Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 2 DOI reference(s) in README -
✓Academic publication links
Links to: biorxiv.org, frontiersin.org -
○Committers with academic emails
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (9.6%) to scientific vocabulary
Repository
Spiking Neural Network of CBGT pathways
Basic Info
Statistics
- Stars: 5
- Watchers: 4
- Forks: 4
- Open Issues: 2
- Releases: 0
Metadata Files
README.md
CBGT
This repository contains code for implementing the spiking cortico-basal ganglia-thalamus (CBGT) network and drift-diffusion model (DDM) fits described in the manuscript Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making.
The code requires several dependencies to be installed (see below for instructions). After completing the installation procedure below, the demo notebook can be downloaded and opened inside Jupyter.
Requirements
Installation Instructions
```sh
create a new conda environment with python 3.6
and hit 'y' to verify the install
conda create -n cbgt_env python=3.6 anaconda ipykernel
activate 'cbgt_env' environment
source activate cbgt_env
use conda (not pip) to install pymc
conda install pymc=2.3.6 --no-deps
install hddm and kabuki
pip install --upgrade kabuki hddm
finally install numpy version 1.11.3
(avoids hddm incompatibility with later numpy)
pip install numpy==1.11.3
install cbgt package
pip install -U cbgt --no-cache-dir ```
- After installing everything, run
jupyter notebookin your terminal to start Jupyter in your browser - Drag/drop the demo notebook (
CBGT_PLOSCompBio2019_Demo.ipynb) into the Jupyter browser window
Miscellaneous:
- HDDM resources: (methods paper, demo, how-to)
- Jupyter Notebook tutorial
Owner
- Name: CoAxLab
- Login: CoAxLab
- Kind: organization
- Repositories: 60
- Profile: https://github.com/CoAxLab
GitHub Events
Total
Last Year
Committers
Last synced: almost 3 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Matthew Clapp | c****m | 39 |
| Kyle Dunovan | d****k@g****m | 30 |
| Tim Verstynen | t****v@g****m | 4 |
Issues and Pull Requests
Last synced: almost 2 years ago
All Time
- Total issues: 1
- Total pull requests: 2
- Average time to close issues: N/A
- Average time to close pull requests: 4 days
- Total issue authors: 1
- Total pull request authors: 1
- Average comments per issue: 3.0
- Average comments per pull request: 0.5
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 2
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- NeolithEra (1)
Pull Request Authors
- dependabot[bot] (2)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 61 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 6
- Total maintainers: 1
pypi.org: cbgt
CBGT contains code for implementing the CBGT network and drift-diffusion model (DDM) fits described in the manuscript Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making.
- Homepage: http://github.com/CoAxLab/CBGT
- Documentation: https://cbgt.readthedocs.io/
- License: BSD License
-
Latest release: 0.0.6
published over 7 years ago
Rankings
Maintainers (1)
Dependencies
- future *
- matplotlib >=1.4.3
- numpy ==1.11.3
- pandas >=0.15.1
- seaborn >=0.5.1
- future *
- matplotlib >=1.4.3
- numpy ==1.11.3
- pandas >=0.15.1
- seaborn >=0.5.1