Implementation of a Computational Model for Information Processing and Signaling from a Biological Neural Network of Neostriatum Nucleus

 

C. Sánchez-Vázquez*¹, M. Ávila-Costa² and F. Cervantes-Pérez³

 

^1,2 Laboratorio de Neuromorfología Experimental y Aplicada. Facultad de Estudios Superiores Iztacala. Universidad Nacional Autónoma de México, México, D. F. *csvm@unam.mx

³ Universidad Abierta y a Distancia de México, México, D. F., México.

 

ABSTRACT

Recently, several mathematical models have been developed 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 the 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 region contains functions related to learning and decision making based on rules of the action-selection type, which 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 generated from widely accepted experiments in both morphology and biophysics, through integrating theories such as the compartmental electrical 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 accomplished through a computational environment prepared for high scale neural simulation which delivers data output equivalent to that from the original model, and that can not only be analyzed as a Bayesian problem, but also successfully compared to the biological specimen.

Keywords: Safety Stock, Guaranteed-service time, Dynamic Programming, Automotive Industry.

 

RESUMEN

Recientemente se han desarrollado modelos matemáticos que permiten explicar y definir a través de la ingeniería la manera como se procesa la información de señales eléctricas producidas por iones en el sistema nervioso de los seres vivos. Se han diseñado numerosas propuestas de este tipo de lo discreto a lo masivo, que operan como segmentos de una neurona, una red, y en últimas fechas con ayuda del supercómputo, hasta conjuntos de núcleos que interactúan en entornos de estímulos y procesamiento a gran escala. De las estructuras neurales más complejas y de más interés ha sido la del grupo denominado de los Ganglios Basales, de los que el Neoestriado forma parte, y sobre el cual se han hecho pocos trabajos de modelado computacional. Se ha demostrado que en esta región residen funciones de aprendizaje, y otras relacionadas con la toma de decisiones bajo las reglas de acción-selección que son ampliamente estudiadas en el aprendizaje autónomo computacional, permitiendo transferir el conocimiento de un campo de investigación a otro. El presente trabajo propone un modelo computacional en tiempo real, a través de integrar el conocimiento obtenido de experimentos ampliamente aceptados en biofísica, aplicando la teorías de compartimientos electrónicos, de la ecuación de cable de Rall, las leyes de potencial de partículas Hodkgin-Huxley, entre otros. Dichos modelos se incorporan en un entorno basado en la función de integrador con fugas, a través de un ambiente computacional de simulación neural a gran escala, que entrega una salida de datos equivalente al modelo biológico, susceptible a ser analizada como un problema Bayesiano, y comparada con el espécimen biológico con éxito.

 

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Acknowledgment

The author thank Posgrado en Ciencias Biologicas of National University of Mexico for the received formation during his postgraduate studies. This work was supported by PAPCA-Iztacala UNAM-2014-2015, and PAPIIT-DGAPA UNAM IN215114 grants.

 

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