Interpretability vs Neuroscience [rough note]
colah.github.io · 1,500 words · saved by 1 readers
A list of advantages that make understanding artificial nerural networks much easier than biological ones.
Interpretability vs Neuroscience [rough note] -- colah's blog Interpretability vs Neuroscience Six major advantages which make artificial neural networks much easier to study than biological ones. Posted on March 12th, 2021 This article is a rough note. Writing rough notes allows me share more content, since polishing takes lots of time. While I hope it's useful, it's likely lower quality and less carefully considered than my usual articles. It's very possible I wouldn't stand by this content if I thought about it more. I study what goes on inside artificial neural networks. I don’t know anyth
related reading
- Analogies between Biology and Deep Learning [rough note] -- colah's blogcolah.github.io
- Neural Networks, Manifolds, and Topology -- colah's blogcolah.github.io
- Home - colah's blogcolah.github.io
- Neural networks are not already cleanlesswrong.com
- A Recipe for Training Neural Networkskarpathy.github.io
- Neuroscience is pre-paradigmatic. Consciousness is whytheintrinsicperspective.com
- Could a Neuroscientist Understand a Microprocessor? | PLOS Computational Biologyjournals.plos.org
- Neural Networks, Types, and Functional Programming -- colah's blogcolah.github.io
- Neural networks and deep learningneuralnetworksanddeeplearning.com
- Mechanistic Interpretability: A Challenge Common to Both Artificial and Biological Intelligencekempnerinstitute.harvard.edu
- A Recipe for Training Neural Networkskarpathy.github.io
- Feature Visualizationdistill.pub