Norse v0.0.7

A deep learning library for spiking neural networks.

Norse aims to exploit the advantages of bio-inspired neural components, which are sparse and event-driven - a fundamental difference from artificial neural networks. Norse expands PyTorch with primitives for bio-inspired neural components, bringing you two advantages: a modern and proven infrastructure based on PyTorch and deep learning-compatible spiking neural network components.

Documentation: norse.github.io/norse/

Getting started

To try Norse, the best option is to run one of the jupyter notebooks on Google collab.

Alternatively, you can install Norse and run one of the included tasks such as MNIST:

python -m norse.task.mnist

The {ref}page-started and {ref}page-working pages show how to build your own models with Norse while explaining a few fundamental concepts around spiking neural networks.

Installing Norse

Note that we assume you are using Python version 3.7+, are in a terminal friendly environment, and have installed the necessary requirements, depending on your installation method. More detailed installation instructions are available here: {ref}page-installing.

Method	Instructions	Prerequisites
From PyPi	pip install norse	Pip
From source	pip install -qU git+https://github.com/norse/norse	Pip, PyTorch
With Docker	docker pull quay.io/norse/norse	Docker
From Conda	conda install -c norse norse	Anaconda or Miniconda

Running examples

Norse is bundled with a number of example tasks, serving as short, self contained, correct examples (SSCCE). They can be run by invoking the norse module from the base directory. More information and tasks are available in our documentation and in your console by typing: python -m norse.task.<task> --help, where <task> is one of the task names.

To train an MNIST classification network, invoke
```
  python -m norse.task.mnist
```
To train a CIFAR classification network, invoke
```
  python -m norse.task.cifar10
```
To train the cartpole balancing task with Policy gradient, invoke
```
  python -m norse.task.cartpole
```

Norse is compatible with PyTorch Lightning, as demonstrated in the PyTorch Lightning MNIST task variant (requires PyTorch lightning):

python -m norse.task.mnist_pl --gpus=4

Read more in our {ref}page-spiking and visit our Jupyter Notebook examples.

Advanced uses and opimizations

Norse is meant to be used as a library for spiking neural networks in customized deep learning models. This typically means porting other models to the spiking/temporal domain, extending existing models, or starting completely from scratch. All three use cases are motivated and briefly described in {ref}page-working.

Unfortunately, spiking neural networks are resource intensive. The page on {ref}page-hardware explains how to accelerate the simulations using dedicated hardware.

Contributing

Contributions are warmly encouraged and always welcome. However, we also have high expectations around the code base so if you wish to contribute, please refer to our contribution guidelines.

Credits

Norse is created by

Christian Pehle (@GitHub cpehle), PostDoc at University of Heidelberg, Germany.
Jens E. Pedersen (@GitHub jegp), doctoral student at KTH Royal Institute of Technology, Sweden.

More information about Norse can be found in our documentation. The research has received funding from the EC Horizon 2020 Framework Programme under Grant Agreements 785907 and 945539 (HBP) and by the Deutsche Forschungsgemeinschaft (DFG, German Research Fundation) under Germany’s Excellence Strategy EXC 2181/1 - 390900948 (the Heidelberg STRUCTURES Excellence Cluster).

Citation

If you use Norse in your work, please cite it as follows:

@software{norse2021,
  author       = {Pehle, Christian and
                  Pedersen, Jens Egholm},
  title        = ,
  month        = jan,
  year         = 2021,
  note         = {Documentation: https://norse.ai/docs/},
  publisher    = {Zenodo},
  version      = {0.0.6},
  doi          = {10.5281/zenodo.4422025},
  url          = {https://doi.org/10.5281/zenodo.4422025}
}

Norse is actively applied and cited in the literature. We are keeping track of the papers cited by Norse in our documentation.

License

LGPLv3. See LICENSE for license details.

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