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 1798 for “"Neuroscience"”.
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Odor mapping in neuroscience and design
The olfactory system remains one of the least well understood out of the five senses. In this thesis we examined the mappings of the odors to pathways in the brain as an initial assessment for the feasibility of digital odor. The results confirmed previous findings that each odor activated 1-6 …
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Dorsal stream : from algorithm to neuroscience
… been developed independently in the field of neuroscience and computer vision. We present a dorsal stream model that can be used for the recognition of actions as well as explaining neurophysiology in the dorsal stream. The model consists of a spatio-temporal feature detectors of increasing …
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Dynamical systems and their applications in neuroscience
… a background is given in dynamical systems and neuroscience. We elucidate what the problems are with some existing classifications of neural models, and suggest an improved version. We introduce the Phase Response Curve (PRC), which is a curve that describes the effect of an input on a periodic …
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Automation and scalability of in vivo neuroscience
Many in vivo neuroscience techniques are limited in terms of scale and suffer from inconsistencies because of the reliance on human operators for critical tasks. Ideally, automation would yield repeatable and reliable experimental procedures. Precision engineering would also allow us to perform …
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Applications of jump processes in epidemiology and neuroscience
… of jump processes in epidemiology and neuroscience. A jump process is a stochastic process that models the occurrence of discrete events over time. In epidemiology, the events correspond to catching an infection, recovering from a disease, etc. In neuroscience, the spike train of a …
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Recurrent neural networks in cognitive and vision neuroscience
This thesis investigates the development of novel training methodologies for biologically plausible neural networks, with a focus on models that incorporate recurrent dynamics characteristic of cortical circuits. First, we present an innovative approach for training stabilized supralinear networks, …
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Modeling neuroscience patient flow and inpatient bed management
… data from MGH. The model is focused on the neuroscience clinical specialties as a microcosm of the larger hospital since the neuroscience units (22 ICU beds and 64 floor beds) are directly affected by the hospital's important capacity issues (e.g., patient overflows into other units, …
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A Behavioural and Cognitive Neuroscience Investigation of Deceptive Communication
There is a rich literature on how people tell lies and detect them in others, but the underlying mechanisms are still poorly understood. The first aim of this thesis was to elucidate key cognitive and neural processes underlying cued (i.e., instructed) and uncued lies. The second aim, based on …
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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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Quintessence of Dust: Cognitive Neuroscience and an Actor's Process
… thesis examines theories provided by cognitive neuroscience and applies them to an actor’s process. In particular, this research addresses the subjectivity and intentionality of our consciousness and special as-if states of consciousness, supported by the work of John Searle and Antonio Damasio. …
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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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Towards an Artificial Neuroscience: Analytics for Language Model Interpretability
The growing deployment of neural language models demands greater understanding of their internal mechanisms. The goal of this thesis is to make progress on understanding the latent computations within large language models (LLMs) to lay the groundwork for monitoring, controlling, and aligning …
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Fundamental problems in Granger causality analysis of neuroscience data
… (GG-causality) has been widely applied in neuroscience because its frequency-domain and conditional forms appear well-suited to highly-multivariate oscillatory data. In this work, I analyze the statistical and structural properties of GG-causality in the context of neuroscience data …
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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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Experimental Knowledge in Cognitive Neuroscience: Evidence, Errors, and Inference
… and intervention in the constructs of cognitive neuroscience. Duhemian challenges and problems of underdetermination are often raised to argue that fNI is of little, if any, epistemic value for psychology. I show how the ES notions of severe tests and error probabilities can be applied in …
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Processing random signals in neuroscience, electrical engineering and operations research
… is the study of noise in electrical engineering, neuroscience, biomedical engineering, and operations research through mathematical models that describe, explain, predict and control dynamic phenomena. Noise is modeled through Brownian Motion and the research problems are mathematically addressed …
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