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A generative growth model for thalamocortical axonal branching in primary visual cortex

Lookup NU author(s): Dr Roman Bauer

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

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.


Publication metadata

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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