Publicación: Análisis de variación de frecuencia en cultivos neuronales para la caracterización de la respuesta al estímulo
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2017-08-05
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info:eu-repo/semantics/openAccess
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Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
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Los cultivos neuronales in vitro embedidos en un sistema de control de lazo cerrado se han utilizado recientemente para estudiar las dinámicas neuronales. Este proceso permite el desarrollo de neuronas en un ambiente controlado con el propósito de explorar las capacidades computacionales de redes neuronales biológicas. Debido a las propiedades intrínsecas de los cultivos neuronales in vitro y la forma en que el tejido neuronal crece en ellos, las formas en que las señales se transmiten y generan dentro y a lo largo del cultivo son difíciles de caracterizar. El código neural está formado por patrones de spikes cuyas propiedades son en esencia no lineales y no estacionarias. El enfoque habitual para esta caracterización ha sido el uso del histograma de tiempo post-estímulo (PSTH). El PSTH se calcula contando los spikes detectados en cada electrodo del cultivo neuronal durante la ventana de tiempo tras un estímulo en uno de los electrodos. La principal estrategia empleada hasta la actualidad consiste en encontrar pares de electrodos Cn = Eest,Eresp disjuntos de forma que C1 6= C2. El objetivo de este trabajo es explorar otras formas posibles de extraer información relevante de la respuesta global al estímulo en el cultivo mediante el estudio de las variaciones en los patrones a lo largo del tiempo para la tasa de disparo(firing rate-FR), estimada a partir de intervalos interspike en cada electrodo. A continuación se pueden aplicar métodos de aprendizaje automático para distinguir el electrodo estimulado a raíz de la la respuesta global del cultivo, con el fin de obtener una mejor caracterización del cultivo y sus capacidades computacionales, de modo que pueda ser útil para aplicaciones robóticas.
In vitro neuronal cultures embodied in a closed-loop control system have been used recently to study neuronal dynamics. This allows the development of neurons in a controlled environment with the purpose of exploring the computational capabilities of such biological neural networks. Due to the intrinsic properties of in vitro neuronal cultures and how the neuronal tissue grows in them, the ways in which signals are transmitted and generated within and throughout the culture can be difficult to characterize. The neural code is formed by patterns of spikes whose properties are in essence non linear and non stationary. The usual approach for this characterization has been the use the post-stimulus time histogram (PSTH). The PSTH is calculated by counting the spikes detected in each neuronal culture electrode during some time windows after a stimulus in one of the electrodes. The objective is to find pairs of electrodes where stimulation in one of the pair produces a response in the other but not in the rest of electrodes in other pairs. The aim of this work is to explore possible ways of extracting relevant information from the global response to culture stimulus by studying the patterns of variation over time for the firing rate, estimated from inverse interspike intervals, in each electrode. Machine-learning methods can then be applied to distinguish the stimulated electrode from the whole culture response, in order to obtain a better characterization of the culture and its computational capabilities so it can be useful for robotic applications.
In vitro neuronal cultures embodied in a closed-loop control system have been used recently to study neuronal dynamics. This allows the development of neurons in a controlled environment with the purpose of exploring the computational capabilities of such biological neural networks. Due to the intrinsic properties of in vitro neuronal cultures and how the neuronal tissue grows in them, the ways in which signals are transmitted and generated within and throughout the culture can be difficult to characterize. The neural code is formed by patterns of spikes whose properties are in essence non linear and non stationary. The usual approach for this characterization has been the use the post-stimulus time histogram (PSTH). The PSTH is calculated by counting the spikes detected in each neuronal culture electrode during some time windows after a stimulus in one of the electrodes. The objective is to find pairs of electrodes where stimulation in one of the pair produces a response in the other but not in the rest of electrodes in other pairs. The aim of this work is to explore possible ways of extracting relevant information from the global response to culture stimulus by studying the patterns of variation over time for the firing rate, estimated from inverse interspike intervals, in each electrode. Machine-learning methods can then be applied to distinguish the stimulated electrode from the whole culture response, in order to obtain a better characterization of the culture and its computational capabilities so it can be useful for robotic applications.
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Facultades y escuelas::E.T.S. de Ingeniería Informática
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Inteligencia Artificial