El Cerebro es probabilístico, electrofisiológicamente intrincado y trino: una perspectiva de la neurociencia computacional basada en caminatas aleatorias dirigidas
La búsqueda de una teoría unificada que capture las complejidades del cerebro y la mente sigue siendo un desafío significativo en la neurociencia teórica. Este artículo presenta un nuevo marco trino que utiliza el concepto de caminatas aleatoria dirigidas colectivas (cBRW). Nuestro enfoque busca tra...
- Autores:
-
Gomez-Molina, Juan Fernando
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2024
- Institución:
- Universidad de San Buenaventura
- Repositorio:
- Repositorio USB
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.usb.edu.co:10819/29000
- Acceso en línea:
- https://hdl.handle.net/10819/29000
https://doi.org/10.21500/20112084.7397
- Palabra clave:
- Brain states
brain theory
brain electrophysiology
neural computations
electroencephalography
non-classic brains
Estados cerebrales
teoría del cerebro
computaciones neurales
electroencefalografía
cerebros no-clásicos
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- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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El Cerebro es probabilístico, electrofisiológicamente intrincado y trino: una perspectiva de la neurociencia computacional basada en caminatas aleatorias dirigidas |
| dc.title.translated.spa.fl_str_mv |
El Cerebro es probabilístico, electrofisiológicamente intrincado y trino: una perspectiva de la neurociencia computacional basada en caminatas aleatorias dirigidas |
| title |
El Cerebro es probabilístico, electrofisiológicamente intrincado y trino: una perspectiva de la neurociencia computacional basada en caminatas aleatorias dirigidas |
| spellingShingle |
El Cerebro es probabilístico, electrofisiológicamente intrincado y trino: una perspectiva de la neurociencia computacional basada en caminatas aleatorias dirigidas Brain states brain theory brain electrophysiology neural computations electroencephalography non-classic brains Estados cerebrales teoría del cerebro computaciones neurales electroencefalografía cerebros no-clásicos |
| title_short |
El Cerebro es probabilístico, electrofisiológicamente intrincado y trino: una perspectiva de la neurociencia computacional basada en caminatas aleatorias dirigidas |
| title_full |
El Cerebro es probabilístico, electrofisiológicamente intrincado y trino: una perspectiva de la neurociencia computacional basada en caminatas aleatorias dirigidas |
| title_fullStr |
El Cerebro es probabilístico, electrofisiológicamente intrincado y trino: una perspectiva de la neurociencia computacional basada en caminatas aleatorias dirigidas |
| title_full_unstemmed |
El Cerebro es probabilístico, electrofisiológicamente intrincado y trino: una perspectiva de la neurociencia computacional basada en caminatas aleatorias dirigidas |
| title_sort |
El Cerebro es probabilístico, electrofisiológicamente intrincado y trino: una perspectiva de la neurociencia computacional basada en caminatas aleatorias dirigidas |
| dc.creator.fl_str_mv |
Gomez-Molina, Juan Fernando |
| dc.contributor.author.eng.fl_str_mv |
Gomez-Molina, Juan Fernando |
| dc.subject.eng.fl_str_mv |
Brain states brain theory brain electrophysiology neural computations electroencephalography non-classic brains |
| topic |
Brain states brain theory brain electrophysiology neural computations electroencephalography non-classic brains Estados cerebrales teoría del cerebro computaciones neurales electroencefalografía cerebros no-clásicos |
| dc.subject.spa.fl_str_mv |
Estados cerebrales teoría del cerebro computaciones neurales electroencefalografía cerebros no-clásicos |
| description |
La búsqueda de una teoría unificada que capture las complejidades del cerebro y la mente sigue siendo un desafío significativo en la neurociencia teórica. Este artículo presenta un nuevo marco trino que utiliza el concepto de caminatas aleatoria dirigidas colectivas (cBRW). Nuestro enfoque busca trascender los detalles biológicos, ofreciendo una abstracción de alto nivel que sigue siendo general y aplicable a diversos fenómenos neuronales. A pesar de la sólida base tradicional de la neurociencia computacional, la delicadeza intrincada de los procesos neuronales requiere un enfoque probabilístico renovado. Nuestro objetivo es utilizar la naturaleza intuitiva de los conceptos de probabilidad, como la probabilidad de localización y estado, y la distribución de probabilidad uniforme, para estudiar la organización estocástica de las cargas y señales eléctricas en el cerebro. Esta complejidad electrofisiológica surge de la realidad aparentemente paradójica de que pequeños eventos eléctricos, aunque aleatorios, colectivamente dan lugar a oscilaciones predecibles y de largo alcance. Estas oscilaciones se manifiestan en tres grupos de estados de activación. Nuestro marco categoriza el cerebro como un sistema trino, acomodando interpretaciones clásicas, semiclásicas y no clásicas de fenómenos probabilísticos y modelos de BRW, junto con estos tres grupos de estados. Concluimos que, al apreciar, en lugar de pasar por alto, las pequeñas caminatas aleatorias de las cargas y señales eléctricas en el cerebro, podemos obtener una base matemática trina para la ciencia teórica del cerebro, las poderosas capacidades de este órgano y las interfaces electromagnéticas que podemos desarrollar. |
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2024 |
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2024-09-03T00:00:00Z 2025-08-22T16:59:36Z |
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2024-09-03T00:00:00Z 2025-08-22T16:59:36Z |
| dc.date.issued.none.fl_str_mv |
2024-09-03 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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10.21500/20112084.7397 |
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2011-7922 |
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2011-2084 |
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https://hdl.handle.net/10819/29000 |
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https://doi.org/10.21500/20112084.7397 |
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10.21500/20112084.7397 2011-7922 2011-2084 |
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https://hdl.handle.net/10819/29000 https://doi.org/10.21500/20112084.7397 |
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eng |
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eng |
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https://revistas.usb.edu.co/index.php/IJPR/article/download/7397/5499 |
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Núm. 2 , Año 2024 : Interdisciplinary Approaches for Human Cognition: Expanding Perspectives on the Mind |
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112 |
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2 |
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100 |
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17 |
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International Journal of Psychological Research |
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Aaronson, S. (2013). Why philosophers should care about computational complexity. In B. Copeland, C. Posy, & O. Shagrir (Eds.), Computability: Godel, Turing, Church, and beyond (pp. xx-xx). MIT Press. Abers, E. (2004). Quantum mechanics. Pearson Education, Addison Wesley, Prentice Hall Inc. Aitchison, L., & Lengyel, M. (2017). With or without you: Predictive coding and Bayesian inference in the brain. Current Opinion in Neurobiology, 46, 219-227. https://doi.org/10.1016/j.conb.2017.08.010 Anastassiou, C. A., & Koch, C. (2014). Ephaptic coupling to endogenous electric field activity: Why bother? Current Opinion in Neurobiology, 31, 95-103. https://doi.org/10.1016/j.conb.2014.09.002 Andreassi, J. L. (2007). Psychophysiology (5th ed.). Taylor and Francis Group. Brugger, W. (1981). Philosophisches Wörterbuch. Verlag Herder Freiburg/Br. Bunge, M. (1979). Treatise on basic philosophy (Vol. 4). Reidel Publishing Company. Buzsaki, G., Anastassiou, C. A., & Koch, C. (2012). The origin of extracellular fields and currents—EEG, ECoG, LFP and spikes. Nature Reviews Neuroscience, 13, 407-420. Codling, E. A., Plank, M. J., & Benhamou, S. (2008). Random walk models in biology. Journal of the Royal Society Interface, 5, 813-834. Corballis, M. C. (2017). The evolution of lateralized brain circuits. Frontiers in Psychology, 8, 1021. https://doi.org/10.3389/fpsyg.2017.01021 Dayan, P., & Abbott, L. F. (2005). Theoretical neuroscience: Computational and mathematical modeling of neural systems. MIT Press. Denizot, A., Arizono, M., Nägerl, U. V., Soula, H., & Berry, H. (2019). Simulation of calcium signaling in fine astrocytic processes: Effect of spatial properties on spontaneous activity. PLoS Computational Biology, 15, e1006795. Doya, K., Ishii, S., Pouget, A., & Rao, R. P. N. (2007). Bayesian brain: Probabilistic approaches to neural coding. MIT Press. Duffi, E. (1972). Activation and behavior. Wiley. Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787 Gentili, P. L. (2021). Establishing a new link between fuzzy logic, neuroscience, and quantum mechanics through Bayesian probability: Perspectives in artificial intelligence and unconventional computing. Molecules, 26(19), 5987. https://doi.org/10.3390/molecules26195987 Giere, R. N. (1973). Objective single case probabilities and the foundations of statistics. Studies in Logic and the Foundations of Mathematics, 73, 467–483. https://doi.org/10.1016/S0049-237X(09)70380-5 Gomez, J. F., & Lopera, F. J. (1999). A topological hypothesis for the functional connections of the cortex: A principle of the cortical graphs based on neuroimaging. Medical Hypotheses, 53(3), 263-266. Gomez-M, J. F. (2000). Ionic current and metabolism for brain scanners (a three state-model of modular activation). Neural Networks, 13(6), 689-690. https://doi.org/10.1016/S0893-6080(00)00033-2 Gomez-Molina, J. F. (2003). Ionic channels and long-range electrical signals: A probabilistic interaction. Medical Hypotheses, 60(4), 463-467. https://doi.org/10.1016/S0306-9877(02)00299-2 Gomez-Molina, J. F. (2008). A probabilistic-Bayesian approach to epileptiform events: Combination of visual stimulation and EEG with fMRI/“Ionic-Current MRI”. In M. Ding & D. Glanzman (Eds.), Proceedings of Dynamical Neuroscience XVI, A Satellite Symposium Immediately Preceding the 38th Annual Meeting of the Society for Neuroscience (p. 27). Washington, DC: JW Marriott Hotel. Gómez Molina, J. F., Gómez Molina, Á., & Restrepo, A. A. (2013). Explorando circuitos cerebrales sin perturbarlos: Neuroingeniería no invasiva. Uni-Pluriversidad, 12(3), 29–35. https://doi.org/10.17533/udea.unipluri.15351 Gomez-Molina, J. F., Corredor, M., Restrepo-Velazquez, A. A., & Botero-Posada, L. F. (2015). Field generated by waves, sequential activations and apparent motion: Effects and typical patterns. Revista Ingeniería Biomédica, 7(17), 13-20. Gomez-Molina, J. F., Corredor, M., Restrepo-Velasquez, A. A., & Ricoy, U. M. (2017). Computer models for ions under electric and magnetic fields: Random walks and relocation of calcium in dendrites depends on timing and population type. In I. Torres, J. Bustamante, & D. Sierra (Eds.), VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th-28th, 2016. IFMBE Proceedings (Vol. 60). Springer, Singapore. https://doi.org/10.1007/978-981-10-4086-3_175 Gomez-Molina, J. F. (2022). Study using Python/Excel and chronobiosymmetry of exotic states between sleep and activation: Sleep spindles, alpha activity and dendritic Ca2+ in aging and Alzheimer’s disease. Program No. 088.02. 2022 Neuroscience Meeting Planner. Society for Neuroscience. Griffiths, D. J. (2014). Introduction to electrodynamics (4th ed.). Pearson Education Limited. Gutierrez, G. J., Rieke, F., & Shea-Brown, E. T. (2021). Nonlinear convergence boosts information coding in circuits with parallel outputs. Proceedings of the National Academy of Sciences, 118(e1921882118). https://doi.org/10.1073/pnas.1921882118 Halnes, G., Mäki-Marttunen, T., Keller, D., Pettersen, K. H., Andreassen, O. A., & Einevoll, G. T. (2016). Effect of ionic diffusion on extracellular potentials in neural tissue. PLoS Computational Biology, 12(e1005193). https://doi.org/10.1371/journal.pcbi.1005193 Halnes, G., Mäki-Marttunen, T., Pettersen, K. H., Andreassen, O. A., & Einevoll, G. T. (2017). Ion diffusion may introduce spurious current sources in current-source density (CSD) analysis. Journal of Neurophysiology, 118, 114–120. https://doi.org/10.1152/jn.00976.2016 Hille, B. (2001). Ion channels of excitable membranes (3rd ed.). Sinauer Associates. Jaynes, E. T. (2003). Probability theory: The logic of science. Cambridge University Press. Johnston, D., & Wu, S. M.-S. (1995). Foundations of cellular neurophysiology. MIT Press. Kempe, J. (2009). Quantum random walks: An introductory overview. Contemporary Physics, 50(1), 339–359. https://doi.org/10.1080/00107510902734722 Kerre, E. E., & Mordeson, J. N. (2005). A historical overview of fuzzy mathematics. New Mathematics and Natural Computation, 1(1), 1-26. https://doi.org/10.1142/S179300570500011X Knill, D. C., & Pouget, A. (2004). The Bayesian brain: The role of uncertainty in neural coding and computation. Trends in Neurosciences, 27(12), 712-719. https://doi.org/10.1016/j.tins.2004.10.007 Koch, C. (1999). Biophysics of computation. Oxford University Press. Lawler, G. (1996). Intersection of random walks. Birkhäuser Boston. https://doi.org/10.1007/978-1-4612-4126-5 Lin, S., Xu, Z., Sheng, Y., Chen, L., & Chen, J. (2022). AT-NeuroEAE: A joint extraction model of events with attributes for research sharing-oriented neuroimaging provenance construction. Frontiers in Neuroscience, 15, 739535. https://doi.org/10.3389/fnins.2021.739535 Madras, N., & Slade, G. (1996). The self-avoiding walk. Birkhäuser Boston. https://doi.org/10.1007/978-1-4612-4126-5 Malmivuo, J., & Plonsey, R. (1995). Bioelectromagnetism: Principles and applications of bioelectric and biomagnetic fields. Oxford University Press. Michel, C. M., & Brunet, D. (2019). EEG source imaging: A practical review of the analysis steps. Frontiers in Neurology, 10, 325. https://doi.org/10.3389/fneur.2019.00325 Nicholson, C. (2005). Factors governing diffusion of molecular signals in brain extracellular space. Journal of Neural Transmission, 112, 29–44. https://doi.org/10.1007/s00702-004-0204-1 Nietz, A. K., Popa, L. S., Streng, M. L., Carter, R. E., Kodandaramaiah, S. B., & Ebner, T. J. (2022). Wide-field calcium imaging of neuronal network dynamics in vivo. Biology (Basel), 11(11), 1601. https://doi.org/10.3390/biology11111601 Nunes, P., & Srinivasan, R. (2006). Electric fields of the brain. Oxford University Press. Postnikov, E. B., Lavrova, A. I., & Postnov, D. E. (2022). Transport in the brain extracellular space: Diffusion, but which kind? International Journal of Molecular Sciences, 23(12401). https://doi.org/10.3390/ijms232012401 Riera, J. J., Ogawa, T., Goto, T., Sumiyoshi, A., Nonaka, H., Evans, A., Miyakawa, H., & Kawashima, R. (2012). Pitfalls in the dipolar model for the neocortical EEG sources. Journal of Neurophysiology, 108, 956–975. https://doi.org/10.1152/jn.00098.2011 Riera, J., & Cabo, A. (2013). Reply to Gratiy et al. Journal of Neurophysiology, 109, 1684–1685. https://doi.org/10.1152/jn.00014.2013 Rodriguez-Falces, J. (2015). Understanding the electrical behavior of the action potential in terms of elementary electrical sources. Advances in Physiology Education, 39, 15–26. https://doi.org/10.1152/advan.00130.2014 Rossi, G. B., Crenna, F., & Berardengo, M. (2023). Probability theory as a logic for modeling the measurement process. Acta IMEKO. Shapiro, S. (Ed.). (2005). The Oxford handbook of philosophy of mathematics and logic. Oxford University Press. Solé, R., Moses, M., & Forrest, S. (2019). Liquid brains, solid brains. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(20190040). https://doi.org/10.1098/rstb.2019.0040 Staii, C. (2023). Biased random walk model of neuronal dynamics on substrates with periodic geometrical patterns. Biomimetics, 8(267). https://doi.org/10.3390/biomimetics8020267 Toi, P. T., Jang, H. J., Min, K., Kim, S. P., Lee, S. K., Lee, J., Kwag, J., & Park, J. Y. (2022). In vivo direct imaging of neuronal activity at high temporospatial resolution. Science, 378(6616), 160-168. https://doi.org/10.1126/science.abh4340 Wilson, C. (2008). Up and down states. Scholarpedia, 3(1410). https://doi.org/10.4249/scholarpedia.1410 Youssef, S. (2001). Physics with exotic probability theory. arXiv. https://arxiv.org/abs/hep-th/0110253 |
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Gomez-Molina, Juan Fernando2024-09-03T00:00:00Z2025-08-22T16:59:36Z2024-09-03T00:00:00Z2025-08-22T16:59:36Z2024-09-03La búsqueda de una teoría unificada que capture las complejidades del cerebro y la mente sigue siendo un desafío significativo en la neurociencia teórica. Este artículo presenta un nuevo marco trino que utiliza el concepto de caminatas aleatoria dirigidas colectivas (cBRW). Nuestro enfoque busca trascender los detalles biológicos, ofreciendo una abstracción de alto nivel que sigue siendo general y aplicable a diversos fenómenos neuronales. A pesar de la sólida base tradicional de la neurociencia computacional, la delicadeza intrincada de los procesos neuronales requiere un enfoque probabilístico renovado. Nuestro objetivo es utilizar la naturaleza intuitiva de los conceptos de probabilidad, como la probabilidad de localización y estado, y la distribución de probabilidad uniforme, para estudiar la organización estocástica de las cargas y señales eléctricas en el cerebro. Esta complejidad electrofisiológica surge de la realidad aparentemente paradójica de que pequeños eventos eléctricos, aunque aleatorios, colectivamente dan lugar a oscilaciones predecibles y de largo alcance. Estas oscilaciones se manifiestan en tres grupos de estados de activación. Nuestro marco categoriza el cerebro como un sistema trino, acomodando interpretaciones clásicas, semiclásicas y no clásicas de fenómenos probabilísticos y modelos de BRW, junto con estos tres grupos de estados. Concluimos que, al apreciar, en lugar de pasar por alto, las pequeñas caminatas aleatorias de las cargas y señales eléctricas en el cerebro, podemos obtener una base matemática trina para la ciencia teórica del cerebro, las poderosas capacidades de este órgano y las interfaces electromagnéticas que podemos desarrollar.The pursuit of a unified theory that captures the intricacies of the brain and mind continues to be a significant challenge in theoretical neuroscience. This paper presents a novel, triune framework that utilizes the concept of collective biased random walk (cBRW). Our approach strives to transcend biological specifics, offering a high-level abstraction that remains general and applicable across various neural phenomena. Despite the solid traditional foundation of computational neuroscience, the intricate delicacy of neural processes calls for a renewed probabilistic approach. We aim to utilize the intuitive nature of probability concepts —such as the probability of localization and state, and uniform probability distribution— to study the stochastic organization of electric charges and signals in the brain. This electrophysiological intricacy emerges from the seemingly paradoxical reality that tiny electric events, while random, collectively give rise to predictable, long-range oscillations. These oscillations manifest in three groups of activation states. Our framework categorizes the brain as a triune system, accommodating classical, semiclassical, and non-classical interpretations of both probabilistic phenomena and cBRW models, alongside three groups of states. We conclude that by appreciating, rather than overlooking, the tiny random walks of electric charges and signals in the brain, we can gain a triune mathematical foundation for theoretical brain science, the powerful capabilities of this organ, and the electromagnetic interfaces we can develop.application/pdf10.21500/20112084.73972011-79222011-2084https://hdl.handle.net/10819/29000https://doi.org/10.21500/20112084.7397engUniversidad San Buenaventura - USB (Colombia)https://revistas.usb.edu.co/index.php/IJPR/article/download/7397/5499Núm. 2 , Año 2024 : Interdisciplinary Approaches for Human Cognition: Expanding Perspectives on the Mind112210017International Journal of Psychological ResearchAaronson, S. (2013). Why philosophers should care about computational complexity. In B. Copeland, C. Posy, & O. Shagrir (Eds.), Computability: Godel, Turing, Church, and beyond (pp. xx-xx). MIT Press. Abers, E. (2004). Quantum mechanics. Pearson Education, Addison Wesley, Prentice Hall Inc. Aitchison, L., & Lengyel, M. (2017). With or without you: Predictive coding and Bayesian inference in the brain. Current Opinion in Neurobiology, 46, 219-227. https://doi.org/10.1016/j.conb.2017.08.010 Anastassiou, C. A., & Koch, C. (2014). Ephaptic coupling to endogenous electric field activity: Why bother? Current Opinion in Neurobiology, 31, 95-103. https://doi.org/10.1016/j.conb.2014.09.002 Andreassi, J. L. (2007). Psychophysiology (5th ed.). Taylor and Francis Group. Brugger, W. (1981). Philosophisches Wörterbuch. Verlag Herder Freiburg/Br. Bunge, M. (1979). Treatise on basic philosophy (Vol. 4). Reidel Publishing Company. Buzsaki, G., Anastassiou, C. A., & Koch, C. (2012). The origin of extracellular fields and currents—EEG, ECoG, LFP and spikes. Nature Reviews Neuroscience, 13, 407-420. Codling, E. A., Plank, M. J., & Benhamou, S. (2008). Random walk models in biology. Journal of the Royal Society Interface, 5, 813-834. Corballis, M. C. (2017). The evolution of lateralized brain circuits. Frontiers in Psychology, 8, 1021. https://doi.org/10.3389/fpsyg.2017.01021 Dayan, P., & Abbott, L. F. (2005). Theoretical neuroscience: Computational and mathematical modeling of neural systems. MIT Press. Denizot, A., Arizono, M., Nägerl, U. V., Soula, H., & Berry, H. (2019). Simulation of calcium signaling in fine astrocytic processes: Effect of spatial properties on spontaneous activity. PLoS Computational Biology, 15, e1006795. Doya, K., Ishii, S., Pouget, A., & Rao, R. P. N. (2007). Bayesian brain: Probabilistic approaches to neural coding. MIT Press. Duffi, E. (1972). Activation and behavior. Wiley. Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787 Gentili, P. L. (2021). Establishing a new link between fuzzy logic, neuroscience, and quantum mechanics through Bayesian probability: Perspectives in artificial intelligence and unconventional computing. Molecules, 26(19), 5987. https://doi.org/10.3390/molecules26195987 Giere, R. N. (1973). Objective single case probabilities and the foundations of statistics. Studies in Logic and the Foundations of Mathematics, 73, 467–483. https://doi.org/10.1016/S0049-237X(09)70380-5 Gomez, J. F., & Lopera, F. J. (1999). A topological hypothesis for the functional connections of the cortex: A principle of the cortical graphs based on neuroimaging. Medical Hypotheses, 53(3), 263-266. Gomez-M, J. F. (2000). Ionic current and metabolism for brain scanners (a three state-model of modular activation). Neural Networks, 13(6), 689-690. https://doi.org/10.1016/S0893-6080(00)00033-2 Gomez-Molina, J. F. (2003). 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Physics with exotic probability theory. arXiv. https://arxiv.org/abs/hep-th/0110253info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.http://creativecommons.org/licenses/by-nc-nd/4.0https://revistas.usb.edu.co/index.php/IJPR/article/view/7397Brain statesbrain theorybrain electrophysiologyneural computationselectroencephalographynon-classic brainsEstados cerebralesteoría del cerebrocomputaciones neuraleselectroencefalografíacerebros no-clásicosEl Cerebro es probabilístico, electrofisiológicamente intrincado y trino: una perspectiva de la neurociencia computacional basada en caminatas aleatorias dirigidasEl Cerebro es probabilístico, electrofisiológicamente intrincado y trino: una perspectiva de la neurociencia computacional basada en caminatas aleatorias dirigidasArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionPublicationOREORE.xmltext/xml2647https://bibliotecadigital.usb.edu.co/bitstreams/fa88a6c7-fbe3-41ce-bb51-099f0d3bdf24/download1ade08c32aec6c0b848c8be836cd75bcMD5110819/29000oai:bibliotecadigital.usb.edu.co:10819/290002025-08-22 11:59:36.442http://creativecommons.org/licenses/by-nc-nd/4.0https://bibliotecadigital.usb.edu.coRepositorio Institucional Universidad de San Buenaventura Colombiabdigital@metabiblioteca.com |