A comparative study on the unsupervised classification of rat neurons by their morphology
Abstract: An ongoing problem regarding the automatic classification of neurons by their morphology is the lack of consensus between experts on neuron types. Unsupervised clustering using persistent homology as a descriptor for the morphology of neurons helps tackle the problem of bias in feature selection and has the potential of aiding neuroscience research in developing a framework for automatic neuron classification. This thesis investigates how two different unsupervised machine learning algorithms would cluster persistence images of already labeled neurons and how similar their clusterings would be. The results showed that the clusterings done by both methods were highly similar and that there was a large variation within the neuronal types defined by experts.
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