
A Mathematical Model Unlocks the Secrets of Vision - theafh
https://www.quantamagazine.org/a-mathematical-model-unlocks-the-secrets-of-vision-20190821/
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apl
This represents the absolute worst kind of science journalism, completely
devoid of context and domain knowledge. Virtually every definitive statement
in here is wrong. Their explanation of spiking alone is a complete disaster.

Out of all the modalities, vision is easily the one we know the most about.
And we do so at a fairly deep level. The discussed work seems fine but it's
not the groundbreaking insight it's made out to be. Great PR work from the
involved scientists (or their enterprising university marketing department).

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asimpletune
I don’t know a lot about this subject. Can you elaborate?

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briga
Cognitive scientists have been studying the computational foundations of
vision in depth since at least the 1980s (see David Marr's 1982 book Vision),
and AI scientists have been using neural networks for computer vision tasks
for at least as long. So yeah, I'm no expert, but this probably isn't the
ground-breaking work the article makes it out to be.

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kkylin
This work is about the very early steps of the primate visual pathway --
retina to LGC to the input layer of V1. Marr's opus is meant to be a much more
wholistic view of the visual system. A more appropriate context for this
article is perhaps David Hubel's (very accessible) _Eye, Brain, Vision_.

(The book used to be freely available on a website hosted by Harvard Med, but
I can't seem to find it anymore.)

FWIW, I work a little bit in computational neuroscience. While I think "ground
breaking" is an exaggeration, and I wish the article spent more time
explaining the general thinking in the field and why it matters, the content
is not terribly written for an article of this type and length. And it should
be emphasized that the point of this modeling is really understanding the
biology of the primate visual system; what it says about the general problem
of vision is a separate question.

Disclaimer: I was not involved in this work, but did collaborate with one of
the scientists extensively in the past.

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kkylin
Can't edit anymore, so two very minor corrections: "LGC" should have been
"LGN" (had "RGC" and "LGN" both on my mind), and "disclaimer" should really
have been "disclosure."

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theobon
"While their model is far from uncovering the full mystery of vision, it is a
step in the right direction"

This penultimate paragraph directly contradicts the headline. I know that
writers don't have much control over their headlines but this is endlessly
frustrating as a reader.

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boltzmannbrain
> the first model to try and decipher vision in a biologically plausible way.

> Their work is the first of its kind.

No and no.

Shapley has great work in this area -- other biologically-plausible models of
visual cortex indeed cite his work -- but this article is making grandiose
claims with little knowledge of a deep field of work.

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program_whiz
a puff piece, seems to have little content or insights that aren't already
known. Didn't see any "breakthrough" to justify "unlocks the secrets of
vision". Sounds like what they're talking about is fairly well-tread ground.
My guess is this was a collusion between Uni and Quanta so both can get
attention / headlines.

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ppod
Agree, hard to see how this can get 55 points in an hour.

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sitkack
Interesting, it sounds like the brain is creating a spatio temporal model and
that these firings are creating a driving signal, but it has to be phased
locked to the model or it falls apart, if enough of these driving signals
align, a new model is formed. Is that a correct interpretation? It would also
make sense that we create a future prediction model to overcome the horrible
lag and the high latency in neural processing. The whole system runs at the
10s of Hz.

A lot could be going on here from an information theoretic standpoint,
compressed sensing, error correction, time series prediction all operating in
concert to reconstruct a model of the world inside our minds.

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slacka
> These “feed forward” models were easier to create, but they ignored the
> plain implications of the anatomy of the cortex — which suggested “feedback”
> loops had to be a big part of the story.

Almost 15 years ago, Jeff Hawkins proposed a similar explanation as to why
biological neural networks are so much more effective than artificial neural
networks. If this paper bears out, it will mean he was right track. His theory
was based on 3 criteria that he believed to be essential to understanding the
brain:

> The second criterion was the importance of feedback. Neuroanatomists have
> known for a long time that the brain is saturated with feedback connections.
> For example, in the circuit between the neocortex and a lower structure
> called the thalamus, connections going backward (toward the input) exceed
> the connections going forward by almost a factor of ten! That is, for every
> fiber feeding information forward into the neocortex, there are ten fibers
> feeding information back toward the senses. Feedback dominates most
> connections throughout the neocortex as well. No one understood the precise
> role of this feedback, but it was clear from published research that it
> existed everywhere. I figured it must be important.

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dekhn
“The job of the theorist, as I see it, is we take these facts and put them
together in a coherent picture,” Young said. “Experimentalists can’t tell you
what makes something work.”

This is not anything real scientists believe. In science, only experimental
data can tell you how something works. Math models are just math models (not
to imply they're not useful in understanding how things work, but they
certainly don't are not sufficient.

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voi_perkele
Here is a Ph.D. dissertation by Daniel Pineo using a computational model of
human vision to evaluate and optimize the perception of data visualizations:
[https://pdfs.semanticscholar.org/1739/ad378c5d77b8a09b8ad13e...](https://pdfs.semanticscholar.org/1739/ad378c5d77b8a09b8ad13eca2a7fb1d06d2b.pdf)

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ggggtez
I don't get it. Aren't they just describing a neural network... Is it even
correct to say this is new?

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proc0
Horrible title as some point out. I got a good tl;dr from the first paper
related to the article (same authors):

"Comprehensive population models, such as the one we have presented here, seek
to link cellular properties and network structure to dynamics and function,
the ultimate goal being to use these models to test hypotheses and to suggest
future experiments. We propose that for areas of the brain about which there
are sufficient data, such as the visual cortex, it is time to move to next-
generation models that are more comprehensive, data driven, and dynamic. Such
a move would constitute a paradigm shift in computational neuroscience, and
the present model is a step in that direction."

Basically they want more than just a simplified mathematical model, something
that accounts for all the measured data. I'm not sure this is the right
approach, and only some example application would show if it is. I don't think
they applied their model yet or whether it can be applied, just browsed the
first paper.

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not_a_cop75
"New research suggests mathematics is the key."

What could they have possibly thought the key was previous? And is the
journalist of preschool intellect, or simply an amazingly ignorant
baccalaureate?

