

A platform for large-scale neuroscience [video] - mousetree
https://www.youtube.com/watch?v=Gg_5fWllfgA&index=5&list=PL-x35fyliRwjCR-gDhk1ekG4jh2ltgKSV

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vesche
While I think the presentation was interesting it was fundamentally just more
proof that "the brain has patterns". Wish that the talk had more depth in
terms of understanding or implementing artificial neural networks.
Understanding that the presentation was primarily to endorse Spark to more
data mining applications however, the talk seems successful.

~~~
frozenport
>>terms of understanding or implementing artificial neural networks

There is no motivation for a connection between artificial and biological
computational networks. A production ANN such as those used by missile
interception systems is not requires to match what the neurons in your brain
do. In fact, the biologists may find that our brains are not very efficient
and often make mistakes. As a practical matter, the behavior of neurons is
still up for debate with many models of interaction proposed.

There are often more effective models for machine learning, especially if you
have lots of data, such as Random Forest.

~~~
vesche
>>There is no motivation for a connection between artificial and biological
computational networks.

I would disagree, and say that future ANN research has high motivation to
become as powerful and complex as BNNs. And I think maybe you get ahead of
yourself by saying that our brains are not very efficient when the fastest
supercomputer we currently have built is still 100,000x less computationally
powerful than a human brain.

[http://www.wired.com/images_blogs/wiredscience/2013/05/neuro...](http://www.wired.com/images_blogs/wiredscience/2013/05/neurologist-
markam-human-brain3_f.jpg)

"Neural Networks attempt to bring computers a little closer to the brain’s
capabilities by imitating aspects of information in the brain in a highly
simplified way. Although neural networks as they are implemented on computers
were inspired by the function of biological neurons, many of the designs have
become far removed from biological reality."

[http://scholarsresearchlibrary.com/EJAESR-
vol2-iss1/EJAESR-2...](http://scholarsresearchlibrary.com/EJAESR-
vol2-iss1/EJAESR-2013-2-1-36-46.pdf)

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frozenport
How do they segment neurons? How can they be sure they are ablating a single
neuron?

~~~
etrautmann
some of their analyses are performed on raw voxels, but individual neurons for
ablation experiments can be identified either manually or using video image
processing to identify single neurons. It is a hard problem and a number of
people are working on improving it, but for many analyses it may not be
strictly necessary as a preprocessing step.

------
mousetree
Definitely the most interesting presentation at the Apache Spark Summit 2014

~~~
closetnerd
I didn't understand exactly why the forms of research this allows for is so
interesting or important even? He just claims that the high dimensional
visualizations are "obviously important and powerful" but doesn't explain why.

I'm always skeptical of researchers who come out with pretty visualizations
actually just looking for funding and/or recognition.

~~~
mousetree
It's interesting as it shows that people are using Spark for something other
than for the traditional web/enterprise analytics. Most of the summit was
focused on BI related use cases so it was really nice to see something
different for a change.

It's important as the speed and interactivity of Spark has apparently helped
the lab quite a lot in their research efforts. Some of the things shown in the
video, particularly the real-time refocusing at the end of the video, would
have been a lot more difficult, if not impossible, without something like
Spark (or similar)

~~~
closetnerd
I think Spark is from the Berkeley AMPLabs which is primarily focused of
machine intelligence, data mining and artificial intelligence in general.

Most, if not all, of there projects
([https://amplab.cs.berkeley.edu/projects/](https://amplab.cs.berkeley.edu/projects/))
are geared at solving similar problems.

I just don't see how this particular application is so important. At most, it
seems to be demonstration of sparks capacities but again it was geared for
high performance cluster computing anyways.

------
reader5000
Let's be honest. The rat is not "enjoying" it.

Also I'm in the camp that trying to reverse engineer the brain by studying
neural activity is like trying to reverse engineer Angry Birds by studying
register activity on an iphone.

Nevertheless, very cool visualizations.

~~~
closetnerd
Quite. The Blue Brain project did a similar thing by mapping all the neurons
in the neocortical column of a cat (or rat?) and visualizing the neural
activity and it hasn't yielded anything ground breaking.

Here's Henry Markram's Ted Talk:
[https://www.youtube.com/watch?v=LS3wMC2BpxU](https://www.youtube.com/watch?v=LS3wMC2BpxU).
But honestly, its really just something to impress people who don't have any
idea of what kind of implications this would have.

But I do think that obviously understanding neural activity is important but
only if its going to help yield understandings of the basic structural units
of the neocortex. Trying to understanding the neural activity of millions upon
billions is definitely not going to go anywhere.

~~~
etrautmann
The approach taken by Markram is quite different from what Jeremy is
presenting here. Markram is attempting to simulate large systems, and this
work is controversial, largely because many people don't think we understand
neural systems well enough to model them with appropriate fidelity at that
scale.

The work presented here represents a new take on a more classically accepted
approach. Record an animal's behavior as well as the neural activity, and
attempt to understand how neural activity controls the observed behaviors. The
difference here is a dramatic increase in scale and computational capability
by recording the entire brain of a zebrafish at once, and being able to run
analyses in a few seconds instead of hours, which has real implications for
experimental neuroscientists.

~~~
closetnerd
You're right, I made a mistake in implying that the "Markram did a similar
thing...".

Specifically I'm criticizing trying to analyze the neural activity of entire
brain regions. Neural simulation or actually trying to record neural activity
wasn't really my point. I assume both currently have drawbacks weather its
"not having accurate enough neural models for simulations" or "not
sufficiently or accurately being able to record neural activity".

Yes being able to run analysis in a few seconds instead of hours has real
implications for experimental analysis but again the benefits of the
technology were never under my criticism or skepticism.

I suppose what I'm specifically alluding to is my criticism of trying to do
such large scale analysis of neural activity itself given how little we know
about what we should be trying to look for at such a scale.

I say "we" but I should clarify I'm no neuroscientist.

