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Sampling Neuron Morphologies

View ORCID ProfileRoozbeh Farhoodi, View ORCID ProfileKonrad Paul Kording
doi: https://doi.org/10.1101/248385
Roozbeh Farhoodi
1Sharif University of Technology
2University of Pennsylvania
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Konrad Paul Kording
2University of Pennsylvania
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Abstract

The intricate morphology of neurons has fascinated since the dawn of neuroscience, and yet, it is hard to synthesize them. Current algorithms typically define a growth process with parameters that allow matching aspects of the morphologies. However, such algorithmic growth processes are far simpler than the biological ones. What is needed is an algorithm that, given a database of morphologies, produces more of those. Here, we introduce a generator for neuron morphologies that is based on a statistical sampling process. Our Reversible Jump Markov chain Monte Carlo (RJMCMC) method starts with a trivial neuron and iteratively perturbs the morphology bringing the features close to those of the database. By quantifying the statistics of the generated neurons, we find that it outperforms growth-based models for many features. Good generative models for neuron morphologies promise to be important both for neural simulations and for morphology reconstructions from imaging data.

Footnotes

  • roozbehfarhoudi{at}gmail.com, koerding{at}gmail.coml

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 15, 2018.
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Sampling Neuron Morphologies
Roozbeh Farhoodi, Konrad Paul Kording
bioRxiv 248385; doi: https://doi.org/10.1101/248385
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Sampling Neuron Morphologies
Roozbeh Farhoodi, Konrad Paul Kording
bioRxiv 248385; doi: https://doi.org/10.1101/248385

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