norse
Neuromorphic-Computing-Guide
Our great sponsors
norse | Neuromorphic-Computing-Guide | |
---|---|---|
6 | 10 | |
611 | 247 | |
3.9% | - | |
6.5 | 5.0 | |
29 days ago | 4 months ago | |
Python | Python | |
GNU Lesser General Public License v3.0 only | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
norse
-
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?
-
[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
-
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.
Neuromorphic-Computing-Guide
-
I am extremely interested second language acquisition and Artificial intelligence. How can I get into research?
Start reading papers on https://www.biorxiv.org/ and notice what seems most interesting or promising to you. Learn python. There are actually quite a few open source "into to machine learning" courses - maybe start with MIT's Learning Library, see what you find there. I also have this bookmarked for myself for later; I'm sure there are a few more goodies worth checking out here: https://github.com/mikeroyal/Neuromorphic-Computing-Guide
-
Getting Started with Neuromorphic Computing
Tools and Resources for getting started with Neumorphic Computing. The process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.
-
Neuromorphic Engineering
Neuromorphic engineering, which combines electrical, computer, and mechanical engineering with biology, physics, and neuroscience. uses specialized computing architectures that reflect the structure (morphology) of neural networks from the bottom up: dedicated processing units emulate the behavior of neurons directly in hardware, and a web of physical interconnections (bus-systems) facilitate the rapid exchange of information. Useful Tools and Resources for learning about Neuromorphic engineering.
- GitHub - mikeroyal/Neuromorphic-Computing-Guide: Neuromorphic Computing Guide
- Neuromorphic Computing that enables fast and power-efficient neural network–based artificial intelligence
-
Neuromorphic Computing
Neuromorphic computing models the way the brain works through spiking neural networks and other types of neural networks. Useful Tools and Resources for learning about Neuromorphic Computing.
-
Tools and Resources for Neuromorphic Computing
Useful Tools and Resources for learning about Neuromorphic Computing. Neuromorphic computing models the way the brain works through spiking neural networks and other types of neural networks.
-
Tools and Resource for Neuromorphic Computing
UsefuleTools and Resource for about Neuromorphic Computing.
-
Cool Neuromorphic Computing Guide/Wiki
Neuromorphic Computing Guide/Wiki: https://github.com/mikeroyal/Neuromorphic-Computing-Guide
What are some alternatives?
snntorch - Deep and online learning with spiking neural networks in Python
lava - A Software Framework for Neuromorphic Computing
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.
spaCy - đź’« Industrial-strength Natural Language Processing (NLP) in Python
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
NIPY - Workflows and interfaces for neuroimaging packages
bindsnet - Simulation of spiking neural networks (SNNs) using PyTorch.
Shallow-learning - Replicating brain's low energy high efficiency model architecture & calculating (maths)
ocaml-torch - OCaml bindings for PyTorch
Nerve - This is a basic implementation of a neural network for use in C and C++ programs. It is intended for use in applications that just happen to need a simple neural network and do not want to use needlessly complex neural network libraries.