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Gomez-Molina, J. F. (2024). El Cerebro es probabilístico, electrofisiológicamente intrincado y trino: una perspectiva de la neurociencia computacional basada en caminatas aleatorias dirigidas. International Journal of Psychological Research, 17(2), 100–112. https://doi.org/10.21500/20112084.7397
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To give up copyright, the authors allow that, International Journal of Psychological Research, distribute the work more broadly, check for the reuse by others and take care of the necessary procedures for the registration and administration of copyright; at the same time, our editorial board represents the interests of the author and allows authors to re-use his work in various forms. In response to the above, authors transfer copyright to the journal, International Journal of Psychological Research. This transfer does not imply other rights which are not those of authorship (for example those that concern about patents). Likewise, preserves the authors rights to use the work integral or partially in lectures, books and courses, as well as make copies for educational purposes. Finally, the authors may use freely the tables and figures in its future work, wherever make explicit reference to the previous publication in International Journal of Psychological Research. The assignment of copyright includes both virtual rights and forms of the article to allow the editorial to disseminate the work in the manner which it deems appropriate.
The editorial board reserves the right of amendments deemed necessary in the application of the rules of publication.
Resumen
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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Referencias
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Doya, K., Ishii, S., Pouget, A., & Rao, R. P. N. (2007). Bayesian brain: Probabilistic approaches to neural coding. MIT Press.
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Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787
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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.
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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
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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