snntorch
bindsnet
| snntorch | bindsnet | |
|---|---|---|
| 2 | 1 | |
| 1,085 | 1,430 | |
| - | 1.5% | |
| 9.2 | 8.6 | |
| 10 days ago | 24 days ago | |
| Python | Python | |
| MIT License | GNU Affero General Public License v3.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
snntorch
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Bio inspired computer vision
Spiking Neural Networks (SNNs): neural networks that use spiking neurons (i.e. neurons that communicate using asynchronous binary spikes similarly to biological neurons) instead of artificial neurons. Apart from this particularity, SNNs can be organized in any kind of topology we all know, like CNNs, ViT, etc. There are tons of approaches to train SNNs, like bio-inspired learning rules (STDP, three factor rules, etc) or adaptations of backprop (which remains the SOTA in a lot of vision tasks). A good resource to begin with backprop-trained SNNs: https://snntorch.readthedocs.io/en/latest/ .
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How to train brain-inspired spiking neural networks using lessons from deep learning. Interactive Colab notebook links in thread.
Github: https://github.com/jeshraghian/snntorch
bindsnet
What are some alternatives?
spikingjelly - SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
norse - Deep learning for spiking neural networks
norse - Deep learning with spiking neural networks (SNNs) in PyTorch.
pytorch-forecasting - Time series forecasting with PyTorch
TorchGA - Train PyTorch Models using the Genetic Algorithm with PyGAD
OpenWorm - Repository for the main Dockerfile with the OpenWorm software stack and project-wide issues
Kilosort - Fast spike sorting with drift correction for up to a thousand channels
Spiking-Neural-Network-SNN-with-PyTorch-where-Backpropagation-engenders-STDP - What about coding a Spiking Neural Network using an automatic differentiation framework? In SNNs, there is a time axis and the neural network sees data throughout time, and activation functions are instead spikes that are raised past a certain pre-activation threshold. Pre-activation values constantly fades if neurons aren't excited enough.
pycox - Survival analysis with PyTorch
Sophysics2D