Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 77 for “"Computational Neuroscience"”.
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Machine Learning As Tool And Theory For Computational Neuroscience
Computational neuroscience is in the midst of constructing a new framework for understanding the brain based on the ideas and methods of machine learning. This is effort has been encouraged, in part, by recent advances in neural network models. It is also driven by a recognition of the complexity …
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A Computational Neuroscience Approach to Higher-Order Texture Perception
Natural images contain large amounts of structural information characterised by higher-order spatial correlations. Neurons have limited capacities, so the visual system must filter out non-salient information, but retain that which is behaviourally relevant. Previous research has concentrated on …
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A Computational Neuroscience Approach to Higher-Order Texture Perception
Natural images contain large amounts of structural information characterised by higher-order spatial correlations. Neurons have limited capacities, so the visual system must filter out non-salient information, but retain that which is behaviourally relevant. Previous research has concentrated on …
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Quantifying uncertainty in computational neuroscience with Bayesian statistical inference
Two key fields of computational neuroscience involve, respectively, the analysis of experimental recordings to understand the functional properties of neurons, and modeling how neurons and networks process sensory information in order to represent the environment. In both of these endeavors, it is …
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Computational models of the human visual cortex: on individual differences and ecologically valid input statistics
… object recognition. In this thesis I investigate computational models of the human visual cortex with regard to their ability to predict cortical responses to visual objects. In particular, I describe two factors playing an important role in using deep neural networks (DNNs) to better understand …
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Emergence
… Mars. The story follows Nate Ein, a prodigy in computational neuroscience, who loses his mother at a young age. He embarks on a single-minded techno-mythological quest to save his mother from death, and grant immortality to all. His quest forces him into a world of political intrigue and …
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Specific bio-modeling and analysis techniques at cellular and systems level
… PART I consists of three chapters focusing on computational neuroscience models at cellular level. The mechanisms of action of a drug on prefrontal cortical cells are elucidated with two possible hypotheses, and a systematic methodology to study the excitability of cells under inhibitory post …
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Shaping of Spike-Timing-Dependent Plasticity curve using interneuron and calcium dynamics
The field of Computational Neuroscience is where neuroscience and computational modelling merge together. It is an ever-emerging area of research where the level of biological modelling can range from small-scale cellular models, to the larger network scale models. This MSc Thesis will detail the …
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Recurrent convolutional neural networks as models of biological object recognition
… neural network models of vision dominate in both computational neuroscience and engineering. However, the primate visual system contains abundant recurrent connections. In this thesis, we investigate the addition of recurrent connections to the popular framework of convolutional neural networks …
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Characterization of a Spiking Neuron Model via a Linear Approach
… extensively researched as an essential issue in computational neuroscience. In this thesis, we examine the estimation problem of two different neuron models. In Chapter 2, We propose a modified Izhikevich model with an adaptive threshold. In our two-stage estimation approach, a linear least …
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Computational models of neuronal fear and addiction circuits
… OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] Computational Neuroscience provides tools to abstract and generalize principles of neuronal functions using mathematics, with applicability to the entire neuroscience spectrum. Subcircuits related to fear and addiction are considered at three …
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Mechanisms of Sensory Adaptation in the Primate Visual System
… In this work, employing visual psychophysics and computational neuroscience, we study the mechanisms by which the brain adapts to the sensory signals that it encounters in the natural environment. We found that the processes underlying motion perception in ecological vision are mediated by an …
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Human Origins: Hidden Nonlinear Dynamics in Brain-Culture Coevolution
… modernity. Using models from the fields of computational neuroscience and cultural evolution, I demonstrate that cognitive modernity can evolve through a parsimonious process, only requiring that the mammalian neocortex increase in volume alongside the cooperative exchange of cultural …
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Learning sparse features and metric in signal and image processing
… vision and machine learning. First introduced in computational neuroscience in the context of sparse coding in the visual system, sparse coding plays a key role in feature representation learning, as the over-complete dictionary allows more representation flexibility and efficiency, and captures …
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Integrate-and-fire modelling of neuronal systems with modulatory properties
… the single neuron level is an important part of computational neuroscience. Conductance-based models remain dominant because of their biophysical interpretation. Nevertheless, their high dimensionality and number of parameters complicate mathematical analysis and numerical simulation. …
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Learning invariant representations of actions and faces
… of wide ranging investigation in systems and computational neuroscience. However, advances in understanding the neural machinery of visual perception have not always translated in precise accounts of the computational principles dictating which representations of sensory input the human visual …
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Constructing and Analyzing Neural Network Dynamics for Information Objectives and Working Memory
… are long-standing challenges in the field of neuroscience. In this work, we blend ideas from computational neuroscience, information and control theories with machine learning to shed light on how certain key functions are encoded through the dynamics of neural circuits. In this regard, we …
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Advanced Statistical Learning Methods in Image Processing
… image processing and Human Connectome Project in computational neuroscience. These big and complex imaging datasets have facilitated a surge of interest in image processing at the interplay of statistics, computer vision, and medical science domains. While new approaches are constantly being …
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