Science Score: 77.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 6 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org, scholar.google, zenodo.org -
✓Committers with academic emails
2 of 25 committers (8.0%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (13.0%) to scientific vocabulary
Keywords
Repository
Deep learning with spiking neural networks (SNNs) in PyTorch.
Basic Info
- Host: GitHub
- Owner: norse
- License: lgpl-3.0
- Language: Python
- Default Branch: main
- Homepage: https://norse.github.io/norse/
- Size: 110 MB
Statistics
- Stars: 750
- Watchers: 16
- Forks: 92
- Open Issues: 58
- Releases: 12
Topics
Metadata Files
README.md
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/
1. Getting started
The fastest way to try Norse is via the jupyter notebooks on Google collab.
2. Using Norse
Norse presents plug-and-play components for deep learning with spiking neural networks. Here, we describe how to install Norse and start to apply it in your own work. Read more in our documentation.
2.1. Installation
We assume you are using Python version 3.8+ and have installed PyTorch version 1.9 or higher. Read more about the prerequisites in our documentation.
| 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 norse
| Anaconda or Miniconda |
For troubleshooting, please refer to our installation guide, create an issue on GitHub or write us on Discord.
2.2. 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
bash python -m norse.task.mnist - To train a CIFAR classification network, invoke
bash python -m norse.task.cifar10 - To train the cartpole balancing task with Policy gradient, invoke
bash python -m norse.task.cartpole
Norse is compatible with PyTorch Lightning, as demonstrated in the PyTorch Lightning MNIST task variant (requires PyTorch lightning):
bash
python -m norse.task.mnist_pl --gpus=4
2.3. Example: Spiking convolutional classifier
This classifier is taken from our tutorial on training a spiking MNIST classifier and achieves >99% accuracy.
```python import torch, torch.nn as nn from norse.torch import LICell # Leaky integrator from norse.torch import LIFCell # Leaky integrate-and-fire from norse.torch import SequentialState # Stateful sequential layers
model = SequentialState( nn.Conv2d(1, 20, 5, 1), # Convolve from 1 -> 20 channels LIFCell(), # Spiking activation layer nn.MaxPool2d(2, 2), nn.Conv2d(20, 50, 5, 1), # Convolve from 20 -> 50 channels LIFCell(), nn.MaxPool2d(2, 2), nn.Flatten(), # Flatten to 800 units nn.Linear(800, 10), LICell(), # Non-spiking integrator layer )
data = torch.randn(8, 1, 28, 28) # 8 batches, 1 channel, 28x28 pixels output, state = model(data) # Provides a tuple (tensor (8, 10), neuron state) ```
2.4. Example: Long short-term spiking neural networks
The long short-term spiking neural networks from the paper by G. Bellec, D. Salaj, A. Subramoney, R. Legenstein, and W. Maass (2018) is another interesting way to apply norse: ```python import torch from norse.torch import LSNNRecurrent
Recurrent LSNN network with 2 input neurons and 10 output neurons
layer = LSNNRecurrent(2, 10)
Generate data: 20 timesteps with 8 datapoints per batch for 2 neurons
data = torch.zeros(20, 8, 2)
Tuple of (output spikes of shape (20, 8, 2), layer state)
output, new_state = layer(data) ```
3. Why Norse?
Norse was created for two reasons: to 1) apply findings from decades of research in practical settings and to 2) accelerate our own research within bio-inspired learning.
We are passionate about Norse: we strive to follow best practices and promise to maintain this library for the simple reason that we depend on it ourselves. We have implemented a number of neuron models, synapse dynamics, encoding and decoding algorithms, dataset integrations, tasks, and examples. Combined with the PyTorch infrastructure and our high coding standards, we have found Norse to be an excellent tool for modelling scaleable experiments and Norse is actively being used in research.
Finally, we are working to keep Norse as performant as possible. Preliminary benchmarks suggest that Norse achieves excellent performance on small networks of up to ~5000 neurons per layer. Aided by the preexisting investment in scalable training and inference with PyTorch, Norse scales from a single laptop to several nodes on an HPC cluster with little effort. As illustrated by our PyTorch Lightning example task.
Read more about Norse in our documentation.
4. Similar work
We refer to the Neuromorphic Software Guide for a comprehensive list of software for neuromorphic computing.
5. 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.
6. 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 Foundation) under Germany's Excellence Strategy EXC 2181/1 - 390900948 (the Heidelberg STRUCTURES Excellence Cluster).
7. Citation
If you use Norse in your work, please cite it as follows:
BibTex
@software{norse2021,
author = {Pehle, Christian and
Pedersen, Jens Egholm},
title = {{Norse - A deep learning library for spiking
neural networks}},
month = jan,
year = 2021,
note = {Documentation: https://norse.ai/docs/},
publisher = {Zenodo},
version = {0.0.7},
doi = {10.5281/zenodo.4422025},
url = {https://doi.org/10.5281/zenodo.4422025}
}
Norse is actively applied and cited in the literature. We refer to Google Scholar or Semantic Scholar for a list of citations.
8. License
LGPLv3. See LICENSE for license details.
Owner
- Name: Norse
- Login: norse
- Kind: organization
- Website: norse.github.io/norse
- Repositories: 3
- Profile: https://github.com/norse
Norse is a library to do deep learning with spiking neural networks.
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Pehle" given-names: "Christian" - family-names: "Pedersen" given-names: "Jens Egholm" title: "Norse - A deep learning library for spiking neural networks" version: 0.0.7 date-released: 2021-10-06 url: "https://github.com/norse/norse"
GitHub Events
Total
- Issues event: 9
- Watch event: 89
- Delete event: 3
- Issue comment event: 34
- Member event: 2
- Push event: 35
- Pull request review event: 3
- Pull request event: 17
- Fork event: 14
- Create event: 5
Last Year
- Issues event: 9
- Watch event: 89
- Delete event: 3
- Issue comment event: 34
- Member event: 2
- Push event: 35
- Pull request review event: 3
- Pull request event: 17
- Fork event: 14
- Create event: 5
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Jens Egholm | j****m@p****m | 205 |
| Christian Pehle | c****e@g****m | 148 |
| Eric Müller | m****r@k****e | 8 |
| dimitriskor | 5****r | 6 |
| Pugavkomm | s****2@g****m | 4 |
| Huizerd | 1****d | 4 |
| lucablessing | 4****g | 2 |
| Jesse | j****s@t****o | 1 |
| 4iar | 4****r | 1 |
| Adrien Delpierre | a****e@h****r | 1 |
| Alessio Colucci | a****i@p****m | 1 |
| AlmaLuna94 | 7****4 | 1 |
| Ben Kroehs | 1****s | 1 |
| BorjaEst | 4****t | 1 |
| ChauhanT | r****h@t****m | 1 |
| Erik Terres | 3****r | 1 |
| Hammouda ELBEZ | h****e@g****m | 1 |
| Harini Sudha | 5****l | 1 |
| JoseGomesJPG | 5****G | 1 |
| KhalifeHilal | 1****l | 1 |
| Sebastian Schmitt | s****t@p****e | 1 |
| Thomas B Homburg | t****s@h****k | 1 |
| Tobias Fischer | i****o@t****o | 1 |
| emijan-kth | 5****h | 1 |
| omahs | 7****s | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 70
- Total pull requests: 102
- Average time to close issues: 5 months
- Average time to close pull requests: 3 months
- Total issue authors: 26
- Total pull request authors: 21
- Average comments per issue: 2.93
- Average comments per pull request: 2.04
- Merged pull requests: 80
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 9
- Pull requests: 21
- Average time to close issues: about 1 month
- Average time to close pull requests: 23 days
- Issue authors: 3
- Pull request authors: 6
- Average comments per issue: 0.44
- Average comments per pull request: 1.57
- Merged pull requests: 15
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- Jegp (23)
- cpehle (19)
- AaronSpieler (3)
- Pugavkomm (2)
- benkroehs (2)
- laurentperrinet (1)
- mhr (1)
- guoweiyu (1)
- yuxuan-z19 (1)
- gcervantes8 (1)
- yjzhang97 (1)
- Eiad21 (1)
- StrasserFlorian (1)
- manuelbre (1)
- biphasic (1)
Pull Request Authors
- Jegp (57)
- cpehle (16)
- dimitriskor (11)
- muffgaga (8)
- fabio-innatera (6)
- theLamentingGirl (3)
- omahs (2)
- homburg (2)
- PMMon (2)
- benkroehs (2)
- Pugavkomm (2)
- lucablessing (2)
- Tobias-Fischer (2)
- KhalifeHilal (2)
- JoseGomesJPG (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 3
-
Total downloads:
- pypi 677 last-month
-
Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 4
(may contain duplicates) - Total versions: 21
- Total maintainers: 2
proxy.golang.org: github.com/norse/norse
- Documentation: https://pkg.go.dev/github.com/norse/norse#section-documentation
- License: lgpl-3.0
-
Latest release: v1.1.0
published almost 2 years ago
Rankings
pypi.org: norse
A library for deep learning with spiking neural networks
- Homepage: https://github.com/norse/norse
- Documentation: https://norse.github.io/norse
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-
Latest release: 1.1.0
published almost 2 years ago
Rankings
Maintainers (2)
conda-forge.org: norse
- Homepage: http://github.com/norse/norse
- License: LGPL-3.0-only
-
Latest release: 0.0.7.post1
published almost 4 years ago
Rankings
Dependencies
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- python 3.9-slim build
- jupyter-book *
- matplotlib *
- numpy *
- tensorboard *
- numpy *
- torch >=1.9.0
- torchvision >=0.10.0