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Full-Text Articles in Neuroscience and Neurobiology
Self-Supervised Pretraining And Transfer Learning On Fmri Data With Transformers, Sean Paulsen
Self-Supervised Pretraining And Transfer Learning On Fmri Data With Transformers, Sean Paulsen
Dartmouth College Ph.D Dissertations
Transfer learning is a machine learning technique founded on the idea that knowledge acquired by a model during “pretraining” on a source task can be transferred to the learning of a target task. Successful transfer learning can result in improved performance, faster convergence, and reduced demand for data. This technique is particularly desirable for the task of brain decoding in the domain of functional magnetic resonance imaging (fMRI), wherein even the most modern machine learning methods can struggle to decode labelled features of brain images. This challenge is due to the highly complex underlying signal, physical and neurological differences between …
Multimodal Neuron Classification Based On Morphology And Electrophysiology, Aqib Ahmad
Multimodal Neuron Classification Based On Morphology And Electrophysiology, Aqib Ahmad
Graduate Theses, Dissertations, and Problem Reports
Categorizing neurons into different types to understand neural circuits and ultimately brain function is a major challenge in neuroscience. While electrical properties are critical in defining a neuron, its morphology is equally important. Advancements in single-cell analysis methods have allowed neuroscientists to simultaneously capture multiple data modalities from a neuron. We propose a method to classify neurons using both morphological structure and electrophysiology. Current approaches are based on a limited analysis of morphological features. We propose to use a new graph neural network to learn representations that more comprehensively account for the complexity of the shape of neuronal structures. In …