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Geometrical Structure of Neuron Morphology

Farhoodi, Roozbeh | 2020

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 52732 (02)
  4. University: Sharif University of Technology
  5. Department: Mathematical Sciences
  6. Advisor(s): Fotouhi, Morteza
  7. Abstract:
  8. The tree structure of neuron morphologies has excited neuroscientists since their discovery in the 19-th century. Many theories assign computational meaning to morphologies, but it is still hard to generate realistic looking morphologies. There are a few growth models for generating neuron morphologies that correctly reproduce some features (e.g. branching angles) of morphologies, but they tend to fall short on other features. Here we present an approach that builds a generative model by extracting a set of human-chosen features from a database of neurons by using the naïve Bayes approach. Then by starting from a neuron with a soma we use statistical sampling techniques to generate morphologies with the desired features. Using this method on various sets of morphologies, we are able to automatically make new samples. Moreover, we present a pipeline to quantifying the effect of experimental choices such as staining methods on the extracted neuron morphologies. This is an important step to find the experimental biases in the morphological data.While massive morphology databases are emerging from connectomics approaches, a comprehensive generative model that can synthesize realistic morphologies promises to be useful for computational neuroscience
  9. Keywords:
  10. Computational Neuro Science ; Markov Chain Monte Carlo ; Bayes Probablity Theorm ; Neuron Morphology ; Neurons Classification ; Geometrical Tree

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