Science Score: 54.0%
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✓CITATION.cff file
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✓codemeta.json file
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✓DOI references
Found 2 DOI reference(s) in README -
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○Scientific vocabulary similarity
Low similarity (19.2%) to scientific vocabulary
Keywords
Repository
A PyTorch-based framework for neuromorphic modeling.
Statistics
- Stars: 2
- Watchers: 5
- Forks: 2
- Open Issues: 20
- Releases: 5
Topics
Metadata Files
README.md
Sapicore
A PyTorch-Based Spiking Neural Network Simulation Framework
Sapicore is a spiking neural network (SNN) simulator, built with PyTorch and designed for neuroscience-inspired models with realistic architectures and dynamics.
We provide a simple API for:
- Data loading and transformation.
- Initializing and connecting network components.
- Extending base neurons (e.g., LIF) and synapses (e.g., STDP).
- Orchestrating experiment pipelines.
- Selectively logging intermediate results.
- Deploying neuromorphic networks as machine learning models.
- Visualizing, interpreting, and analyzing output.
Users may configure networks in YAML or directly in code. Develop custom neuron, synapse, network, and data loader classes.
We made component extension and customization easy. Default implementations of common models used in computational neuroscience and neuromorphic machine learning applications are provided out of the box.
Sapicore interfaces with industry-standard ML libraries, including scikit-learn.
A project of the Computational Physiology Lab @ Cornell University.
Current Release
Sapicore 0.4 is runtime- and memory-optimized. This beta version includes:
- Flexible data classes (metadata-based row selection).
- Spiking neurons (LIF and IZ).
- Graded neurons (integrators, oscillators).
- Static and plastic synapses (STDP).
- Automated network construction and simulation.
- Scikit-integrated ML model API (fit/predict).
- Sampling and cross validation tools.
- Visualization tools.
To simulate a network from a YAML configuration using the default simulation pipeline:
python simple.py -config /path/to/config_file.yaml
See tutorials and tests/engine/network/test_network for instructive scripts and YAML files.
Installation
Basic requirements include Python 3.11+, PyTorch 2.1+, NetworkX, and the scientific stack (numpy, scipy, pandas).
To install the most recent development version:
pip install https://github.com/cplab/sapicore/archive/refs/heads/main.zip
To run tests, change directory to the one containing pytest.ini (sapicore) and call:
pytest -v -s
To run tests with a coverage report, run sapicore/tests/scripts/run_tests.py.
The coverage report will be generated in a separate directory on the same level (open index.html).
Development
If you would like to modify or extend this library:
Clone the repository:
git clone https://github.com/cplab/sapicore.git
Create a conda virtual environment (optional):
conda create -n
python=3.11 conda activate Change directory to
sapicoreand install with pip:cd sapicore pip install -e .
This project uses black to format code and flake8 for linting. We support pre-commit.
To configure your local environment, install these development dependencies and set up the commit hooks:
pip install black flake8 pre-commit
pre-commit install
Documentation can be compiled by installing Sphinx and RTD, then running docs/refresh.sh.
See setup.py for more information.
Citation
If you use Sapicore, please cite it as:
- Moyal, R., Einhorn, M., Borthakur, A., & Cleland, T. (2024). Sapicore (Version 0.4.0) [Computer software]. https://github.com/cplab/sapicore
References
For more information about past and ongoing projects utilizing Sapicore, refer to the following publications:
R. Moyal, K. R. Mama, M. Einhorn, A. Borthakur, and T. A. Cleland (2024). Heterogeneous quantization regularizes spiking neural network activity. arXiv:2409.18396.
A. Borthakur (2022). Sapinet: A sparse event-based spatiotemporal oscillator for learning in the wild. arXiv:2204.06216.
For a dynamical systems perspective on neural computation, temporal coding, and top-down control of sensory processing, the following article may be of interest:
- R. Moyal and S. Edelman (2019). Dynamic computation in visual thalamocortical networks. Entropy, 21(5).
Contributors
Owner
- Name: cplab
- Login: cplab
- Kind: organization
- Location: Cornell University
- Website: http://cplab.net
- Repositories: 15
- Profile: https://github.com/cplab
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Moyal" given-names: "Roy" orcid: "https://orcid.org/0000-0001-8224-8410" - family-names: "Einhorn" given-names: "Matthew" - family-names: "Borthakur" given-names: "Ayon" orcid: "https://orcid.org/0000-0003-1558-9869" - family-names: "Cleland" given-names: "Thomas" orcid: "https://orcid.org/0000-0001-7506-1201" title: "Sapicore" version: 0.4.0 date-released: 2024-12-08 url: "https://github.com/cplab/sapicore"
GitHub Events
Total
- Create event: 2
- Release event: 4
- Issues event: 4
- Delete event: 3
- Issue comment event: 1
- Push event: 14
- Pull request event: 4
Last Year
- Create event: 2
- Release event: 4
- Issues event: 4
- Delete event: 3
- Issue comment event: 1
- Push event: 14
- Pull request event: 4
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 28
- Total pull requests: 8
- Average time to close issues: 10 months
- Average time to close pull requests: 6 months
- Total issue authors: 3
- Total pull request authors: 3
- Average comments per issue: 0.11
- Average comments per pull request: 0.88
- Merged pull requests: 5
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 3
- Average time to close issues: N/A
- Average time to close pull requests: 27 days
- Issue authors: 1
- Pull request authors: 1
- Average comments per issue: 0.0
- Average comments per pull request: 0.0
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- matham (18)
- rm875 (5)
- jeremyforest (3)
Pull Request Authors
- rm875 (8)
- jeremyforest (4)
- matham (1)
Top Labels
Issue Labels
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Packages
- Total packages: 1
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Total downloads:
- pypi 30 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 4
- Total maintainers: 2
pypi.org: sapicore
A framework for spiking neural network modeling.
- Homepage: https://github.com/cplab/sapicore
- Documentation: https://sapicore.readthedocs.io/
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Latest release: 0.4.1
published 9 months ago
Rankings
Dependencies
- nixio *
- numpy *
- pandas *
- ruamel.yaml *
- tqdm *
- tree-config *