Η εργασία ασχολείται με την ομαδοποίηση δεδομένων ανθρώπινου γονιδιόματος με την χρήση αλγόριθμων ομαδοποίοησης και νευρωνικών δικτύων για τον διαχωρισμό του δείγματος σε ομάδες με βάση το αν έχουν κάποιο είδος ασθένειας ή όχι ή για τον καθορισμό του τύπου της ασθένειας. Παρουσιάζονται κάποια πειράματα που έγιναν με την χρήση και των δύο μεθόδων. / This master thesis is an application of clustering algorithms and artificial neural networks on human dna data in order to cluster the data in groups depending on wether a person has or hasn't an illness or what type of ilness one has. The thesis shows the results of some experiments conducted using either technique (clustering, ANNs) and a combination of both.

Deep generative neural networks such as the Deep Belief Network and Deep Boltzmann Machines have been used successfully to model high dimensional visual data. However, they are not robust to common variations such as occlusion and random noise. In this thesis, we explore two strategies for improving the robustness of DBNs. First, we show that a DBN with sparse connections in the first layer is more robust to variations that are not in the training set. Second, we develop a probabilistic denoising algorithm to determine a subset of the hidden layer nodes to unclamp. We show that this can be applied to any feedforward network classifier with localized first layer connections. By utilizing the already available generative model for denoising prior to recognition, we show significantly better performance over the standard DBN implementations for various sources of noise on the standard and Variations MNIST databases.

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The electrical synapse was first described over 50 years ago. Since that time appreciation of its complexity and importance has grown, including the hypothesis that early transient formation of these synapses is important to adult patterns of connectivity in neural networks. Presented in this dissertation are studies utilizing identified neurons in cell culture from the snail Helisoma trivolvis to examine discrete periods of electrical synapse formation during regeneration with sustained or transient expression. Extensive knowledge of connectivity patterns of the buccal neurons of Helisoma in cell culture and the ganglia, provide a useful framework for looking at modulation and manipulation of electrical synapses and their impact and emerging connectivity in a simple neuronal network. Two types of electrical connections were observed those that were transient, between a B19 and a B110 and those that were sustained, between a B19 and another B19. Dopamine (DA) modulation of forming electrical synapses (FES) produces a synapse specific effect at those either destined to be transient (TES) or sustained (SES) and may be a direct effect on the gap junctions at the synapses, as is the case at TES, or an indirect effect on other membrane currents, as seen in SES. DA modulation produces different outcomes at SES-centered networks and TES-centered networks with respect to new chemical synapse formation, demonstrating network-dependent effects of electrical synapse modulation. Pharmacological blockade of chemical and electrical components at forming mixed synapses in some cases alters subsequent synapse formation although due to the variable nature does not appear to be a direct interaction between chemical and electrical synapses. Three-cell networks appear to display a balancing mechanism for overall electrical coupling when electrical synapses are blocked suggesting a competition for some resource in the construction or trafficking of gap junctions. In addition to electrophysiological examinations, network coupling can be assessed utilizing fluorescent calcium imaging to look at coincidence of calcium changes as an output for coupling between cells. This technique provides a useful tool for less invasive studies of neuronal networks and the impact of coupling at mixed synapses.

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The ease with which our brains learn to control our bodies belies intricate neural processing which remains poorly understood. We know that a network of brain regions work together in a carefully coordinated fashion to allow us to move from one place to another. In mammals, we know that the motor cortex plays a central role in this process, but precisely how its activity contributes to control is a matter of long and continued debate. In this thesis we demonstrate the need for developing mechanistic neural network models to address this question. Using such models, we show that contentious response properties of non-human primate primary motor cortex (M1) neurons can be understood as reflecting control processes which take into account the physics of the body. And we develop new computational techniques for teaching neural network models how to execute control. In the first study (Chapter 2), we critically examined a recently developed correlation-based descriptive model for characterizing the activity of M1 neuron activity. In the second study (Chapter 3), we developed neural network control laws which performed reaching and postural tasks using a physics model of the upper limb. We show that the population of artificial neurons in these networks exhibit preferences for certain directions of movement and certain forces applied during posture. These patterns parallel empirical observations in M1, and the model shows that the patterns reflect particular features of the biomechanics of the arm. The final study (Chapter 4) develops new techniques for building network models. To understand how the brain solves difficult control tasks we need to be able to construct mechanistic models which can do the same. And, we need to be able to construct controllers that compute via simple neuron-like units. In this study, we combine tools for automatic computation of derivatives with recently developed ideas about second-order approaches to optimization to build better neural network control laws. Taken together, this thesis helps develop arguments for, and the tools to build mechanistic neural network models to understand how motor cortex contributes to control of the body. / Thesis (Ph.D, Neuroscience) -- Queen's University, 2014-01-31 10:34:43.816

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