Inteligencia artificial y neurotecnologías
Resumen
El 2 de abril de 2013, el presidente de los EE. UU. Barack Obama, anunció la Iniciativa BRAIN (Brain Research through Advancing Innovative Neurotechnologies) (The White House, 2013). Su propósito era mapear y comprender la actividad cerebral, un objetivo análogo al que The Human Genome Project llevó a cabo años antes (1990-2003) cuando, gracias a la colaboración internacional, se logró mapear y secuenciar el genoma humano por primera vez. Desde 2013 en adelante se han financiado proyectos para investigar el cerebro en todo el planeta (EE.UU; Europa, China, Japón, Australia, Canadá y Corea del Sur). El foco de dichas investigaciones radica en el desarrollo y aplicación de neurotecnologías para entender las complejas dinámicas de los circuitos de la actividad neuronal y cómo éstas dan origen a nuestra cognición y conducta. Dichas tecnologías de alto rendimiento modelan y simulan informáticamente el cerebro humano integrando datos masivos, los cuales, aportan a los investigadores nuevas herramientas matemáticas para enfrentar diversas enfermedades neurológicas, neurodegenerativas, y otros trastornos (European Commission, 2021).
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Colucci, A., Vermehren, M., Cavallo, A., Angerhöfer, C., Peekhaus, N., Zollo, L., Kim, W., Paik, N., & Soekadar, S. (2022). Brain–Computer Interface-Controlled Exoskeletons in Clinical Neurorehabilitation: Ready or Not?. Neurorehabilitation and Neural Repair, 36, 747 - 756. https://doi.org/10.1177/15459683221138751
Cortina, A. (2001). Ética aplicada y democracia radical. Madrid: Tecnos
Dawkins, R. (1976). The selfish gene. New York: Oxford University Press
Défossez, A., Caucheteux, C., Rapin, J., Kabeli, O., & King, J. (2022). Decoding speech from non-invasive brain recordings. ArXiv, abs/2208.12266
European Commission. (2021). The Human Brain Project: A European flagship project. https://digital-strategy.ec.europa.eu/en/news/final-human-brain-project-summit-achievements-and-future-digital-brain-research
Francisco. (2015). Laudato Si: Sobre el cuidado de la casa común. https://www.vatican.va/content/francesco/es/encyclicals/documents/papa-francesco_20150524_enciclica-laudato-si.html
Genser, J., Herrmann, S. & Yuste, R. (2022). International Human Rights Protection Gaps in the Age of Neurotechnology. https://collimateur.uqam.ca/wp-content/uploads/sites/11/2022/09/NeurorightsFoundation PU BLICAnalysis5.6.22.PDF
Giansanti, D. (2023). An Umbrella Review of the Fusion of fMRI and AI in Autism. Diagnostics, 13. https://doi.org/10.3390/diagnostics13233552)
Hamilton, W.D. (1964). The genetical evolution of social behaviour. J Theo Biology7, 1-52
Hauser, M. (2008). La mente moral. Cómo la naturaleza ha desarrollado nuestro sentido delbien y del mal (Miguel Candel trad.). Barcelona: Paidós
Janapati, R., Dalal, V., Kumar, G. M., Anuradha, P., & Shekar, P. V. R. (2022). Web interface applications controllers used by autonomous EEG-BCI technologies. AIP Conference Proceedings, 2418, 030038. https://doi.org/10.1063/5.0081780
Julià-Pijoan, M. (2022). Sobre la valoración judicial de las neuroimágenes. Rev. chil. derecho vol.49 no.3 Santiago dic. 2022. https://www.scielo.cl/scielo.php?pid=S0718-34372022000300009&script=sci_arttext
Julià-Pijoan, M. (2023). La prueba penal de los estados mentales desde la “neurotecnología”: ¿ya es una realidad?. Polít. crim. vol.18 no.35 Santiago jul. 2023. https://www.scielo.cl/scielo.php?pid=S0718-33992023000100091&script=sci_arttext
Kadry, S., Taniar, D., Damasevicius, R., & Rajinikanth, V. (2021). Automated Detection of Schizophrenia from Brain MRI Slices using Optimized Deep-Features. 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), 1-5. https://doi.org/10.1109/ICBSII51839.2021.9445133)
Katsnelson, A. (2021). Criminalidad: ¿las imágenes del cerebro sirven de prueba? https://courier.unesco.org/es/articles/criminalidad-las-imagenes-del-cerebro-sirven-de-prueba
Kazazian, K., Edlow, B. L., & Owen, A. M. (2024). Detecting awareness after acute brain injury. The Lancet Neurology, 23, 836–844. https://doi.org/10.1016/S1474-4422(24)00456-7
Kumari, N., Anwar, S. & Bhattacharjee, V. (2022). Automated visual stimuli evoked multi-channel EEG signal classification using EEGCapsNet. Pattern Recognition Letters 153, 29–35.www.doi.org/10.1016/j.patrec.2021.11.019
LHF Labs. (2023). El sesgo en los modelos de lenguaje. https://www.lhf.ai/el-sesgo-en-los-modelos-de-lenguaje/
Li, C., Li, W., Liu, C., Zheng, H., Cai, J., & Wang, S. (2022). Artificial intelligence in multi-parametric magnetic resonance imaging: A review. Medical physics. https://doi.org/10.1002/mp.15936
McDuff, D., Schaekermann, M., Tu, T., Palepu, A., Wang, A., Garrison, J., Singhal, K., Sharma, Y., Azizi, S., Kulkarni, K., Hou, L., Cheng, Y., Liu, Y., Mahdavi, S., Prakash, S., Pathak, A., Semturs, C., Patel, S., Webster, D., Dominowska, E., Gottweis, J., Barral, J., Chou, K., Corrado, G., Matias, Y., Sunshine, J., Karthikesalingam, A., & Natarajan, V. (2023). Towards Accurate Differential Diagnosis with Large Language Models. ArXiv, abs/2312.00164. https://doi.org/10.48550/arXiv.2312.00164
Metzger, S. L., Littlejohn, K. T., Silva, A. B., Moses, D. A., Seaton, M. P., Wang, R., Dougherty, M. E., Liu, J. R., Wu, P., Berger, M. A., Zhuravleva, I., Tu-Chan, A., Ganguly, K., Anumanchipalli, G. K., & Chang, E. F. (2023). A high-performance neuroprosthesis for speech decoding and avatar control.Nature, 620(7976), 1037–1046. https://doi.org/10.1038/s41586-023-06443-4
Moses, D. A., Metzger, S. L., Liu, J. R., Anumanchipalli, G. K., Makin, J. G., Sun, P. F., Chartier, J., Dougherty, M. E., Liu, P. M., Abrams, G. M., Tu-Chan, A., Ganguly, K., & Chang, E. F. (2021). Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria. The New England journal of medicine, 385(3), 217–227. https://doi.org/10.1056/NEJMoa2027540
Precision Neuroscience. (2024, 7 de octubre). Breakthrough in brain–computer interface technology: A new era of neural decoding at the Society for Neuroscience annual meeting Neuroscience 2024. GlobeNewswire. https://www.globenewswire.com/news-release/2024/10/07/2959101/0/en/Breakthrough-in-Brain-Computer-Interface-Technology-A-New-Era-of-Neural-Decoding-at-the-Society-for-Neuroscience-Annual-Meeting-Neuroscience-2024.html
Rapeaux, A., & Constandinou, T.G. (2021). Implantable brain machine interfaces: first-in-human studies, technology challenges and trends. Current opinion in biotechnology, 72, 102-111
Roskies, A. (2002). Neuroethics for the New Millenium. Neuron, 35, 21-23
Saeidi, M., Karwowski, W., Farahani, F., Fiok, K., Taiar, R., Hancock, P., & Al-Juaid, A. (2021). Neural Decoding of EEG Signals with Machine Learning: A Systematic Review. Brain Sciences, 11. https://doi.org/10.3390/brainsci11111525
Samejima, S., Khorasani, A., Ranganathan, V., Nakahara, J., Tolley, N., Boissenin, A., Shalchyan, V., Daliri, M., Smith, J., & Moritz, C. (2021). Brain-Computer-Spinal Interface Restores Upper Limb Function After Spinal Cord Injury. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 1233-1242. https://doi.org/10.1109/TNSRE.2021.3090269
Sani, O. G., Yang, Y., Lee, M. B., Dawes, H. E., Chang, E. F., & Shanechi, M. M. (2018). Mood variations decoded from multi-site intracranial human brain activity. Nature Biotechnology, 36, 954–961. https://doi.org/10.1038/nbt.4200
Schwartzmann, B., Chatterjee, R., Vaghei, Y., Quilty, L. C., Allen, T. A., Arnott, S. R., Atluri, S., Blier, P., Dhami, P., Foster, J. A., Frey, B. N., Kloiber, S., Lam, R. W., Milev, R., Müller, D. J., Soares, C. N., Stengel, C., Parikh, S. V., Turecki, G., Uher, R., ... Farzan, F. (2024). Modulation of neural oscillations in escitalopram treatment: a Canadian biomarker integration network in depression study. Translational psychiatry, 14(1), 432. https://doi.org/10.1038/s41398-024-03110-8
Shanechi, M. M. (2019). Brain–machine interfaces from motor to mood. Nature Neuroscience, 22, 1554–1564. https://doi.org/10.1038/s41593-019-0488-y
Shen, G., Horikawa, T., Majima, K., & Kamitani, Y. (2019). Deep image reconstruction from human brain activity. PLoS computational biology, 15(1), e1006633. https://doi.org/10.1371/journal.pcbi.1006633
Smith, E., Storch, E., Vahia, I., Wong, S., Lavretsky, H., Cummings, J., & Eyre, H. (2021). Affective Computing for Late-Life Mood and Cognitive Disorders. Frontiers in Psychiatry, 12. https://doi.org/10.3389/fpsyt.2021.782183
Takagi, Y., & Nishimoto, S. (2023). High-resolution image reconstruction with latent diffusion models from human brain activity. bioRxiv
Tang, J., LeBel, A., Jain, S., & Huth, A.G. (2022). Semantic reconstruction of continuous language from non-invasive brain recordings. Nature Neuroscience, 26, 858-866
The White House. (2013, abril 2).Fact Sheet: BRAIN Initiative. https://obamawhitehouse.archives.gov/the-press-office/2013/04/02/fact-sheet-brain-initiative
Unger, P. (1996). Living high and letting die: Our illusion of inocence. New York: Oxford University Press
Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., & Zhang, W. (2022). A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances. ArXiv, abs/2203.06935. https://doi.org/10.48550/arXiv.2203.06935
Watanabe, K., Jogia, J., & Yoshimura, R. (2024). Editorial: Recent developments in neuroimaging in mood disorders. Frontiers in psychiatry, 15, 1371347. https://doi.org/10.3389/fpsyt.2024.1371347
Willett, F. R., Avansino, D. T., Hochberg, L. R., Henderson, J. M., & Shenoy, K. V. (2021). High-performance brain-to-text communication via handwriting. Nature, 593(7858), 249–254. https://doi.org/10.1038/s41586-021-03506-2
Wilson, J.Q. (1993). The moral sense. New York: Free Press
Zhang, W., Kim, S. M., Wang, W., Cai, C., Feng, Y., Kong, W., & Lin, X. (2018). Cochlear Gene Therapy for Sensorineural Hearing Loss: Current Status and Major Remaining Hurdles for Translational Success. Frontiers in molecular neuroscience, 11, 221. https://doi.org/10.3389/fnmol.2018.00221
Zhang, Z., Li, G., Xu, Y., & Tang, X. (2021). Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review. Diagnostics, 11. https://doi.org/10.3390/diagnostics11081402
Zheng, L., Liao, P., Wu, X., Cao, M., Cui, W., Lu, L., Xu, H., Zhu, L., Lyu, B., Wang, X., Teng, P., Wang, J., Vogrin, S., Plummer, C., Luan, G., & Gao, J. (2023). An artificial intelligence–based pipeline for automated detection and localisation of epileptic sources from magnetoencephalography. Journal of Neural Engineering, 20. https://doi.org/10.1088/1741-2552/acef92
Zhu, M., Li, S., Kuang, Y., Hill, V., Heimberger, A., Zhai, L., & Zhai, S. (2022). Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective. Frontiers in Oncology, 12. https://doi.org/10.3389/fonc.2022.924245
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Derechos de autor 2024 Carlos Alberto Castro Campolongo

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