Author ORCID Identifier
Semester
Spring
Date of Graduation
2023
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Lane Department of Computer Science and Electrical Engineering
Committee Chair
Gianfranco Doretto
Committee Member
Donald Adjeroh
Committee Member
Katerina Goseva-Popstojanova
Abstract
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 addition, we design a self-supervised approach for learning representations of electrophysiology data with a convolutional neural network. Morphological and electrophysiology representations are then fused in different ways in an end-to-end approach. Our methods are tested on multiple datasets, and we show a performance that exceeds the state of the art in the single modalities, while we also establish the first baseline for the combined modalities.
Recommended Citation
Ahmad, Aqib, "Multimodal Neuron Classification based on Morphology and Electrophysiology" (2023). Graduate Theses, Dissertations, and Problem Reports. 11808.
https://researchrepository.wvu.edu/etd/11808
Included in
Artificial Intelligence and Robotics Commons, Bioinformatics Commons, Computational Neuroscience Commons, Data Science Commons