pyN
Spiking-Neural-Network
| pyN | Spiking-Neural-Network | |
|---|---|---|
| 1 | 1 | |
| 6 | 978 | |
| - | - | |
| 0.0 | 0.0 | |
| over 11 years ago | almost 2 years ago | |
| Python | Python | |
| BSD 2-clause "Simplified" License | Apache License 2.0 |
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pyN
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Don't Mess with Backprop: Doubts about Biologically Plausible Deep Learning
Deep Learning has nothing to do with biophysical neuron simulation, even though there is a confusing overloading of the term "neural network". A good introduction to deep learning is this chapter: https://mlstory.org/deep.html.
STDP falls under biophysical models of neuron simulation, where we try to faithfully reproduce biophysics of brain simulation (trivia: I started my undergrad in computational neuroscience and implemented STDP several times [1, 2, 3]). STDP is a learning mechanism, but it has not demonstrated the ability to learn as powerful models as DNN.
[1] https://github.com/ericjang/pyN
[2] https://github.com/ericjang/julia-NeuralNets
[3] https://github.com/ericjang/NeuralNets
Spiking-Neural-Network
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Don't Mess with Backprop: Doubts about Biologically Plausible Deep Learning
I'm not very familiar with deep learning. How does this compare to the biomimicking Spike Time Dependent Plasticity of spiking neural networks?
https://github.com/Shikhargupta/Spiking-Neural-Network#train...
What are some alternatives?
norse - Deep learning with spiking neural networks (SNNs) in PyTorch.
lava - A Software Framework for Neuromorphic Computing
norse - Deep learning for spiking neural networks
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.
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.