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Deep Learning has nothing to do with biophysical neuron simulation, even though there is a confusing overloading of the term "neural network". A good introduct
by ericjang 6y ago
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 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 https://github.com/ericjang/pyN
[2] https://github.com/ericjang/julia-NeuralNets https://github.com/ericjang/julia-NeuralNets
[3] https://github.com/ericjang/NeuralNets https://github.com/ericjang/NeuralNets
- kaba0 6y agoI have asked it in this thread already, but I am really interested in your answer to this as well: > Is there some truth to the “reverse” though —- that is, is the emerging patterns similar for similarish problems? What comes to mind is the similar first-layers in the human vision and google’s vision AI, with vertical/horizontal lines being “matched”. I don’t know much about NNs, but in the big picture isn’t it sort of similar? Instead of discrete on-off signals where multiple firings in a short succession result in higher activation, in NNs we sort of take the integral over time of said discrete signals. Of course things like neuron fatigue are not accounted for in this model, but does the base idea differ all that much? Or perhaps I misunderstood the whole topic and only the learning process’s difference is the question now?