SuperNeuroMAT 3.1.0 documentation#

SuperNeuroMAT is a Python package for simulating and analyzing spiking neural networks.

Unlike its sister package, SuperNeuroABM, SuperNeuroMAT uses a matrix-based representation of the network, which allows for more efficient simulation and GPU acceleration.

SuperNeuroMAT focuses on super-fast computation of Leaky Integrate and Fire (LIF) spiking neuron models with STDP.

This documentation currently covers the kenblu24/superneuromat fork, but this will be upstreamed to the main SuperNeuroMAT repository in the future.

Warning

Both the documentation and the simulator software are under development. Please report any issues with the software or documentation to the GitHub issue tracker.

Get Started#

pip install git+https://github.com/kenblu24/superneuromat.git@main

For more detailed instructions, see the installation guide, which covers virtual environments, faster installation with uv, installing support for CUDA GPU acceleration, and more.

Then, you can import the superneuromat package:

Python#
from superneuromat import SNN

Cite SuperNeuroMAT#

Thank you for your interest in SuperNeuroMAT! If you use it in your research, please cite the following paper:

@inproceedings{date2023superneuro,
    title={SuperNeuro: A fast and scalable simulator for neuromorphic computing},
    author={Date, Prasanna and Gunaratne, Chathika and R. Kulkarni, Shruti and Patton, Robert and Coletti, Mark and Potok, Thomas},
    booktitle={Proceedings of the 2023 International Conference on Neuromorphic Systems},
    pages={1--4},
    year={2023}
}

References

See the literature [1] [2] [3] for SuperNeuroMAT.

Contents: