Avances en el conocimiento y modelado computacional del cerebro autista: Una revisión de literatura
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Avances en el conocimiento y modelado computacional del cerebro autista: Una revisión de literatura
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El estudio del funcionamiento del cerebro permite, no sólo el descubrimiento de sus principios, sino también en la construcción de máquinas que lo emulen cada vez más inteligentes. En ese sentido, las neurociencias están aportando importantes conocimientos sobre cómo los diferentes elementos del cerebro interactúan en el procesamiento de información, para dar origen a funciones cognitivas de alto nivel (aprendizaje, conciencia, qualía, etc.), que caracterizan la conducta humana. Por otra parte, existen cerebros que viene con una maquinaria neuronal distinta caracterizados por sus capacidades cognitivas extraordinarias, comúnmente conocidos como autistas. A partir de estos dos hechos se planteó el siguiente interrogante. ¿Qué tanto se sabe... Ver más

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Puerto, Eduard

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10.53995/20278101.425

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2017-03-29

109

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Eduard Puerto - 2017

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spelling Avances en el conocimiento y modelado computacional del cerebro autista: Una revisión de literatura
Advances in knowledge and computational modeling of the autistic brain: A literature review
El estudio del funcionamiento del cerebro permite, no sólo el descubrimiento de sus principios, sino también en la construcción de máquinas que lo emulen cada vez más inteligentes. En ese sentido, las neurociencias están aportando importantes conocimientos sobre cómo los diferentes elementos del cerebro interactúan en el procesamiento de información, para dar origen a funciones cognitivas de alto nivel (aprendizaje, conciencia, qualía, etc.), que caracterizan la conducta humana. Por otra parte, existen cerebros que viene con una maquinaria neuronal distinta caracterizados por sus capacidades cognitivas extraordinarias, comúnmente conocidos como autistas. A partir de estos dos hechos se planteó el siguiente interrogante. ¿Qué tanto se sabe sobre el autismo y como se ha avanzado en su modelado a nivel computacional?. Este artículo da una respuesta particular a modo de síntesis teórica del fenómeno autista y avances que a nivel computacional se han logrado en cuanto a simulación, emulación y desarrollo de herramientas de apoyo relacionados con este complejo fenómeno. Lo anterior con base en más de 50 estudios tomados de bases de datos científicas, tales como: Nature, Scopus, ACM, IEEE, Google scholar, entre otras.Palabras clave: Neurociencia computacional, autismo, tecnologías de exploración cerebral, savant, modelos computacionales TEA, herramientas de apoyo TEA, anatomía del cerebro autista.
The study of the functioning of the brain allows, not only the discovery of its principles, but also in the construction of machines that emulate getting smarter. In that sense, neurosciences are providing important insights into how different elements of the brain interact in information processing to give rise to high-level cognitive functions (learning, awareness, quality, etc.) that characterize human behavior. On the other hand, there are brains that come with distinct neuronal machinery characterized by their extraordinary cognitive abilities, commonly known as autistic. From these two facts the following question arises. How much is known about autism and how it has advanced in its modeling at the computational level?. This article gives a particular answer as a theoretical synthesis of the autistic phenomenon and advances that at computational level have been achieved in relation to simulation, emulation and development of support tools related to this complex phenomenon. The above based on more than 50 studies taken from scientific databases, such as: Nature, Scopus, ACM, IEEE, Google Scholar, among others.Keywords: Computational neuroscience, autism, brain scanning technologies, savant, computational models of TEA, support tools TEA, anatomy of the autistic brain.
Puerto, Eduard
9
9
Artículo de revista
Journal article
2017-03-29T00:00:00Z
2017-03-29T00:00:00Z
2017-03-29
application/pdf
text/xml
Tecnológico de Antioquia - Institución Universitaria
Cuaderno activa
2027-8101
2619-5232
https://ojs.tdea.edu.co/index.php/cuadernoactiva/article/view/425
10.53995/20278101.425
https://doi.org/10.53995/20278101.425
spa
https://creativecommons.org/licenses/by-nc-sa/4.0
Eduard Puerto - 2017
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
109
125
Aguilar, J. (2005). A survey about fuzzy cognitive maps papers. International journal of computational cognition, 3(2), 27-33.
Alivisatos, P., Chun, M., Church, G., Greenspan, R., Roukes, M., & Yuste, R. (2012). The brain activity map project and the challenge of functional connectomics. Neuron, 74(6), 970-974.
Veros, M. (2016). VirtuaCyL: desarrollo y validación de un sistema ubicuo basado en Android para refuerzo educativo de niños con autismo dentro de la metodología Teacch (tesis máster en Inteligencia Artificial). Universidad Politécnica de Valencia, Valencia, España.
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (Fifth edition). Recuperado de http://dsm.psychiatryonline.org/doi/book/10.1176/appi.books.9780890425596
Anderson, J. (2013). The architecture of cognition. Psychology Press. 340.
Baron, S, et al. (2002). Development of a new screening instrument for autism spectrum disorders - the Q-CHAT. Paper presented at the International Meeting for Autism Research. Orlando, FL.
Baron, S., Leslie, A. M., & Frith, U. (1985). Does the autistic child have a “theory of mind”?. Cognition, 21(1), 37-46.
Bellani, M., Fornasari, L., Chittaro, L., & Brambilla, P. (2011). Virtual reality in autism: state of the art. Epidemiology and psychiatric sciences, 20(03), 235-238.
Bone, D., Bishop, S., Black, M., Goodwin, M., Lord, C., & Narayanan, S. (2016). Use of machine learning to improve autism screening and diagnostic instruments: Effectiveness, efficiency, and multinstrument fusion. Journal of Child Psychology and Psychiatry, 57(8), 927-937.
Cai, D., & otros. (2013). Improved tools for the brainbow toolbox. Nature methods, 10(6), 540-547.
Carandini, M., & Heeger, D. (2013). Normalization as a canonical neural computation. Nature Reviews Neuroscience, 14(2), 152-152.
Vidal, L., Carvalho, N., & Fiszman, A. (July 1999). A neurocomputational model for autism. Proceedings oe the IV Brazilian Conference on Neural networks. Congresso Brasileiro de Redes Neurais. Sao José dos Campos, Brazil.
Castillo, T., Pérez, C., Lara, C., Somodevilla, M., Pineda, I., de Alba, K., y Romero, E. (2016). Authic: Herramienta computacional para niños con espectro autista. XVIII Simposio Internacional de Informática Educativa, Puebla, México.
Cattell, R., & Parker, A. (2012). Challenges for brain emulation: why is building a brain so difficult. Natural intelligence, 1(3), 17-31.
Cererols, R. (2011). Descubrir el Asperger (Segunda edición), 184.
Corrigan, N., Richards, T., Treffert, D., & Dager, S. (2012). Toward a better understanding of the savant brain. Comprehensive psychiatry, 53(6), 706-717.
Włodzisław, D., Wiesław, N., Jaroslaw M., Grzegorz, O., Krzysztof, D., Dariusz, M., & Grzegorz, M. (2012). Computational approach to understanding autism spectrum disorders. Computer Science, 13(2), 47-61.
Frey, J., Mühl, C., Lotte, F., & Hachet, M. (2013). Review of the use of electroencephalography as an evaluation method for human-computer interaction. Recuperado de https://arxiv.org/pdf/1311.2222.pdf
Friston, K., & Buzsáki, G. (2016). The Functional Anatomy of Time: What and when in the Brain. Trends in cognitive sciences, 20(7). 500-511.
Galitsky, B. (2013). A computational simulation tool for training autistic reasoning about mental attitudes. Knowledge-based systems, 50(C), 25-43.
Grandin, T., & Panek, R. (2013). The autistic brain: Thinking across the spectrum. Houghton Mifflin Harcourt. 253.
Happé, F., & Frith, U. (2006). The weak coherence account: detail-focused cognitive style in autism spectrum disorders. Journal of autism and developmental disorders, 36(1), 5-25.
Harper, S. (2010). nano-TAB: Specification to facilitate data exchange among nanotechnology resources. Cancer biomedical informatics grid, 32.
Houdé, O., & Tzourio-Mazoyer, N. (2003). Neural foundations of logical and mathematical cognition. Nature Reviews Neuroscience, 4(6), 507-514.
Howlin, P. (2012). Understanding savant skills in autism. Developmental Medicine & Child Neurology, 54 (6), 484-484.
Junek, W. (2007). Mind reading: The interactive guide to emotions. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 16 (4), 182.
Kapur N. (1996). Paradoxical functional facilitation in brain-behavior research. A critical review. Brain, 119(5), 1775-1790.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Software Engineering Group, Department of Computer Science, 33.
Kodandaramaiah, S. B., y otros. (2016). Assembly and operation of the autopatcher for automated intracellular neural recording in vivo. Nature protocols, 11(4), 634-654.
Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Li, K., Guo, L., Li, G., & Liu, T. (2009). Review of methods for functional brain connectivity detection using fMRI. Computerized Medical Imaging and Graphics, 33(2), 131-139.
Liu, E., & Konkle, A. (2016). Extreme male brain theory of autism. Revue interdisciplinaire des sciences de la santé-Interdisciplinary Journal of Health Sciences, 2(1), 32-43.
Lou, H., Changeux, J., & Rosenstand, A. (2016). Towards a cognitive neuroscience of self-awareness. Neuroscience & Biobehavioral Reviews. 75, 9.
Lovaas, O., Koegel, R., & Schreibman, L. (1979). Stimulus overselectivity in autism: a review of research. Psychological bulletin, 86(6), 1236-1254.
Markram, K., & Markram, H. (2010). The intense world theory-a unifying theory of the neurobiology of autism. doi: doi.org/10.3389/fnhum.2010.00224
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title Avances en el conocimiento y modelado computacional del cerebro autista: Una revisión de literatura
spellingShingle Avances en el conocimiento y modelado computacional del cerebro autista: Una revisión de literatura
Puerto, Eduard
title_short Avances en el conocimiento y modelado computacional del cerebro autista: Una revisión de literatura
title_full Avances en el conocimiento y modelado computacional del cerebro autista: Una revisión de literatura
title_fullStr Avances en el conocimiento y modelado computacional del cerebro autista: Una revisión de literatura
title_full_unstemmed Avances en el conocimiento y modelado computacional del cerebro autista: Una revisión de literatura
title_sort avances en el conocimiento y modelado computacional del cerebro autista: una revisión de literatura
title_eng Advances in knowledge and computational modeling of the autistic brain: A literature review
description El estudio del funcionamiento del cerebro permite, no sólo el descubrimiento de sus principios, sino también en la construcción de máquinas que lo emulen cada vez más inteligentes. En ese sentido, las neurociencias están aportando importantes conocimientos sobre cómo los diferentes elementos del cerebro interactúan en el procesamiento de información, para dar origen a funciones cognitivas de alto nivel (aprendizaje, conciencia, qualía, etc.), que caracterizan la conducta humana. Por otra parte, existen cerebros que viene con una maquinaria neuronal distinta caracterizados por sus capacidades cognitivas extraordinarias, comúnmente conocidos como autistas. A partir de estos dos hechos se planteó el siguiente interrogante. ¿Qué tanto se sabe sobre el autismo y como se ha avanzado en su modelado a nivel computacional?. Este artículo da una respuesta particular a modo de síntesis teórica del fenómeno autista y avances que a nivel computacional se han logrado en cuanto a simulación, emulación y desarrollo de herramientas de apoyo relacionados con este complejo fenómeno. Lo anterior con base en más de 50 estudios tomados de bases de datos científicas, tales como: Nature, Scopus, ACM, IEEE, Google scholar, entre otras.Palabras clave: Neurociencia computacional, autismo, tecnologías de exploración cerebral, savant, modelos computacionales TEA, herramientas de apoyo TEA, anatomía del cerebro autista.
description_eng The study of the functioning of the brain allows, not only the discovery of its principles, but also in the construction of machines that emulate getting smarter. In that sense, neurosciences are providing important insights into how different elements of the brain interact in information processing to give rise to high-level cognitive functions (learning, awareness, quality, etc.) that characterize human behavior. On the other hand, there are brains that come with distinct neuronal machinery characterized by their extraordinary cognitive abilities, commonly known as autistic. From these two facts the following question arises. How much is known about autism and how it has advanced in its modeling at the computational level?. This article gives a particular answer as a theoretical synthesis of the autistic phenomenon and advances that at computational level have been achieved in relation to simulation, emulation and development of support tools related to this complex phenomenon. The above based on more than 50 studies taken from scientific databases, such as: Nature, Scopus, ACM, IEEE, Google Scholar, among others.Keywords: Computational neuroscience, autism, brain scanning technologies, savant, computational models of TEA, support tools TEA, anatomy of the autistic brain.
author Puerto, Eduard
author_facet Puerto, Eduard
citationvolume 9
citationissue 9
publisher Tecnológico de Antioquia - Institución Universitaria
ispartofjournal Cuaderno activa
source https://ojs.tdea.edu.co/index.php/cuadernoactiva/article/view/425
language spa
format Article
rights https://creativecommons.org/licenses/by-nc-sa/4.0
Eduard Puerto - 2017
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
info:eu-repo/semantics/openAccess
http://purl.org/coar/access_right/c_abf2
references Aguilar, J. (2005). A survey about fuzzy cognitive maps papers. International journal of computational cognition, 3(2), 27-33.
Alivisatos, P., Chun, M., Church, G., Greenspan, R., Roukes, M., & Yuste, R. (2012). The brain activity map project and the challenge of functional connectomics. Neuron, 74(6), 970-974.
Veros, M. (2016). VirtuaCyL: desarrollo y validación de un sistema ubicuo basado en Android para refuerzo educativo de niños con autismo dentro de la metodología Teacch (tesis máster en Inteligencia Artificial). Universidad Politécnica de Valencia, Valencia, España.
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (Fifth edition). Recuperado de http://dsm.psychiatryonline.org/doi/book/10.1176/appi.books.9780890425596
Anderson, J. (2013). The architecture of cognition. Psychology Press. 340.
Baron, S, et al. (2002). Development of a new screening instrument for autism spectrum disorders - the Q-CHAT. Paper presented at the International Meeting for Autism Research. Orlando, FL.
Baron, S., Leslie, A. M., & Frith, U. (1985). Does the autistic child have a “theory of mind”?. Cognition, 21(1), 37-46.
Bellani, M., Fornasari, L., Chittaro, L., & Brambilla, P. (2011). Virtual reality in autism: state of the art. Epidemiology and psychiatric sciences, 20(03), 235-238.
Bone, D., Bishop, S., Black, M., Goodwin, M., Lord, C., & Narayanan, S. (2016). Use of machine learning to improve autism screening and diagnostic instruments: Effectiveness, efficiency, and multinstrument fusion. Journal of Child Psychology and Psychiatry, 57(8), 927-937.
Cai, D., & otros. (2013). Improved tools for the brainbow toolbox. Nature methods, 10(6), 540-547.
Carandini, M., & Heeger, D. (2013). Normalization as a canonical neural computation. Nature Reviews Neuroscience, 14(2), 152-152.
Vidal, L., Carvalho, N., & Fiszman, A. (July 1999). A neurocomputational model for autism. Proceedings oe the IV Brazilian Conference on Neural networks. Congresso Brasileiro de Redes Neurais. Sao José dos Campos, Brazil.
Castillo, T., Pérez, C., Lara, C., Somodevilla, M., Pineda, I., de Alba, K., y Romero, E. (2016). Authic: Herramienta computacional para niños con espectro autista. XVIII Simposio Internacional de Informática Educativa, Puebla, México.
Cattell, R., & Parker, A. (2012). Challenges for brain emulation: why is building a brain so difficult. Natural intelligence, 1(3), 17-31.
Cererols, R. (2011). Descubrir el Asperger (Segunda edición), 184.
Corrigan, N., Richards, T., Treffert, D., & Dager, S. (2012). Toward a better understanding of the savant brain. Comprehensive psychiatry, 53(6), 706-717.
Włodzisław, D., Wiesław, N., Jaroslaw M., Grzegorz, O., Krzysztof, D., Dariusz, M., & Grzegorz, M. (2012). Computational approach to understanding autism spectrum disorders. Computer Science, 13(2), 47-61.
Frey, J., Mühl, C., Lotte, F., & Hachet, M. (2013). Review of the use of electroencephalography as an evaluation method for human-computer interaction. Recuperado de https://arxiv.org/pdf/1311.2222.pdf
Friston, K., & Buzsáki, G. (2016). The Functional Anatomy of Time: What and when in the Brain. Trends in cognitive sciences, 20(7). 500-511.
Galitsky, B. (2013). A computational simulation tool for training autistic reasoning about mental attitudes. Knowledge-based systems, 50(C), 25-43.
Grandin, T., & Panek, R. (2013). The autistic brain: Thinking across the spectrum. Houghton Mifflin Harcourt. 253.
Happé, F., & Frith, U. (2006). The weak coherence account: detail-focused cognitive style in autism spectrum disorders. Journal of autism and developmental disorders, 36(1), 5-25.
Harper, S. (2010). nano-TAB: Specification to facilitate data exchange among nanotechnology resources. Cancer biomedical informatics grid, 32.
Houdé, O., & Tzourio-Mazoyer, N. (2003). Neural foundations of logical and mathematical cognition. Nature Reviews Neuroscience, 4(6), 507-514.
Howlin, P. (2012). Understanding savant skills in autism. Developmental Medicine & Child Neurology, 54 (6), 484-484.
Junek, W. (2007). Mind reading: The interactive guide to emotions. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 16 (4), 182.
Kapur N. (1996). Paradoxical functional facilitation in brain-behavior research. A critical review. Brain, 119(5), 1775-1790.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Software Engineering Group, Department of Computer Science, 33.
Kodandaramaiah, S. B., y otros. (2016). Assembly and operation of the autopatcher for automated intracellular neural recording in vivo. Nature protocols, 11(4), 634-654.
Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Li, K., Guo, L., Li, G., & Liu, T. (2009). Review of methods for functional brain connectivity detection using fMRI. Computerized Medical Imaging and Graphics, 33(2), 131-139.
Liu, E., & Konkle, A. (2016). Extreme male brain theory of autism. Revue interdisciplinaire des sciences de la santé-Interdisciplinary Journal of Health Sciences, 2(1), 32-43.
Lou, H., Changeux, J., & Rosenstand, A. (2016). Towards a cognitive neuroscience of self-awareness. Neuroscience & Biobehavioral Reviews. 75, 9.
Lovaas, O., Koegel, R., & Schreibman, L. (1979). Stimulus overselectivity in autism: a review of research. Psychological bulletin, 86(6), 1236-1254.
Markram, K., & Markram, H. (2010). The intense world theory-a unifying theory of the neurobiology of autism. doi: doi.org/10.3389/fnhum.2010.00224
Mccoy, D., Arrigoni, M., & Gallaher, N. (2015). Optogenetics research drives new laser technologies. Recuperado de http://www.laserfocusworld.com/articles/print/volume-51/issue-06/biooptics-world/biooptics-features/optogenetics-optogenetics-research-drives-new-laser-technologies.html
Men, W., Falk, D., Sun, T., Chen, W., Li, J., Yin, D., Zang, L., & Fan, M. (2013). The corpus callosum of Albert Einstein‘s brain: another clue to his high intelligence?. doi.org/10.1093/brain/awt252
Mottron, L., & Burack, J. (2001). Enhanced perceptual functionning in the development of autism. Lawrence Earlbaum Associates,148. J. Burack, T. Charman, N. Yirmiya, P. Zelazo (Eds.) (2001), , The Development of Autism: Perspectives from Theory and Research.
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