snudda

Create realistic networks of neurons, synapses placed using touch detection between axons and dendrites

https://github.com/hjorthmedh/snudda

Science Score: 49.0%

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CITATION.cff file
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codemeta.json file
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DOI references
Found 4 DOI reference(s) in README
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Academic publication links
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Committers with academic emails
5 of 18 committers (27.8%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (7.5%) to scientific vocabulary

Last synced: 7 months ago · JSON representation

Repository

Create realistic networks of neurons, synapses placed using touch detection between axons and dendrites

Basic Info

Host: GitHub
Owner: Hjorthmedh
License: gpl-3.0
Language: Python
Default Branch: master
Size: 966 MB

Statistics

Stars: 32
Watchers: 5
Forks: 20
Open Issues: 2
Releases: 2

Created over 6 years ago · Last pushed 7 months ago

Metadata Files

Readme License Codemeta

Summary of Snudda

Snudda creates the connectivity for realistic networks of simulated neurons in silico in a bottom up fashion that can then be simulated using the NEURON software. Neurons are placed within 3D meshes representing the structures of interest, with neural densities as seen in experiments. Based on reconstructed morphologies and neuron placement we can infer locations of putative synapses based on proximity between axon and dendrites. Projections between different structures can be added either using axon reconstructions, or by defining a connectivity map between regions. Putative synapses are pruned to match experimental pair-wise data on connectivity. Networks can be simulated either on desktop machines, or on super computers.

Contact details

Johannes Hjorth, Royal Institute of Technology (KTH) Human Brain Project hjorth@kth.se

Funding

Simulations were also performed on resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at PDC KTH partially funded by the Swedish Research Council through grant agreement no. 2022-06725.

The study was supported by the Swedish Research Council (VR-M-2020-01652), Swedish e-Science Research Centre (SeRC), Science for Life Lab, EU/Horizon 2020 no. 945539 (HBP SGA3) and No. 101147319 (EBRAINS 2.0 Project), European Union's Research and Innovation Program Horizon Europe under grant agreement No 101137289 (the Virtual Brain Twin Project), and KTH Digital Futures.

Horizon 2020 Framework Programme (785907, HBP SGA2); Horizon 2020 Framework Programme (945539, HBP SGA3); Vetenskapsrdet (VR-M-2017-02806, VR-M-2020-01652); Swedish e-science Research Center (SeRC); KTH Digital Futures. The computations are enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at PDC KTH partially funded by the Swedish Research Council through grant agreement no. 2018-05973. We acknowledge the use of Fenix Infrastructure resources, which are partially funded from the European Union's Horizon 2020 research and innovation programme through the ICEI project under the grant agreement No. 800858. Snudda is supported and featured on EBRAINS.

Citation

Please cite the first paper for the general Snudda network creation and simulation methods, and the second paper for the Striatal microcircutiry model.

Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda. J. J. Johannes Hjorth, Jeanette Hellgren Kotaleski, Alexander Kozlov. Neuroinform (2021). https://doi.org/10.1007/s12021-021-09531-w
The microcircuits of striatum in silico. J. J. Johannes Hjorth, Alexander Kozlov, Ilaria Carannante, Johanna Frost Nyln, Robert Lindroos, Yvonne Johansson, Anna Tokarska, Matthijs C. Dorst, Shreyas M. Suryanarayana, Gilad Silberberg, Jeanette Hellgren Kotaleski, Sten Grillner. Proceedings of the National Academy of Sciences (2020). https://doi.org/10.1073/pnas.2000671117

Installation

To install Snudda:

pip3 install snudda

For more information, see Github:

https://github.com/Hjorthmedh/Snudda/wiki/1.-User-installation

Jupyter Notebook examples

There are a number of examples for how to create and run networks on github which illustrates the functionality of Snudda. Several of these are created as short notebooks to showcase a particular feature or function.

https://github.com/Hjorthmedh/Snudda/tree/master/examples/notebooks

Command line example

Once installed Snudda can also be run from the command line, using the snudda command. Below is a small list of the relevant commands that can be used.

Creates an a json config file: snudda init <networkPath> --size XXX

Cell placement within volumes specified: snudda place <networkPath>

Touch detection of putative synapses: snudda detect <networkPath> [--hvsize hyperVoxelSize]

Prune the synapses snudda prune <networkPath> [--mergeonly]

Setup the input, obs you need to manually pick a input config file snudda input <networkPath> [--input yourInputConfig]

Run the network simulation using neuron snudda simulate <networkPath>

Plot figurs with some network analysis: snudda analyse <networkPath>

Show this help text snudda help me

Additional information:

https://snudda.readthedocs.io/ https://snudda.readthedocs.io/en/dev

Owner

Name: Johannes Hjorth
Login: Hjorthmedh
Kind: user
Company: Royal Institute of Technology, Stockholm

Repositories: 9
Profile: https://github.com/Hjorthmedh

CodeMeta (codemeta.json)

{
  "@context": "https://doi.org/10.5063/schema/codemeta-2.0",
  "type": "SoftwareSourceCode",
  "applicationCategory": "Neuroscience",
  "author": [
    {
      "@id": "https://orcid.org/0000-0002-9302-0750",
      "@type": "Person",
      "affiliation": {
        "@type": "Organization",
        "name": "Royal Institute of Technology (KTH)"
      },
      "email": "hjorth@kth.se",
      "familyName": "Hjorth",
      "givenName": "Johannes"
    },
    {
      "@type": "Person",
      "email": "wilhelm.thunberg@ki.se",
      "familyName": "Thunberg",
      "givenName": "Wilhelm"
    }
  ],
  "codeRepository": "https://github.com/hjorthmedh/Snudda",
  "dateCreated": "2024-12-06",
  "dateModified": "2025-10-21",
  "datePublished": "2021-07-01",
  "description": "Snudda creates the connectivity for realistic networks of simulated neurons in silico in a bottom up fashion that can then be simulated using the NEURON software. Neurons are placed within 3D meshes representing the structures of interest, with neural densities as seen in experiments. Based on reconstructed morphologies and neuron placement we can infer locations of putative synapses based on proximity between axon and dendrites. Projections between different structures can be added either using axon reconstructions, or by defining a connectivity map between regions. Putative synapses are pruned to match experimental pair-wise data on connectivity. Networks can be simulated either on desktop machines, or on super computers.",
  "downloadUrl": "https://files.pythonhosted.org/packages/5e/b2/ee6baf2c583a20dfb54d030943d2931882021ccd6aeb838caf91eaf012c9/snudda-2.2.6.2.tar.gz",
  "funder": [
    {
      "@type": "Organization",
      "name": "Royal Institute of Technology (KTH)"
    },
    {
      "@type": "Organization",
      "name": "European Commission"
    }
  ],
  "name": "Snudda",
  "programmingLanguage": "Python",
  "softwareRequirements": "Python 3.11+",
  "version": "2.2.6.2",
  "referencePublication": "https://doi.org/10.1007/s12021-021-09531-w",
  "license": "https://spdx.org/licenses/GPL-3.0-only.html"
}

GitHub Events

Total

Issues event: 2
Watch event: 4
Issue comment event: 4
Push event: 308
Pull request event: 88
Fork event: 3
Create event: 3

Last Year

Issues event: 2
Watch event: 4
Issue comment event: 4
Push event: 308
Pull request event: 88
Fork event: 3
Create event: 3

Committers

Last synced: about 3 years ago

All Time

Total Commits: 2,285
Total Committers: 18
Avg Commits per committer: 126.944
Development Distribution Score (DDS): 0.192

Top Committers

Name	Email	Commits
Johannes Hjorth	h**h@k**e	1,847
jofrony	j**y@g**m	204
Johannes Hjorth	H**h@u**m	93
bosse89	b**9@g**m	26
Robin De Schepper	r**3@g**m	26
Alexander Kozlov	a**v@k**e	22
Ilaria Carannante	i**c@k**e	20
wthun	w**g@g**m	11
William Scott Thompson	5**o@u**m	10
Robert @ ws	r**s@k**e	9
appukuttan-shailesh	a**h@g**m	5
Robert Lindroos	r**s@g**m	3
Johanna Frost Nylen	b**4@d**h	2
Johannes Hjorth	h**h@d**n	2
Johanna Frost Nylen	b**4@d**h	2
Johanna Frost Nylen	b**4@d**h	1
Johannes Hjorth	h**h@d**n	1
wilhelm	w**m@w**l	1

Committer Domains (Top 20 + Academic)

kth.se: 3 debian-gnu-linux-11.localdomain: 1 daint102.login.cscs.ch: 1 daint101.login.cscs.ch: 1 debian-gnu-linux-10-vm.localdomain: 1 daint105.login.cscs.ch: 1 ki.se: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time

Total issues: 8
Total pull requests: 252
Average time to close issues: about 1 year
Average time to close pull requests: 1 day
Total issue authors: 4
Total pull request authors: 9
Average comments per issue: 0.75
Average comments per pull request: 0.16
Merged pull requests: 229
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 3
Pull requests: 94
Average time to close issues: 1 minute
Average time to close pull requests: 11 minutes
Issue authors: 2
Pull request authors: 3
Average comments per issue: 0.0
Average comments per pull request: 0.1
Merged pull requests: 81
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

bosse89 (3)
wstho (2)
appukuttan-shailesh (2)
Hjorthmedh (1)

Pull Request Authors

Hjorthmedh (207)
jofrony (14)
wstho (14)
IlaCar (5)
wthun (4)
appukuttan-shailesh (3)
bosse89 (2)
apdavison (2)
robban80 (1)

Top Labels

Issue Labels

Pull Request Labels

Dependencies

docs/requirements.txt pypi

m2r2 *
mock *
myst-parser *
sphinx *
sphinx-argparse *

requirements.txt pypi

NEURON >=7.8.2
argparse *
bluepyopt >=1.11.7
cython *
h5py >=3.2.1
ipyparallel >=6.3.0
matplotlib >=3.3.4
mpi4py >=3.0.3
numba >=0.55.1
numexpr >=2.7.3
numpy >=1.20.2
psutil >=5.8.0
pyswarms >=1.3.0
pyzmq >=22.0.3
scipy >=1.6.3
setuptools *
snudda *
sonata >=0.0.2

snudda

Science Score: 49.0%

Repository

Basic Info

Statistics

Metadata Files

README.md

Summary of Snudda

Contact details

Funding

Citation

Installation

Jupyter Notebook examples

Command line example

Additional information:

Owner

CodeMeta (codemeta.json)

GitHub Events

Total

Last Year

Committers

All Time

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Dependencies