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Brain networks and neural networks are not similar in structure or function, other than the superficial abstraction of “nodes and connections” (graphs). They’r

by mayank 9y ago

Brain networks and neural networks are not similar in structure or function, other than the superficial abstraction of “nodes and connections” (graphs). They’re about as related as genetic algorithms are to actual genetics.

aje403 9y ago
Which is what he's saying, the use of the term is because of the extremely-vague-homeomorphism implied by the structure Unfortunately, the word choice can lead to "they've gots em a neural networks and an AI singularities and them robots coming to take over"
- Maybestring 9y ago
  >extremely-vague-homeomorphism Analogy is the concept in its full generality.
  - aje403 9y ago
    If only. Analogy is more like the bastardization of an Ideal Form
    - Maybestring 9y ago
      Thats a very foreign view of the concept for me. I think of analogy as a non-rigorous functor. A mapping between concepts and relationships among concepts in one context to concepts and relationships among concepts in another context.
lev99 9y ago
Genetic Algorithms and Neural Networks are clearly inspired by the scientific fields of Genetics and Neuroscience.
- kxyvr 9y ago
  I'll disagree with this. Cybenko's approximation for a multilayer perceptron is simply a concrete expression for Kolmogorov's representation of a continuous function, which was created in order to solve Hilbert's Thirteenth problem. This has absolutely nothing to do with the brain. Yes, Cybenko uses the word "neural network", but just look at expression (1) in his paper "Approximation by Superpositions of a Sigmoidal Function" and compare that to expression (1) in Kolmogorov's paper "On the Representation of Continuous Functions of Many Variables by Superposition of Continuous Functions of One Variable and Addition". The brain diagram used by most neural network books is really just a graph diagram of a matrix-vector multiplication used in the superposition. While I can't claim to know everyone's inspiration for their research, there really is a mundane explanation and history of these algorithms that has nothing to do with the brain.
  - bertil 9y ago
    As a data scientist dating a neuro-scientist, I can confirm that the two fields are quite apart in practice (except from the occasional image processing of fMRIs) — however, Cybenko’s work started in the late 70s; the inspiration for using a formalism of unit perceptrons organised in layers (as well as the convolution structure) comes from a paper on cat’s vision system published in 1962 [1]. As far as I’m aware, all key papers (Hinton, Le Cunn) cite Hubel & Wiesel as their explicit inspiration. The current practice has moved far from neuro-science, but I don’t think it’s pedantic when discussing machine learning to talk about “artificial neural networks” and acknowledge that neuroscience is still working on understanding the structure of brains. Some people, focused on their research, didn’t have that discretion. I wouldn’t imitate them. [1] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359523/ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359523/

by mayank 9y ago

aje403 9y ago

Which is what he's saying, the use of the term is because of the extremely-vague-homeomorphism implied by the structure Unfortunately, the word choice can lead to "they've gots em a neural networks and an AI singularities and them robots coming to take over"

Maybestring 9y ago
>extremely-vague-homeomorphism Analogy is the concept in its full generality.
- aje403 9y ago
  If only. Analogy is more like the bastardization of an Ideal Form
  - Maybestring 9y ago
    Thats a very foreign view of the concept for me. I think of analogy as a non-rigorous functor. A mapping between concepts and relationships among concepts in one context to concepts and relationships among concepts in another context.

lev99 9y ago

Genetic Algorithms and Neural Networks are clearly inspired by the scientific fields of Genetics and Neuroscience.

kxyvr 9y ago
I'll disagree with this. Cybenko's approximation for a multilayer perceptron is simply a concrete expression for Kolmogorov's representation of a continuous function, which was created in order to solve Hilbert's Thirteenth problem. This has absolutely nothing to do with the brain. Yes, Cybenko uses the word "neural network", but just look at expression (1) in his paper "Approximation by Superpositions of a Sigmoidal Function" and compare that to expression (1) in Kolmogorov's paper "On the Representation of Continuous Functions of Many Variables by Superposition of Continuous Functions of One Variable and Addition". The brain diagram used by most neural network books is really just a graph diagram of a matrix-vector multiplication used in the superposition. While I can't claim to know everyone's inspiration for their research, there really is a mundane explanation and history of these algorithms that has nothing to do with the brain.
- bertil 9y ago
  As a data scientist dating a neuro-scientist, I can confirm that the two fields are quite apart in practice (except from the occasional image processing of fMRIs) — however, Cybenko’s work started in the late 70s; the inspiration for using a formalism of unit perceptrons organised in layers (as well as the convolution structure) comes from a paper on cat’s vision system published in 1962 [1]. As far as I’m aware, all key papers (Hinton, Le Cunn) cite Hubel & Wiesel as their explicit inspiration. The current practice has moved far from neuro-science, but I don’t think it’s pedantic when discussing machine learning to talk about “artificial neural networks” and acknowledge that neuroscience is still working on understanding the structure of brains. Some people, focused on their research, didn’t have that discretion. I wouldn’t imitate them. [1] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359523/ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359523/