You are right that the learning process which ANNs use is pretty much nothing like the brain. But an argument can be made that the feature representations which they learn are quite brain like. For example in visual cortex, you will find feature detectors for things like edges and whatnot, with more abstract features as you climb to higher layers. You see very similar things in Convolutional nets.

Spiking neural network models are actually very brain like, but no one really knows how to use them effectively.

I think its also important to realize that we don't have simulate the brain to do what the brain does. If you want to build a flying machine, you could model a bird with flapping wings, but its much easier to just build a fixed wing aircraft. Back propagation may be a similar case. Evolution found a very good way to learn with neurons but it may not be optimal for the things we care about. We may be better off sticking to back propagation because it works so well. Also people are investigating backpropagation alternatives which start to look more and more brain like.

Basically my point is there seems to be intrinsic characteristics of neural computation, which ANNs and real brains both demonstrate.

quite brain like*

* for just two layers of the visual cortex, V1 and V2 (out of 6 layers). But nevermind for any other brain regions, and never-mind about CNNs applied to speech, robotics, NLP, finance... basically any non-vision domain.

You may be right that NN activations don't resemble neural receptive fields (though Tomasso Pogio and others disagree [1]), but I strongly suspect that to the extent that NNets are able to learn to represent statistical invariants, we will see clear homologs to biological neural networks.

The grandparent post made the point that there could intrinsic characteristics to neural computation shared by natural and artificial networks. There's something that feels right about that, but I'd take it a step further: both natural and artificial networks learn to explain the statistics of the natural world in a compact representation. How many possible equivalently compact representations are there? Wouldn't it seem like a monstrous coincidence if there were multiple representations possible yet both DNNs and the mammalian visual system both decided to represent edges first?

By the way, you should get your anatomy right if you wish to speak so authoritatively: V1 and V2 are not layers of cortex, but rather adjacent cortical subdivisions with cytoarchitecture that correspond to Brodmann areas 17 and 18.

1. https://mitpress.mit.edu/books/visual-cortex-and-deep-networ...

Notwithstanding the fact that this is just his theory, Tomasso Pogio mostly does not seem to disagree. Reading the summary of that link, it appears he asserts the ventral stream (one of two streams) is well explained by convolutions. So he does not assert the same about the dorsal stream, any other brain region, or any other non-visual domain of deep learning.

It bears noting that our visual cortex is one of the simpler areas of our brain, considering that equivalent functionality is in all sorts of animals.

It's not a coincidence that DNNs and mammalian visual systems both decide to represent edges first. That's exactly what Yann Lecun was trying to do when he designed convolutional neural nets! You've got the causality mixed up. Yann Lecun explicitly wanted to model the visual system so he came up with a system that would give him the results he wanted (edge detection in the first layer) - which meant linear filters.

Well, the ventral stream is the "what" stream, so that's the natural one to draw comparisons to for the task of representing objects. The "where" pathway for the most part represents space with a place code, that is, different neurons "care about" different parts of space in an increasingly abstract way going from representing eg "the left visual hemifield" early in the dorsal stream to representing "the left side of objects" in parietal cortex. It would be interesting to see if this kind of invariant emerges in NNs. Convolutional networks are able to caption images with labels like "a cake on top of an oven". What do their activations look like?

I reject the notion that visual cortex is "simple", but I will concede that it is highly conserved across species. This just means that the representations that it uses are effective.

You are no doubt right that Lecun was inspired by the biology of visual cortex (along with theorists before him), but you missed my meaning: can we build a network that doesn't start with edge detectors first that does better? My guess is no.

This is just speculation, however. (Speculation about captioning neural nets and the direction of evolution and edge detectors)

What's your favorite JS framework?

Oh stop already. The original paper for ANN's published by Pitts said they were directly inspired by biological models of neuronal activation functions [1] - hence the name. Yes it's potentially misleading to a non practitioner, but everyone gets so mad about this point unnecessarily at tech writers.

[1] https://books.google.com/books?id=gJhBBAAAQBAJ&pg=PA209&hl=e...

You're not technically wrong, but I think this is a bit pedantic. Most people realize this. Convolutional networks don't actually seem to be a totally bad approximation of feedforward visual processes in the brain at a computational level. And I say this as a neuroscientist.

Most people don't realize this. Most people will just read popular science articles in the media. They will never read discussions on HN/Reddit/Twitter.

which is precisely why analogies are helpful in communicating with them.

Which is precisely why false analogies are not helpful.

>> Convolutional networks don't actually seem to be a totally bad approximation of feedforward visual processes in the brain at a computational level. And I say this as a neuroscientist.

I'm saying that a "(not) totally bad approximation" can still be bad enough that it's completely useless. And even a good one can.

The problem is that you can model pretty much anything with an analogy that's broad enough. Say, you can model any distribution with a straight line... except you will often not learn anything you didn't know before. Or maybe you'll learn a lot about a whole class of distributions, but not about a specific distribution, or how it differs from all the others in its class.

If you've read Foucault's Pendulum- it basically makes this point about the Tree of Life [1]. They're always "fitting" it to everything from pinball machines, to cars, to peoples' sex organs. And it always fits so well! Maybe that's because it's truly divine?

Come to that- why are ANNs based "on the brain" and not the Tree of Life? They sure look a lot like it, superficially. Maybe ANNs are really based on the Mystic Qabbalah, Geoff Hinton is a Rosicrucian and it's all a scheme of the Illuminati. CNNs are probably a model of The Eye on the Pyramid. It all fits, innit! That way lies madness- or at least a whole big bunch of confusion and waste of time.

You mentioned computation- look at Turing machines for a good model of a thing. It's an analogy that's broad enough to represent a whole class of computational devices, yet at the same time it only represents those devices and nothing else. You can't mistake a Turing machine for a potato, or a cricket, fnord what have you. Why can't we have that sort of thing, instead of "Neural Networks"?

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[1] https://en.wikipedia.org/wiki/Tree_of_life#Kabbalah

Can you name something that's a better approximation to the brain's computational processes than a neural network?

You know, pong is a LOT like tennis. Certainly more than most other ball games. Perhaps your specialized knowledge makes you more critical than necessary? Neural networks certainly work analogously to brain structures, and certainly at the layman level readers of this article are likely to understand.

Though this is the prevailing view in Neuroscience, in truth there is very little information on the actual computation the brain in doing biologically.

The majority of Neuroscience literature in the CNS focuses on sensory systems signal processing and on the low levels of those pipelines( vision, sound, and taste). There has been recent work in olfaction, but there exist inherent problems with state space and encoding (also olfactory neurons seem to project all over- causing even more experimental problems) to consider (one of the reasons for the attractiveness of vision, sound and taste encoding).

All higher order processing really is a black box. This is one of the primary arguments of pursuing a connectomics based approach. Some cool work in doing targeted connectomics is being done at HHMI and the Allen Institute (both with fluorescent and em microscopy). Where the latter focuses more on fly because of the resolution

https://www.janelia.org/project-team/mouselight https://www.janelia.org/project-team/flylight https://www.janelia.org/project-team/flyem https://www.alleninstitute.org/

So in short, we don't know if NN are like the brain or not.

"Towards an integration of deep learning and neuroscience" - https://arxiv.org/abs/1606.03813

Dismissing factual kinship of basic functionality because of difference in details of implementation. Well done, you! Have a cookie.