Skip to main content

Geometry-aware network motif analysis for neocortical microcircuits

Project description

neuromotifs

Python tools to load neuronal microcircuit geometry, generate geometry-aware null models, and quantify over/under-expression of 3-node motifs.

Paper: Neuron Morphological Asymmetry Explains Fundamental Network Stereotypy Across Neocortex (Gal et al.)

Install

pip install neuromotifs
# or, for dev
pip install -e .[dev]

Highlights

  • Motif counting for directed triplets (#1-#13)
  • Geometry-driven random graph generators (1st-5th order) mirroring the paper’s models
  • Reproducibility notebooks for Figures 1-4
  • Simple CLI: neuromotifs motifs, neuromotifs generate, neuromotifs fit

Quickstart

# TBD

Data

  • data/nmc/ contains tiny demonstrators only.
  • For full datasets, see data/README.md for scripted download instructions.

Citing

Please cite the paper and this package (see CITATION.cff).

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

neuromotifs-0.1.0a2.tar.gz (856.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

neuromotifs-0.1.0a2-py3-none-any.whl (853.9 kB view details)

Uploaded Python 3

File details

Details for the file neuromotifs-0.1.0a2.tar.gz.

File metadata

  • Download URL: neuromotifs-0.1.0a2.tar.gz
  • Upload date:
  • Size: 856.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.18

File hashes

Hashes for neuromotifs-0.1.0a2.tar.gz
Algorithm Hash digest
SHA256 9b501f826f7738e1c816a145adaf3f8892f40729856db25295725289a166d589
MD5 666de2f39537596ec461f630cad8f5ac
BLAKE2b-256 329cd7e99054a1be76afdaa7f87b02505f4ad5c83f045cd5b6ae0e30f341ef82

See more details on using hashes here.

File details

Details for the file neuromotifs-0.1.0a2-py3-none-any.whl.

File metadata

  • Download URL: neuromotifs-0.1.0a2-py3-none-any.whl
  • Upload date:
  • Size: 853.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.18

File hashes

Hashes for neuromotifs-0.1.0a2-py3-none-any.whl
Algorithm Hash digest
SHA256 ef9e738402a989e798199a27cbc4d4ef990116ae7342305be95ff7a6c2db9b95
MD5 9d26b62696b42f1ac1ee14cf17656dc2
BLAKE2b-256 2edb2a1e2bde2646804a524988ac622d10b6ab3108a3c1ca6766e70b531f867e

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page