https://github.com/bluebrain/neurots
Topological Neuron Synthesis
Science Score: 62.0%
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Found 3 DOI reference(s) in README -
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10 of 12 committers (83.3%) from academic institutions -
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Low similarity (14.6%) to scientific vocabulary
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Repository
Topological Neuron Synthesis
Basic Info
- Host: GitHub
- Owner: BlueBrain
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://neurots.readthedocs.io/en/stable
- Size: 7.57 MB
Statistics
- Stars: 39
- Watchers: 8
- Forks: 5
- Open Issues: 4
- Releases: 12
Topics
Metadata Files
README.md
[!WARNING] The Blue Brain Project concluded in December 2024, so development has ceased under the BlueBrain GitHub organization. Future development will take place at: https://github.com/openbraininstitute/NeuroTS
NeuroTS
Computational generation of artificial neuronal trees based on the topology of reconstructed cells and their statistical properties.
Main usage
Neuronal morphologies provide the foundation for the electrical behavior of neurons, the connectomes they form, and the dynamical properties of the brain. Comprehensive neuron models are essential for defining cell types, discerning their functional roles, and investigating brain disease related dendritic alterations. However, a lack of understanding of the principles underlying neuron morphologies has hindered attempts to computationally synthesize morphologies for decades. We introduce a synthesis algorithm based on a topological descriptor of neurons, which enables the rapid digital reconstruction of entire brain regions from few reference cells. This topology-guided synthesis (NeuroTS) generates dendrites that are statistically similar to biological reconstructions in terms of morpho-electrical and connectivity properties and offers a significant opportunity to investigate the links between neuronal morphology and brain function across different spatio-temporal scales.
NeuroTS can be used for the creation of neuronal morphologies from biological reconstructions. The user needs to extract the distributions of topological and statistical properties using the software in order to create the necessary synthesis inputs. Examples of parameters and distributions can be found in the Parameters and distributions page of the doc.
Once the input_parameters and input_distributions have been defined, then NeuroTS can generate one or multiple cells based on the respective inputs. The generated cells can be saved in a variety of file formats (SWC, ASC, H5) so that they can be analyzed and visualized by a variety of different software packages. You can find examples on how to extract distributions, generate cells and run basic validations below.
Examples
We provide some basic examples to showcase the basic functionality of NeuroTS:
* synthesize a single neuron from a basic set of inputs
* synthesize many neurons with the same input parameters and distributions
* synthesize a single neuron with its diameters using a simple method
* synthesize a single neuron with its diameters using an external diametrizer
* extract parameters and distributions that can be used as synthesis inputs
All the scripts of these examples and the required input data are located in the examples directory of the repository.
More information can be found in Examples page of the doc.
Installation
It is recommended to install NeuroTS using pip:
bash
pip install neurots
Citation
When you use the NeuroTS software or method for your research, we ask you to cite the publication associated to this repository (use the Cite this repository button on the main page of the code).
Funding & Acknowledgment
The development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.
For license and authors, see LICENSE.txt and AUTHORS.md respectively.
Copyright © 2022 Blue Brain Project/EPFL
Owner
- Name: The Blue Brain Project
- Login: BlueBrain
- Kind: organization
- Email: bbp.opensource@epfl.ch
- Location: Geneva, Switzerland
- Website: https://portal.bluebrain.epfl.ch/
- Repositories: 226
- Profile: https://github.com/BlueBrain
Open Source Software produced and used by the Blue Brain Project
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
Computational synthesis of cortical dendritic
morphologies.
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- family-names: Kanari
given-names: Lida
orcid: 'https://orcid.org/0000-0002-9539-5070'
- family-names: Dictus
given-names: Hugo
- family-names: Chalimourda
given-names: Athanassia
- family-names: Van Geit
given-names: Werner
orcid: 'https://orcid.org/0000-0002-2915-720X'
- family-names: Coste
given-names: Benoit
- family-names: Shillcock
given-names: Julian
orcid: 'https://orcid.org/0000-0002-7885-735X'
- given-names: Kathryn
family-names: Hess
orcid: 'https://orcid.org/0000-0003-2788-9754'
- family-names: Markram
given-names: Henry
orcid: 'https://orcid.org/0000-0001-6164-2590'
- family-names: Arnaudon
given-names: Alexis
orcid: 'https://orcid.org/0000-0001-9486-1458'
identifiers:
- type: doi
value: 10.1016/j.celrep.2022.110586
description: The DOI of the related article.
repository-code: 'https://github.com/BlueBrain/NeuroTS'
abstract: >-
Neuronal morphologies provide the foundation for the electrical behavior of
neurons, the connectomes they form, and the dynamical properties of the brain.
Comprehensive neuron models are essential for defining cell types, discerning
their functional roles, and investigating brain-disease-related dendritic
alterations. However, a lack of understanding of the principles underlying
neuron morphologies has hindered attempts to computationally synthesize
morphologies for decades. We introduce a synthesis algorithm based on a
topological descriptor of neurons, which enables the rapid digital
reconstruction of entire brain regions from few reference cells. This
topology-guided synthesis generates dendrites that are statistically similar to
biological reconstructions in terms of morpho-electrical and connectivity
properties and offers a significant opportunity to investigate the links between
neuronal morphology and brain function across different spatiotemporal scales.
Synthesized cortical networks based on structurally altered dendrites associated
with diverse brain pathologies revealed principles linking branching properties
to the structure of large-scale
networks.
keywords:
- Dendritic morphology
- Topological synthesis
- Artificial neuron
- Topological Morphology Descriptor
- Morphological synthesis
license: GPL-3.0
GitHub Events
Total
- Release event: 1
- Watch event: 3
- Delete event: 8
- Issue comment event: 1
- Push event: 15
- Pull request review comment event: 19
- Pull request review event: 21
- Pull request event: 11
- Create event: 6
Last Year
- Release event: 1
- Watch event: 3
- Delete event: 8
- Issue comment event: 1
- Push event: 15
- Pull request review comment event: 19
- Pull request review event: 21
- Pull request event: 11
- Create event: 6
Committers
Last synced: 12 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Adrien Berchet | a****t@e****h | 88 |
| kanari | l****i@e****h | 62 |
| Benoît Coste | b****e@e****h | 41 |
| Alexis Arnaudon | a****n@e****h | 37 |
| Eleftherios Zisis | e****s@e****h | 14 |
| Arseny V. Povolotsky | a****y@e****h | 10 |
| dependabot[bot] | 4****] | 9 |
| alex4200 | a****z@e****h | 4 |
| aleksei sanin | a****n@e****h | 4 |
| jacquemi-bbp | 6****p | 1 |
| Erik Heeren | e****n@o****g | 1 |
| Julien Francioli | j****i@e****h | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 12
- Total pull requests: 101
- Average time to close issues: about 1 month
- Average time to close pull requests: 27 days
- Total issue authors: 5
- Total pull request authors: 9
- Average comments per issue: 2.25
- Average comments per pull request: 1.92
- Merged pull requests: 90
- Bot issues: 0
- Bot pull requests: 9
Past Year
- Issues: 0
- Pull requests: 9
- Average time to close issues: N/A
- Average time to close pull requests: 3 days
- Issue authors: 0
- Pull request authors: 3
- Average comments per issue: 0
- Average comments per pull request: 0.44
- Merged pull requests: 9
- Bot issues: 0
- Bot pull requests: 2
Top Authors
Issue Authors
- arnaudon (5)
- adrien-berchet (2)
- riddick-the-furyan (2)
- jacquemi-bbp (1)
- KeremKurban (1)
Pull Request Authors
- adrien-berchet (55)
- arnaudon (33)
- dependabot[bot] (12)
- alex4200 (4)
- lidakanari (3)
- eleftherioszisis (3)
- jacquemi-bbp (2)
- jazz031195 (1)
- bbpgithubaudit (1)
Top Labels
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Packages
- Total packages: 1
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Total downloads:
- pypi 125 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 13
- Total maintainers: 4
pypi.org: neurots
Synthesis of artificial neurons using their topological profiles package.
- Homepage: https://NeuroTS.readthedocs.io
- Documentation: https://neurots.readthedocs.io/
- License: Apache License 2.0
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Latest release: 3.7.0
published about 1 year ago
Rankings
Maintainers (4)
Dependencies
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