconfigurable computing. Neurocomputing 71, 766\u2013775 (2008). https:\/\/doi.org\/10.1016\/j.neucom.2007.06.011","DOI":"10.1016\/j.neucom.2007.06.011"},{"key":"38_CR34","doi-asserted-by":"crossref","unstructured":"Ortiz-Rios, M., Agayby, B., Balezeau, F., Haag, M., Rima, S., Schmid, M.C.: Optogenetic stimulation of primate v1 reveals local laminar and large-scale cortical networks related to perceptual phosphenes (2021). http:\/\/biorxiv.org\/lookup\/doi\/10.1101\/2021.06.01.446505","DOI":"10.1101\/2021.06.01.446505"},{"key":"38_CR35","doi-asserted-by":"publisher","unstructured":"Pogoncheff, G., Hu, Z., Rokem, A., Beyeler, M.: Explainable machine learning predictions of perceptual sensitivity for retinal prostheses. J. Neural Eng. 21 (2024). https:\/\/doi.org\/10.1088\/1741-2552\/ad310f","DOI":"10.1088\/1741-2552\/ad310f"},{"key":"38_CR36","doi-asserted-by":"publisher","first-page":"95","DOI":"10.2147\/EB.S524322","volume":"17","author":"I Sarbout","year":"2025","unstructured":"Sarbout, I., et al.: Visual prostheses in the era of artificial intelligence technology. Eye and Brain 17, 95\u2013113 (2025). https:\/\/doi.org\/10.2147\/EB.S524322","journal-title":"Eye and Brain"},{"key":"38_CR37","doi-asserted-by":"crossref","unstructured":"Schoinas, E., Rastogi, A., Carter, A., Granley, J., Beyeler, M.: Evaluating deep human-in-the-loop optimization for retinal implants using sighted participants. arXiv:2502.00177 (2025)","DOI":"10.1109\/EMBC58623.2025.11253762"},{"key":"38_CR38","doi-asserted-by":"publisher","unstructured":"de\u00a0Ruyter\u00a0van Steveninck, J., G\u00fc\u00e7l\u00fc, U., van Wezel, R., van Gerven, M.: End-to-end optimization of prosthetic vision. J. Vis. 22 (2022). https:\/\/doi.org\/10.1167\/jov.22.2.20","DOI":"10.1167\/jov.22.2.20"},{"key":"38_CR39","doi-asserted-by":"publisher","unstructured":"Toledo-Moreo, F.J., Mart\u00ednez-Alvarez, J.J., Garrig\u00f3s-Guerrero, J., Ferr\u00e1ndez-Vicente, J.M.: FPGA-based architecture for the real-time computation of 2-d convolution with large kernel size. J. Syst. Architect. 58, 277\u2013285 (2012). https:\/\/doi.org\/10.1016\/j.sysarc.2012.06.002","DOI":"10.1016\/j.sysarc.2012.06.002"},{"key":"38_CR40","doi-asserted-by":"publisher","unstructured":"Tsirtsakis, P., Zacharis, G., Maraslidis, G.S., Fragulis, G.F.: Deep learning for object recognition: a comprehensive review of models and algorithms. Int. J. Cogn. Comput. Eng. 6, 298\u2013312 (2025). https:\/\/doi.org\/10.1016\/j.ijcce.2025.01.004","DOI":"10.1016\/j.ijcce.2025.01.004"},{"key":"38_CR41","doi-asserted-by":"publisher","first-page":"3998","DOI":"10.1109\/TNSRE.2025.3615286","volume":"33","author":"D Waclawczyk","year":"2025","unstructured":"Waclawczyk, D., Soo, L., Ruiz, R.M., Caspi, A., Fernandez, E.: Integrating eye-tracking with cortical visual prostheses in patients without eyes: a case study. IEEE Trans. Neural Syst. Rehabil. Eng. 33, 3998\u20134007 (2025). https:\/\/doi.org\/10.1109\/TNSRE.2025.3615286","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"38_CR42","doi-asserted-by":"crossref","unstructured":"Xie, S., Tu, Z.: Holistically-nested edge detection. arXiv:1504.06375 (2015)","DOI":"10.1109\/ICCV.2015.164"},{"key":"38_CR43","doi-asserted-by":"publisher","unstructured":"Y\u00fccel, E.I., et al.: Factors affecting two-point discrimination in argus ii patients. Front. Neurosci. 16 (2022). https:\/\/doi.org\/10.3389\/fnins.2022.901337","DOI":"10.3389\/fnins.2022.901337"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence for Neuroscience, Mental Health, and Neurodegenerative Disorders"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-27314-7_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T18:28:54Z","timestamp":1779992934000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-27314-7_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032273130","9783032273147"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-27314-7_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"29 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"IWINAC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on the Interplay Between Natural and Artificial Computation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canary Islands","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 May 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwinac2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwinac.eu\/iwinac.org\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}