Learning the shape of neurons: Representation learning, generative modeling and hierarchical clustering
by Laura Hansel
Date of Examination:2025-11-19
Date of issue:2025-12-10
Advisor:Prof. Dr. Alexander Ecker
Referee:Prof. Dr. Alexander Ecker
Referee:Prof. Dr. Florentin Wörgötter
Persistent Address:
http://dx.doi.org/10.53846/goediss-11691
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Abstract
English
The remarkable diversity of neuronal morphologies has continued to fascinate researchers since Ramón y Cajal's pioneering work in 1911. A neuron's dendritic shape determines the inputs it receives, and, consequently, the computations it can perform. Therefore, in order to understand brain function, it is crucial to understand neuronal structure. For this analysis, we need methods that can systematically describe the morphological features. The advent of large-scale neuronal datasets has made it both necessary and feasible to use quantitative, data-driven approaches to analyze the neurons. In this thesis, we focus on "learning the shape of neurons" by learning representations of neuronal morphologies and examining their organization in relation to cortical gradients, laminar structure, and expert-defined cell types. To achieve this, we leverage a large-scale, serial-section electron microscopy dataset, containing more than 30,000 three-dimensional reconstructions of neuronal morphologies from the mouse early visual cortex. First, we aim to learn meaningful, data-driven representations directly from the raw 3D morphology of neurons. For this, we develop and refine methods for learning compact, low-dimensional feature embeddings -- "bar codes" -- that contain the neurons' structures. These bar codes uniquely identify the neurons, thereby providing a meaningful similarity metric for comparing morphologies. We evaluate the encoding performance of various supervised and unsupervised representation learning approaches by examining their latent spaces, and demonstrate alignment with established biological knowledge. Second, we want to understand the principles underlying morphological diversity. Therefore, we move beyond feature extraction, and introduce MorphOcc, a generative model of 3D surface shapes of neurons. The resulting learned embeddings not only capture morphological features with high detail but also support classification into expert-defined cell types. Moreover, by interpolating within the embedding space, the generative model is able to synthesize novel instances of neuronal morphologies without supervision. Third, given the latent spaces obtained in the previous steps, we raise the question as to how these embeddings are organized. To this end, we propose t-NEB, a hierarchical clustering algorithm designed to uncover structure at multiple scales without the need to predefine the number of clusters. As a proof of concept, we apply this method to multiple 2D synthetic datasets, as well as a real-world transcriptomic dataset. We demonstrate that it can not only cluster accurately but also recover a biologically meaningful hierarchy of transcriptomic cell types. These contributions establish a methodological foundation for modeling and understanding the diversity of neuronal shapes, offering a high potential to advance our knowledge about the neurons in the early visual system and beyond. The code is available at https://github.com/ecker-lab/neuron-shape-learning.
Keywords: neuronal morphologies; representation learning; generative modeling; hierarchical clustering; machine learning; deep learning; computational neuroscience; data science
