Author ORCID Identifier



Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Lane Department of Computer Science and Electrical Engineering

Committee Chair

Gianfranco Doretto

Committee Member

Donald Adjeroh

Committee Member

Katerina Goseva-Popstojanova


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.