
Systems Neuroscience Is About to Get Bonkers - hardmaru
https://www.simonsfoundation.org/2018/08/03/systems-neuroscience-is-about-to-get-bonkers/
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ssivark
> _One successful deep-learning approach involves modeling behavioral tasks.
> Researchers build an ANN and optimize it to solve a task analogous to the
> one studied in animals. They then compare the internals of the trained ANN
> with the biological neural recordings, typically smoothed spike trains of a
> population of neurons. If there are quantitative similarities, researchers
> can then attempt to reverse-engineer the ANN in order to develop a
> mechanistic explanation for how the ANN solves the task. The insights found
> in the ANN can lead to testable hypotheses for how the biological network
> implements the behavior._

So, IIUC, the approach involves (motivated) guesswork over the space of
practically implementable artificial neural network architectures (which is a
strongly biased subset of the set of all actual neural networks), to mimic
behavior that is presumably fully captured by biological neural networks.

While it is tremendously exciting to try something new and find surprises, why
would I expect this to be a fruitful systematic approach to neuroscience? I.E.
choose a particular behavior, find an artificial network solving it, and then
"understand" why it works biologically.

Basically comes down to two noob questions:

1\. Why should we expect the search over architectures of artificial network
architectures to be efficient/fruitful?

2\. How do we know that the brain might not use a completely different
architecture since computationally implementable NNs have many artificial
limitations.

I would appreciate any insights and expert comments. Thanks!

~~~
amelius
> How do we know that the brain might not use a completely different
> architecture since computationally implementable NNs have many artificial
> limitations.

I'd like to add this question: have we found the natural equivalent of
"backpropagation" yet?

~~~
matt4077
There's a somewhat similar mechanism which underlies all learning. It's "what
fires together, wires together", meaning that neural connections with
correlated activity patterns tend to get strengthened over time.

While that mechanism operates on what at first seems to be the forward pass,
the neurosystem seems to often operate in loops, with "useful" signals
continuing to go around for a while to provide the learning signal.

~~~
amelius
Interesting. Do you have a link which explains it in more detail? Is there a
name for this phenomenon?

~~~
DavidSJ
Hebbian learning is the name:
[https://en.wikipedia.org/wiki/Hebbian_theory](https://en.wikipedia.org/wiki/Hebbian_theory)

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marmaduke
As an engineer in a systems neuroscience lab, I'm likely biased, but this is a
misguided point of view: sufficiently flexible models for data have been
around for a long time, but they're not useful because they provide no insight
into the mechanisms at work. A DL model which might perform 100% still
requires study itself to produce anything with explanatory value.

 _edit_ to add, we are in fact doing projects with DL on real data, and it's
extremely difficult to reverse-engineer the trained DL model to figure out
how/why it does what it does: the parameters are simply uninterpretable
without a good theory or mechanism, and this is precisely what is sought.

~~~
dontreact
What if the models just can't be distilled beyond a certain point down to an
explanation in English words that makes sense to you and has "explanatory
power"?

Clearly reducing the problem from the brain to an ANN is valuable because if
we want to predict build or fix the brain, having approximations to pieces of
it as an ANN let us get closer to doing that in the same way that more compact
models or explanations help us get closer to doing those things.

~~~
marmaduke
You can air-quote explanatory power, but it remains a useful way to refer to
the relative utility of a scientific theory.

An approximate model is fine, but its variables require an interpretation
under some theory, which is not accomplished with a trained ANN.

It’s like saying a histogram is useful: sure is but not as a theory.

~~~
dontreact
At the moment, the way they are trained is not a good theory. But that is the
compact human interpretable way of thinking of these models. It seems like if
we keep iterating on this then we could arrive at a compact description of the
neural network which is its learning rules, architecture and environment. Why
is it important to have a compact explanation of the trained resulting model
if the learning rule, architecture and data are a fairly compact description?

It seems like for vision there are a few simple theories of learning:

having layers of nonlinearities

weight sharing across space

and some way of doing credit assignment on the loss from a visual task

Which taken together are enough to explain a large amount of the explainable
variance in the neural data. I agree that the models could get more
biologically realistic in the way they learn, but I disagree that it's
important to explain how the learned model functions in a compact way, since
there may be no such explanation better than the one based on learning.

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7373737373
I highly recommend this presentation:
[https://www.youtube.com/watch?v=PVuSHjeh1Os](https://www.youtube.com/watch?v=PVuSHjeh1Os)
(go to 21:00 if you are not convinced)

~~~
DerSaidin
Very interesting, thanks.

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sufiyan
Major takeaway : We are too bored to bother with really understanding stuff
from first principles, let the computer do my work and I'll write a paper that
I made the computer do my work.

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ChefboyOG
"We are all familiar with the standard paradigm in systems neuroscience"

I don't know why but that opening cracked me up. I'm sure everyone who
frequents the site is actually familiar, but reading that opener felt like
being back in high school and being asked about the book you didn't read.

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DanielleMolloy
As an example, this is a great article detailing out how the visual system and
convolutional neural networks use similar layers (representations) of
processing: [https://neurdiness.wordpress.com/2018/05/17/deep-
convolution...](https://neurdiness.wordpress.com/2018/05/17/deep-
convolutional-neural-networks-as-models-of-the-visual-system-qa/)

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graycat
IMHO, they could, maybe should, with some promise start with what are likely
relatively _low level_ brain tasks say, among the first things a brain does
with an input via touch, temperature, sound, sight, movement.

In a computer analogy, pay attention to the processor, main memory, Ethernet
connection, PCI bus, etc.

IMHO, for the usual meanings of _behavior_ , e.g., a kitty cat chases a mouse,
a kitty cat jumps successfully from a kitchen stool to the kitchen counter top
that has the offered food and water, a kitty cat follows its owner from room
to room, etc. are at too high a level.

With a computer analogy, such _behavior_ doesn't really directly involve the
processor instruction set, the word length in main memory, communications over
Ethernet or USB but involves software.

As we know well, given inputs and outputs of some software, even millions of
pairs of inputs and outputs, tough to infer the source code or even the
programming language used, C++, C, C#, Lisp, Fortran, etc.

Still, we know some things about software, e.g., the programs commonly use Do-
While, If-Then-Else, Call-Return, raise exceptional condition. So, there's a
microscopically thin chance that the coveted _theory_ of neuro-science could
have the coveted _theories_ with testable hypotheses at roughly that level.

For more about behavior, IMHO have to accept real _cognition_ , actually
thinking, e.g., considering scenarios and possibilities for responding. We can
guess that the work of this cognition will as software in a computer use
relevant accumulated data, some usually crude causal models, and some simple
deductive logic.

Can the line of attack in the OP address such cognition? Maybe, but here being
closely _reductionist_ standing on solid theories of the lower level
functioning is likely asking for too much and maybe, for some progress, not
necessary. E.g., commonly parts of psychology study cognition while largely
ignoring the lower level neuro-science mechanisms.

~~~
yontherubicon
Part of the trouble is that the processor, main memory and such are not really
discrete. The brain and nervous system is far more interconnected than that.
So what's happening isn't that the something in a discrete memory changes, but
the circuits themselves change.

What you are suggesting is, IIRC, far more high level than where the field has
been. What the author is suggesting is that they now have the ability to gain
larger samples than from a single neuron, so they're suggesting use cases for
the new data. This is somewhat akin to examining what is happening at several
logic gates within a microprocessor, probably quite a bit lower level than
what you're suggesting.

~~~
graycat
Have to take analogies with current computers with a large shovel full of
salt.

The OP says more than once that they are trying to understand "behavior". To
me that is like the kitty cat chasing a mouse, and in that case I suggested
starting at a level lower than that. Sure, maybe as you suggest that level is
higher than neuro-science has achieved so far, but I was responding to the OP.

A few days ago I did conjecture that building models of brain activity might
give some clues to how the brain actually works, maybe.

If memory in brains involved rewiring, than maybe could treat that memory just
as memory, although based on rewiring. In that case, maybe would want to study
the wiring and, if some memory changed, the new wiring and, thus, see how
memory writing is implemented by neuron rewiring. Then, maybe, the more times
that memory is read and the length of time the reading is active determines
how strong the connections are in the rewiring and, thus, how persistent the
memory is. Maybe.

~~~
nonbel
You may be interested in this paper:
[https://www.ncbi.nlm.nih.gov/pubmed/20700495](https://www.ncbi.nlm.nih.gov/pubmed/20700495)

They take basic neuronal growth "laws" inferred by Ramon y Cajal just by
looking at a crapload of stained neurons in the late 19th to early 20th
century and apply modern computational techniques to grow realistic neurons
based on randomly placed "growth signals" and an extremely simple rule.

Of course the next step is to grow groups of these "neurons" together with
non-randomly placed signals based on some sort of input and allowing one to
input on the other. I haven't followed up on it for a few years so maybe
they've gotten into that.

To me this looks like successful science in action. Something that looks very
complex (dendritic arbors) turns out to be explainable by a very simple to
understand process/rule/principle.

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peterlk
> low-cost recording technologies that are easy to use, such as the
> Neuropixels probes

I'm going to withhold my belief that it's going to go bonkers due to this
technology. Things go bonkers when they're useful to people and money is
behind it. Neuropixel probes require the scientist to shove silicon into a
brain to measure it. So, not very useful for people. If we figured out cheap
low temperature superconductors, and everyone courd afford an fMRI machine at
home, that would be bonkers.

With that said, I think brain research is ready to blossom. We now have proven
models for neural nets, and the compute capacity is getting there. The problem
is that it is still too expensive (cost, signal/noise, physical discomfort) to
reliably read (complex) brains.

~~~
kkylin
I don't know anything about neuropixels (added that Nature paper to my queue),
so don't know if they live up to the hype. That said, the standard tool in
many electrophysiology experiments are still tetrodes and variants
([https://en.wikipedia.org/wiki/Tetrode_(biology)](https://en.wikipedia.org/wiki/Tetrode_\(biology\))),
which can at best record a few to several cells at a time, within a small
radius of the recording tip. Simultaneously recording from hundreds to
thousands of cells in multiple brain areas would probably yield quite a bit of
valuable data.

Also, non-invasive techniques like fMRI
([https://en.wikipedia.org/wiki/Functional_magnetic_resonance_...](https://en.wikipedia.org/wiki/Functional_magnetic_resonance_imaging))
tend to have relatively poor spatial resolution, and track signals that
correlate with neural activity (e.g., blood flow) rather than actual
electrical activity. Since nearby neurons do not always encode the same
information or perform the same function, a spatially-averaged signal isn't
going to be as informative.

