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.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Spiking Neural Network Snn With Pytorch Where Backpropagation Engenders Stdp | 192 | 2 years ago | 7 | Jupyter Notebook | ||||||
| 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. | ||||||||||
| Twpca | 98 | 3 years ago | 2 | February 26, 2017 | 3 | mit | Jupyter Notebook | |||
| 🕝 Time-warped principal components analysis (twPCA) | ||||||||||
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