Estimation de paramètres de modèles de neurones biologiques sur une plate-forme de SNN (Spiking Neural Network) implantés "in silico"

Abstract : These works, which were conducted in a research group designing neuromimetic analog integrated circuits based on the Hodgkin-Huxley model, deal with the parameter estimation of biological neuron models. The first part of the manuscript tries to bridge the gap between neuron modeling and optimization. We focus our interest on the Hodgkin-Huxley model because it is used in the group for designing the neuromimetic circuits. There already existed an estimation method associated to the voltage-clamp technique. Nevertheless, this classical estimation method does not allow to extract precisely all parameters of the model, also in the second part, we propose an alternative method to jointly estimate all parameters of one ionic channel, avoiding the usual approximations. This method is based on the differential evolution algorithm. The third chapter is divided into three sections : the first two sections present the application of our new estimation method to two different problems, model fitting from biological data and development of an automated tuning of neuromimetic chips. In the third section, we propose an estimation technique using only membrane voltage recordings – easier to measure than ionic currents. Finally, the fourth and last chapter is a numerical experimentation preparing the implementation of small neural networks on neuromimetic chips. More specifically, we try to study the influence of cellular intrinsic properties on the global behavior of a neural network in the context of gamma oscillations.
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Laure Buhry. Estimation de paramètres de modèles de neurones biologiques sur une plate-forme de SNN (Spiking Neural Network) implantés "in silico". Micro et nanotechnologies/Microélectronique. Université Sciences et Technologies - Bordeaux I, 2010. Français. ⟨tel-00561396⟩

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