
How brains are built: Principles of computational neuroscience - blopeur
https://arxiv.org/abs/1704.03855
======
SubiculumCode
Here is my problem with the article: It has the title of a magna opus. The
article has a title that suggests content that is expansive and authoritative,
containing both rigorous theory and seminal empirical research. This article
is not at all deserving of such a title, and I cannot think of a single
neuroscientist I know that would put such a title on even their most ambitious
work, much less this loosely thought out treatment.

It is, frankly, embarrassing. Even in 2011.

~~~
nabla9
It was an introductory article written for private foundation that gives out
research grants.

Title was fine. Why it was posted to HN is different question.

~~~
SubiculumCode
If the context of the title is to capture the goals of the private funding
foundation and the article's contents are to describe some potential avenues
of investigation or outstanding problems, then that does make me reconsider my
expressed opinion in the grandparent. Thanks for sharing.

------
georgecmu
Dave Touretzky's course _Computational Models of Neural Systems_ should be
checked out by anyone with the interest in the topic.

Lectures, assignments and matlab code are all available online:
[http://www.cs.cmu.edu/afs/cs/academic/class/15883-f15/](http://www.cs.cmu.edu/afs/cs/academic/class/15883-f15/)

The readings page alone is a treasure trove of background text in
computational neuroscience theory starting from 1970s.

------
jmedwards
In the 1900s, people used to think the mind (and body, in a way) worked like a
steam engine. In part, the steam engine was used as the analogy because that
was the nearest and most technologically advanced input/output closed system
that was available. (And, importantly, that most people could grasp and talk
about.)

Hence colloquialisms like I need to "let off steam" or "I am under so much
pressure".

It turned out to be an analogy that was so far removed from reality, it was
useless.

I wonder if we are making the same mistake with computers as we know them
today?

"I really just need to reset and reboot, y'know."

~~~
simonh
From the Abstract: "...computational science 'is no more about computers than
astronomy is about telescopes.'"

So when they say 'Computational' Neuroscience, they're not particularly
referring to using computers, but analyzing neurological systems using
computational analytical techniques.

~~~
aschampion
A salient difference from astronomy is that computational neuroscience is
typically concerned with describing neural systems in terms of information
processing. Our principle technological example of an information processing
system is the computer. So while there's a distinction between "computational"
as (a) a tool used for analysis, (b) a methodology or model employed to
describe a system, and (c) an statement about a property of the system under
study, computational neuroscience refers to at least both ab and often abc.
This isn't the case with astronomy, because we aren't typically using
telescopes to study how stars bend and collect light like telescopes (although
we of course sometimes do).

~~~
simonh
It's just an analogy.

------
paulsutter
The article says nothing about how brains are built, nor does it mention any
principles of computational neuroscience.

The paper largely consists of smug statements such as:

> Despite huge efforts and large budgets, we have no artificial systems that
> rival humans at recognizing faces, nor understanding natural languages, nor
> learning from experience

Progress in these areas is very rapid, I hope the author won't be too
disappointed in the outcome.

~~~
pkolaczk
Currently artificial systems cannot rival humans at recognizing images,
understanding natural languages and learning from experience yet, despite all
the great progress that has been made. Great progress doesn't mean we're there
yet.

~~~
paulsutter
I had to check if you were the author of the paper. You seem to think exactly
the same way.

~~~
pakl
If you actually try current vision systems out on real raw video data as
opposed to clean datasets of "good" photos pre-selected by humans, you'll see
that they are terribly far from human performance.

Same goes for translation systems.

Most current systems (by their very design) lack dynamical representation
capability necessary for modeling interactions in/of the world. I hypothesize
this is important for AI that actually gets what it's dealing with.

------
bluetwo
I know it was published in 2011, but to extend the logic in this article:

To build a machine that can fly, we need to build a machine that can flap its
wings.

To build a car that moves, we must build a machine that can lift its two feet
in alternating motion.

To build a camera that sees, we need to build a lens that can flex itself to
change focus.

~~~
akyu
If you are talking about building AI, maybe you have a point. But its pretty
clear the domain of discourse here is computationally understanding the brain.
In which case I think it is prudent to actually understand the brain.

~~~
bluetwo
What you really want to understand is the abstract model the brain uses to
think, then you can go build that using other tools that may or may not look
like a brain.

Sure, the biology gives us some clues, but it may not be the most useful way
to view what is going on.

------
KasianFranks
We also have the mind, Computational Theory of the Mind (CTM)
[https://plato.stanford.edu/entries/computational-
mind/](https://plato.stanford.edu/entries/computational-mind/)

~~~
jorgemf
I like the mix between philosophy, biology and computation. Philosophy tries
to describe what is thinking, biology tries to understand how it happens and
in computation we try to replicate it.

~~~
posterboy
psychlogy is what tries to describe _thought_ and emotions. Philosophy tries
to describe _what to think about_.

~~~
jorgemf
I said it right, psychology is not involved. Thoughts and emotions are
emerging properties. But you need to define the basics as what is
intelligence, what is thinking. You only do that with philosophy. Philosophy
is not only about what to think about.

One of the biggest challenges to create an intelligent systems is to define
what is intelligence. If you can define it in a way you can measure it you can
track the progress. Nowadays there is not a clear definition.

~~~
posterboy
> One of the biggest challenges to create an intelligent systems is to define
> what is intelligence

That would be paradoxical. The challenge can't be to define the challenge.
Likewise, Philosophy presupposes a notion of _Philos_ and _Sophia_. Psychology
is a related field that can help to refine this notion, isn't it?

------
return0
The paper is not about computational neuroscience, but about the brain in
general. For those interested, there is a great book actually titled
"Principles of computational neuroscience"[1]. Also, the free "Book of
Genesis"[2] has an excellent short introduction to computational neuroscience.

1\. [https://www.amazon.com/Principles-Computational-Modelling-
Ne...](https://www.amazon.com/Principles-Computational-Modelling-Neuroscience-
Sterratt/dp/0521877954)

2\. [http://www.genesis-sim.org/iBoG/iBoGpdf/index.html](http://www.genesis-
sim.org/iBoG/iBoGpdf/index.html)

------
SubiculumCode
It would be nice if the article's title mentioned its publishing date. I wrote
a comment criticizing the article for ignoring a number of important papers
published since 2010, the latest published article that was cited, and then
had to delete it.

~~~
liuhenry
If you have the references handy, those recent papers would still be
interesting to folks (like myself) who aren't in the field/up-to-date but are
interested in the topic!

------
eli_gottlieb
Hey guys, I found the non-crappy papers:

[https://arxiv.org/abs/1604.00289](https://arxiv.org/abs/1604.00289) \--
_Building Machines that Learn and Think like People_

[http://rsif.royalsocietypublishing.org/content/13/122/201606...](http://rsif.royalsocietypublishing.org/content/13/122/20160616)
\-- _Active Inference and Robot Control: a case-study_

------
partycoder
The origin of the quote in the abstract is sort of disputed. While many people
attribute it to Dijkstra, the true origin is unclear.

