
Should we strive to simplify neural models? - nefitty
http://www.nature.com/nature/journal/v531/n7592_supp/full/531S16a.html
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mc808
Having a physically realistic model could at least serve as a point of
reference to see how various abstractions and simplifications differ from that
baseline.

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officialchicken
There are no revelations and everything worked out as expected, so this
experiment is a failure? How? And now you need more money for funding studies,
because reasons such as it's too hard?

I'd hope these scientists would delight in nuance, but it seems as the pursuit
for a major breakthrough has ruined their own brains. Build the right thing,
build the thing right. And please stop bull-shitting. If nothing happened,
that's fine. If you need ten more iterations of Moore's law, then say so.

But don't fake-it-until-you-make-it... pretty sure that only leads to bad
science.

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bjornsing
> For example, a single cortical neuron receives input from thousands of other
> cells, but it is unclear how it processes this information.

Is this true? If so it sounds like one of the most pressing research questions
in AI, or perhaps even all of science...

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shmageggy
Yes, it's true, for certain definitions of "unclear" and "processes". The
whole issue here is that the mind/brain is too complex to describe at any
single level of abstraction. There are just so many different, valid levels of
description, all the way from physics to high level computational approaches.
Wikipedia lists 22 distinct branches of neuroscience [1], and each has their
own set of problems they are trying to answer.

The problem this article brings up is that one researcher (who has received a
lot of money to fund his project) has come up with an explanation of "process"
that is "clear" to him for the questions he is interested in. However, for
everyone else, "process" means something different, and they are unable to see
how to translate his model to answers questions they are interested in.
Without a simplified model, one that abstracts away the huge complexity,
there's no ability to generalize its conclusions beyond the exact simulation
at hand, and this means it has virtually no explanatory power.

So yes, because there are many valid definitions of "process", that statement
is true for many of the interesting definitions.

[1]
[https://en.wikipedia.org/wiki/Neuroscience#Major_branches](https://en.wikipedia.org/wiki/Neuroscience#Major_branches)

~~~
bjornsing
> the mind/brain is too complex to describe at any single level of abstraction

Sure, but now we're talking about a _single neuron_...

> Wikipedia lists 22 distinct branches of neuroscience [1], and each has their
> own set of problems they are trying to answer.

Sure, but only one or two of those 22 are about _single neurons_...

In artificial neural networks neurons are approximated basically as a weighted
summation and a thresholding/sigmoid/rectified linear operation. It would be
interesting to know how far from the truth this is. Is the biological neuron
doing something entirely different than what our artificial neurons are?
That's what I feel would be a pressing research question.

~~~
shmageggy
> Sure, but only one or two of those 22 are about single neurons...

I count four or five, but I agree that may not be the best example for my
point, which is that (even within those few subfields) there are many valid
levels of description.

> Is the biological neuron doing something entirely different than what our
> artificial neurons are?

Yes, very; they are highly non-linear and have complex timing dependencies.
This illustrates the point. Once you model all those intricate biological
details it's unclear how to translate that into a mathematical function that
we can understand for the purposes of higher level computation. Not to
mention, we're still talking about spiking models, so we'd also have to
abstract away spiking in order to look like an artificial neuron. The gulf
between the level of description that you and I want and what biologically
realistic models provide is huge.

