Science Score: 44.0%
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Low similarity (8.4%) to scientific vocabulary
Repository
MaviBeyin
Basic Info
- Host: GitHub
- Owner: Attilaali
- License: gpl-3.0
- Language: JavaScript
- Default Branch: main
- Size: 24.4 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
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.
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.
Owner
- Name: Dr. Ali Attila
- Login: Attilaali
- Kind: user
- Repositories: 1
- Profile: https://github.com/Attilaali
Artificial Intelligence Technologies, AI Algorithms, Applications of DeepLearning & ComputerVision, Computer Version Algorithms & Applications, etc.
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'
identifiers:
- type: doi
value: 10.1101/2020.04.15.040410
description: The DOI of the preprint version.
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 structural
alterations associated with diseased brain states.
Recently, we introduced a topological descriptor
that reliably categorizes dendritic morphologies.
We apply this descriptor to digitally synthesize
dendrites to address the challenge of insufficient
biological reconstructions. The synthesized
cortical dendrites are statistically
indistinguishable from the corresponding
reconstructed dendrites in terms of
morpho-electrical properties and connectivity. This
topology-guided synthesis enables the rapid digital
reconstruction of entire brain regions from
relatively few reference cells, thereby allowing
the investigation of links between neuronal
morphologies and brain function across different
spatio-temporal scales. We synthesized cortical
networks based on structural alterations of
dendrites associated with medical conditions and
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