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CITATION.cff
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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: Computational generation of long-range axonal morphologies
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Adrien
family-names: Berchet
email: [email protected]
affiliation: 'Blue Brain Project, EPFL'
orcid: 'https://orcid.org/0000-0001-6354-9947'
- family-names: Petkantchin
given-names: Rémy
orcid: 'https://orcid.org/0000-0001-8137-8811'
affiliation: 'Blue Brain Project, EPFL'
email: [email protected]
- given-names: Henry
family-names: Markram
affiliation: 'Blue Brain Project, EPFL'
email: [email protected]
- given-names: Lida
family-names: Kanari
email: [email protected]
affiliation: 'Blue Brain Project, EPFL'
orcid: 'https://orcid.org/0000-0002-9539-5070'
identifiers:
- type: doi
value: 10.1101/2024.10.16.618695
description: Preprint on BioRxiv
repository-code: 'https://github.com/BlueBrain/axon-synthesis'
url: 'https://axon-synthesis.readthedocs.io/en/stable/'
abstract: >-
Long-range axons are fundamental to brain connectivity and
functional organization, enabling communication between
different regions of the brain. Recent advances in
experimental techniques have yielded a substantial number
of whole-brain axonal reconstructions. While most previous
computational generative models of neurons have
predominantly focused on dendrites, generating realistic
axonal morphologies is challenging due to their distinct
targeting. In this study, we present a novel algorithm for
axon synthesis that combines algebraic topology with the
Steiner tree algorithm, an extension of the minimum
spanning tree, to generate both the local and long-range
compartments of axons. We demonstrate that our
computationally generated axons closely replicate
experimental data in terms of their morphological
properties. This approach enables the generation of
biologically accurate long-range axons that span large
distances and connect multiple brain regions, advancing
the digital reconstruction of the brain. Ultimately, our
approach opens up new possibilities for large-scale
in-silico simulations, advancing research into brain
function and disorders.
keywords:
- neuronal morphology
- axon synthesis
- Steiner tree
- algebraic topology
- brain connectivity
license: Apache-2.0