bindsnet
spikingjelly
| bindsnet | spikingjelly | |
|---|---|---|
| 1 | 1 | |
| 1,433 | 1,153 | |
| 1.7% | - | |
| 8.6 | 8.7 | |
| 3 days ago | 15 days ago | |
| Python | Python | |
| GNU Affero General Public License v3.0 | GNU General Public License v3.0 or later |
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bindsnet
spikingjelly
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Has anyone used Spiking Neural Networks (SNNs) for image processing?
Surrogate gradient learning w/ backpropagation: for short, you can use backpropagation with SNNs (by a little trick during the backward pass). Super easy to implement, super efficient. You have a deep SNN trained via backprop with any type of input you want. Personally, that is completely my jam. Maybe you can use such paradigm to easily train an SNN in your biomed image dataset. Good repos: SnnTorch comes with the best tutorials to explain SNNs and surrogate gradient learning. This is the fastest way to understand the field and begin to implement you solution. Nevertheless, spikingjelly remains a better option when it comes to implement your ideas (better memory efficiency, etc). Good mention to lava-dl, with which you can train a neural network and directly transfer it into neuromorphic hardware (Intel Loihi) if you have access to this kind of chip.
What are some alternatives?
snntorch - Deep and online learning with spiking neural networks in Python
norse - Deep learning for spiking neural networks
norse - Deep learning with spiking neural networks (SNNs) in PyTorch.
lava-dl - Deep Learning library for Lava
OpenWorm - Repository for the main Dockerfile with the OpenWorm software stack and project-wide issues
CUDA-Guide - CUDA Guide
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.
Sophysics2D
NeuroM - Neuronal Morphology Analysis Tool