Solving Minimum Spanning Tree Problem in Spiking Neural Networks: Improved Results

This repository contains the official implementation for Solving Minimum Spanning Tree Problem in Spiking Neural Networks: Improved Results

Reference

If you find our paper or this repository helpful, please consider citing:

@inproceedings{janssenSolvingMinimumSpanning2024,
  author={Janssen, Simon and Groenen, Stijn and Reichert, Simon and Kwisthout, Johan},
  booktitle={2024 International Conference on Neuromorphic Systems (ICONS)},
  title={Solving Minimum Spanning Tree Problem in Spiking Neural Networks: Improved Results},
  year={2024},
  volume={},
  number={},
  pages={47-54},
  keywords={Power demand;Neuromorphics;Neuromorphic engineering;Neurons;Graphics processing units;Spiking neural networks;Parallel processing;Hardware;Complexity theory;Time complexity;Neuromorphic Computing;Graph Algorithms},
  doi={10.1109/ICONS62911.2024.00015}
}

Requirements

To run this project, you need to have Python 3.x installed. You can install the required dependencies using pip:

pip install -r requirements.txt

Usage

The spiking neural network algorithm can be run using the following code, where weight_matrix is a numpy array containing the weight matrix of the input graph:

from solver import MSTSolver

MSTSolver(weight_matrix).solve()

Testing

The test.py script runs several test cases for the SNN-based minimum spanning tree solver algorithm. It can be run with the following command:

python test.py

License

This project is licensed under the MIT License. See the LICENSE file for more details.