bindsnet
norse
| bindsnet | norse | |
|---|---|---|
| 1 | 6 | |
| 1,437 | 618 | |
| 1.9% | 2.6% | |
| 8.6 | 6.4 | |
| 12 days ago | 26 days ago | |
| Python | Python | |
| GNU Affero General Public License v3.0 | GNU Lesser General Public License v3.0 only |
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bindsnet
norse
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Neuromorphic learning, working memory, and metaplasticity in nanowire networks
This gives you a ludicrous advantage over current neural net accelerators. Specifically 3-5 orders is magnitude in energy and time, as demonstrated in the BranScaleS system https://www.humanbrainproject.eu/en/science-development/focu...
Unfortunately, that doesn't solve the problem of learning. Just because you can build efficient neuromorphic systems doesn't mean that we know how to train them. Briefly put, the problem is that a physical system has physical constraints. You can't just read the global state in NWN and use gradient descent as we would in deep learning. Rather, we have to somehow use local signals to approximate local behaviour that's helpful on a global scale. That's why they use Hebbian learning in the paper (what fires together, wires together), but it's tricky to get right and I haven't personally seen examples that scale to systems/problems of "interesting" sizes. This is basically the frontier of the field: we need local, but generalizable, learning rules that are stable across time and compose freely into higher-order systems.
Regarding educational material, I'm afraid I haven't seen great entries for learning about SNNs in full generality. I co-author a simulator (https://github.com/norse/norse/) based on PyTorch with a few notebook tutorials (https://github.com/norse/notebooks) that may be helpful.
I'm actually working on some open resources/course material for neuromorphic computing. So if you have any wishes/ideas, please do reach out. Like, what would a newcomer be looking for specifically?
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[D] The Complete Guide to Spiking Neural Networks
Surrogate gradients and BPTT, this is what is implemented in Norse https://github.com/Norse/Norse. It is also possible to compute exact gradients using the Eventprop algorithm.
- [P] Norse - Deep learning with spiking neural networks (SNNs) in PyTorch
- Show HN: Deep learning with spiking neural networks (SNNs) in PyTorch
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Don't Mess with Backprop: Doubts about Biologically Plausible Deep Learning
That repo is slightly outdated, development now continues at https://github.com/norse/norse.
What are some alternatives?
snntorch - Deep and online learning with spiking neural networks in Python
norse - Deep learning for spiking neural networks
Spiking-Neural-Network - Pure python implementation of SNN
spikingjelly - SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
OpenWorm - Repository for the main Dockerfile with the OpenWorm software stack and project-wide issues
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
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.
ocaml-torch - OCaml bindings for PyTorch
Sophysics2D
Neuromorphic-Computing-Guide - Learn about the Neumorphic engineering process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.