Implementation of a Computational Model for Information Processing and Signaling from a Biological Neural Network of Neostriatum Nucleus
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| Format: | Artículo científico |
| Sprache: | en |
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Universidad Nacional Autónoma de México
2014
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| _version_ | 1876473892755734528 |
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| author | C. Sanchez-Vazquez |
| author_facet | C. Sanchez-Vazquez |
| contents | Implementation of a Computational Model for Information Processing and Signaling from a Biological Neural Network of Neostriatum Nucleus C. Sanchez-Vazquez M. Avila-Costa F. Cervantes-Pérez Ingeniería Guaranteed Safety Stock service time Dynamic Programming Automotive Industry Recently, several mathematical models have been deve loped to study and explain the way information is processed in the brain. The models published account for a myriad of perspectives from single neuron segments to neural networks, and lately, with the use of supercomputing facilities, to the study of whole environments of nuclei interacting for massive stimuli and processing. Some of the most complex neural structures -and also most studied- are basal ganglia nuclei in t he brain; amongst which we can find the Neostriatum. Currently, just a few papers about high scale biological-based computational modeling of this region have been published. It has been demonstrated that the Basal Ganglia r egion contains functions related to learning and decision making based on rules of the action-selection type, whic h are of particular interest for the machine autonomous-learning field. This knowledge could be clearly transferred between areas of research. The present work proposes a model of information processing, by integrating knowledge generat ed from widely accepted exper iments in both morphology and biophysics, through integrating theorie s such as the compartmental electric al model, the Rall’s cable equation, and the Hodking-Huxley particle potential regulations, among others. Additionally, the leaky integrator framework is incorporated in an adapted function. This was accomp lished through a computational environment prepared for high scale neural simulation which delivers data output equival ent to that from the orig inal model, and that can not only be analyzed as a Bayesian problem, but also successfully compared to the biological specimen. 2014 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47431368021 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.3 Vol.12 |
| format | Artículo científico |
| id | redalyc_47431368021 |
| institution | Redalyc |
| language | en |
| publishDate | 2014 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | Implementation of a Computational Model for Information Processing and Signaling from a Biological Neural Network of Neostriatum Nucleus C. Sanchez-Vazquez Ingeniería Guaranteed Safety Stock service time Dynamic Programming Automotive Industry Implementation of a Computational Model for Information Processing and Signaling from a Biological Neural Network of Neostriatum Nucleus C. Sanchez-Vazquez M. Avila-Costa F. Cervantes-Pérez Ingeniería Guaranteed Safety Stock service time Dynamic Programming Automotive Industry Recently, several mathematical models have been deve loped to study and explain the way information is processed in the brain. The models published account for a myriad of perspectives from single neuron segments to neural networks, and lately, with the use of supercomputing facilities, to the study of whole environments of nuclei interacting for massive stimuli and processing. Some of the most complex neural structures -and also most studied- are basal ganglia nuclei in t he brain; amongst which we can find the Neostriatum. Currently, just a few papers about high scale biological-based computational modeling of this region have been published. It has been demonstrated that the Basal Ganglia r egion contains functions related to learning and decision making based on rules of the action-selection type, whic h are of particular interest for the machine autonomous-learning field. This knowledge could be clearly transferred between areas of research. The present work proposes a model of information processing, by integrating knowledge generat ed from widely accepted exper iments in both morphology and biophysics, through integrating theorie s such as the compartmental electric al model, the Rall’s cable equation, and the Hodking-Huxley particle potential regulations, among others. Additionally, the leaky integrator framework is incorporated in an adapted function. This was accomp lished through a computational environment prepared for high scale neural simulation which delivers data output equival ent to that from the orig inal model, and that can not only be analyzed as a Bayesian problem, but also successfully compared to the biological specimen. 2014 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47431368021 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.3 Vol.12 |
| title | Implementation of a Computational Model for Information Processing and Signaling from a Biological Neural Network of Neostriatum Nucleus |
| topic | Ingeniería Guaranteed Safety Stock service time Dynamic Programming Automotive Industry |
| url | https://www.redalyc.org/articulo.oa?id=47431368021 |