Spelling suggestions: "subject:"biological neuronal""
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Scalable parallel architecture for biological neural simulation on hardware platformsPourhaj, Peyman 04 October 2010
Difficulties and dangers in doing experiments on living systems and providing a
testbed for theorists make the biologically detailed neural simulation an essential part of
neurobiology. Due to the complexity of the neural systems and dynamic properties of the
neurons simulation of biologically realistic models is very challenging area. Currently all
general purpose simulator are software based. Limitation on the available processing
power provides a huge gap between the maximum practical simulation size and human
brain simulation as the most complex neural system. This thesis aimed at providing a
hardware friendly parallel architecture in order to accelerate the simulation process.<p>
This thesis presents a scalable hierarchical architecture for accelerating simulations of
large-scale biological neural systems on field-programmable gate arrays (FPGAs). The
architecture provides a high degree of flexibility to optimize the parallelization ratio
based on available hardware resources and model specifications such as complexity of
dendritic trees. The whole design is based on three types of customized processors and a
switching module. An addressing scheme is developed which allows flexible integration
of various combination of processors. The proposed addressing scheme, design
modularity and data process localization allow the whole system to extend over multiple
FPGA platforms to simulate a very large biological neural system.<p>
In this research Hodgkin-Huxley model is adopted for cell excitability. Passive
compartmental approach is used to model dendritic tree with any level of complexity.
The whole architecture is verified in MATLAB and all processor modules and the
switching unit implemented in Verilog HDL and Schematic Capture. A prototype
simulator is integrated and synthesized for Xilinx V5-330t-1 as the target FPGA. While
not dependent on particular IP (Intellectual Property) cores, the whole implementation is
based on Xilinx IP cores including IEEE-754 64-bit floating-point adder and multiplier
cores. The synthesize results and performance analyses are provided.
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Scalable parallel architecture for biological neural simulation on hardware platformsPourhaj, Peyman 04 October 2010 (has links)
Difficulties and dangers in doing experiments on living systems and providing a
testbed for theorists make the biologically detailed neural simulation an essential part of
neurobiology. Due to the complexity of the neural systems and dynamic properties of the
neurons simulation of biologically realistic models is very challenging area. Currently all
general purpose simulator are software based. Limitation on the available processing
power provides a huge gap between the maximum practical simulation size and human
brain simulation as the most complex neural system. This thesis aimed at providing a
hardware friendly parallel architecture in order to accelerate the simulation process.<p>
This thesis presents a scalable hierarchical architecture for accelerating simulations of
large-scale biological neural systems on field-programmable gate arrays (FPGAs). The
architecture provides a high degree of flexibility to optimize the parallelization ratio
based on available hardware resources and model specifications such as complexity of
dendritic trees. The whole design is based on three types of customized processors and a
switching module. An addressing scheme is developed which allows flexible integration
of various combination of processors. The proposed addressing scheme, design
modularity and data process localization allow the whole system to extend over multiple
FPGA platforms to simulate a very large biological neural system.<p>
In this research Hodgkin-Huxley model is adopted for cell excitability. Passive
compartmental approach is used to model dendritic tree with any level of complexity.
The whole architecture is verified in MATLAB and all processor modules and the
switching unit implemented in Verilog HDL and Schematic Capture. A prototype
simulator is integrated and synthesized for Xilinx V5-330t-1 as the target FPGA. While
not dependent on particular IP (Intellectual Property) cores, the whole implementation is
based on Xilinx IP cores including IEEE-754 64-bit floating-point adder and multiplier
cores. The synthesize results and performance analyses are provided.
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Expanding multilayer perceptrons with a brain inspired activation algorithm : Experimental comparison of the performance of an activation enhanced multi layer perceptronWajud Abdul Aziz, Karar, Gripenberg, Kim Emil Leonard January 2022 (has links)
Machine learning is a field that is inspired by how humans and, by extension, the brain learns.The brain consists of a biological neural network that has neurons that are either active or inactive. Modern-day artificial intelligence is loosely based on how biological neural networks function. This paper investigates whether a multi layered perceptron that utilizes inactive/active neurons can reduce the number of active neurons during the forward and backward pass while maintaining accuracy. This is done by implementing a multi layer perceptron using a python environment and building a neuron activation algorithm on top of it. Results show that it ispossible to reduce the number of active neurons by around 30% with a negligible impact on test accuracy. Future works include algorithmic improvements and further testing if it is possible to reduce the total amount of mathematical operations in other neural network architectures with a bigger computational overhead.
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Estimation de paramètres de modèles de neurones biologiques sur une plate-forme de SNN (Spiking Neural Network) implantés "insilico"Buhry, Laure 21 September 2010 (has links)
Ces travaux de thèse, réalisés dans une équipe concevant des circuits analogiques neuromimétiques suivant le modèle d’Hodgkin-Huxley, concernent la modélisation de neurones biologiques, plus précisément, l’estimation des paramètres de modèles de neurones. Une première partie de ce manuscrit s’attache à faire le lien entre la modélisation neuronale et l’optimisation. L’accent est mis sur le modèle d’Hodgkin- Huxley pour lequel il existait déjà une méthode d’extraction des paramètres associée à une technique de mesures électrophysiologiques (le voltage-clamp) mais dont les approximations successives rendaient impossible la détermination précise de certains paramètres. Nous proposons dans une seconde partie une méthode alternative d’estimation des paramètres du modèle d’Hodgkin-Huxley s’appuyant sur l’algorithme d’évolution différentielle et qui pallie les limitations de la méthode classique. Cette alternative permet d’estimer conjointement tous les paramètres d’un même canal ionique. Le troisième chapitre est divisé en trois sections. Dans les deux premières, nous appliquons notre nouvelle technique à l’estimation des paramètres du même modèle à partir de données biologiques, puis développons un protocole automatisé de réglage de circuits neuromimétiques, canal ionique par canal ionique. La troisième section présente une méthode d’estimation des paramètres à partir d’enregistrements de la tension de membrane d’un neurone, données dont l’acquisition est plus aisée que celle des courants ioniques. Le quatrième et dernier chapitre, quant à lui, est une ouverture vers l’utilisation de petits réseaux d’une centaine de neurones électroniques : nous réalisons une étude logicielle de l’influence des propriétés intrinsèques de la cellule sur le comportement global du réseau dans le cadre des oscillations gamma. / These works, which were conducted in a research group designing neuromimetic 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. 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, so 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 chaper 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 mesure than ionic currents. Finally, the fourth and last chapter is a theoretical study 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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