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Lookup NU author(s): Dr Roman BauerORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Axonal morphology displays large variability and complexity, yet the canonical regularities of the cortex suggest that such wiring is based on the repeated initiation of a small set of genetically encoded rules. Extracting underlying developmental principles can hence shed light on what genetically encoded instructions must be available during cortical development. Within a generative model, we investigate growth rules for axonal branching patterns in cat area 17, originating from the lateral geniculate nucleus of the thalamus. This target area of synaptic connections is characterized by extensive ramifications and a high bouton density, characteristics thought to preserve the spatial resolution of receptive fields and to enable connections for the ocular dominance columns. We compare individual and global statistics, such as a newly introduced length-weighted asymmetry index and the global segment-length distribution, of generated and biological branching patterns as the benchmark for growth rules. We show that the proposed model surpasses the statistical accuracy of the Galton-Watson model, which is the most commonly employed model for biological growth processes. In contrast to the Galton-Watson model, our model can recreate the log-normal segment-length distribution of the experimental dataset and is considerably more accurate in recreating individual axonal morphologies. To provide a biophysical interpretation for statistical quantifications of the axonal branching patterns, the generative model is ported into the physically accurate simulation framework of Cx3D. In this 3D simulation environment we demonstrate how the proposed growth process can be formulated as an interactive process between genetic growth rules and chemical cues in the local environment.
Author(s): Kassraian-Fard P, Pfeiffer M, Bauer R
Publication type: Article
Publication status: Published
Journal: PLoS computational biology
Year: 2020
Volume: 16
Issue: 2
Online publication date: 13/02/2020
Acceptance date: 06/08/2019
Date deposited: 24/02/2020
ISSN (print): 1553-734X
ISSN (electronic): 1553-7358
Publisher: Public Library of Science
URL: https://doi.org/10.1371/journal.pcbi.1007315
DOI: 10.1371/journal.pcbi.1007315
PubMed id: 32053598
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