Connectionists: Brain-like computing fanfare and big data fanfare

[Thomas Trappenberg]

James, enjoyed your writing.

So, what to do? We are trying to get organized in Canada and are thinking
how we fit in with your (US) and the European approaches and big money. My
thought is that our advantage might be flexibility by not having a single
theme but rather a general supporting structure for theory and
theory-experimental interactions. I believe the ultimate place where we
want to be is to take theoretical proposals more seriously and try to make
specific experiments for them; like the Higgs project. (Any other
suggestions? Canadians, see http://www.neuroinfocomp.ca  if you are not
already on there.)

Also, with regards to big data, I believe that one very fascinating thing
about the brain is that it can function with 'small data'.

Cheers, Thomas


On 2014-01-25 12:09 AM, "james bower" <bower at uthscsa.edu> wrote:

> Ivan thanks for the response,
>
> Actually, the talks at the recent Neuroscience Meeting about the Brain
> Project either excluded modeling altogether  -  or declared we in the US
> could leave it to the Europeans.  I am not in the least bit nationalistic -
> but, collecting data without having models (rather than imaginings) to
> indicate what to collect, is simply foolish, with many examples from
> history to demonstrate the foolishness.  In fact, one of the primary
> proponents (and likely beneficiaries) of this Brain Project, who gave the
> big talk at Neuroscience on the project (showing lots of pretty pictures),
> started his talk by asking: “what have we really learned since Cajal,
> except that there are also inhibitory neurons?”  Shocking, not only because
> Cajal actually suggested that there might be inhibitory neurons - in fact.
>  To quote “Stupid is as stupid does”.
>
> Forbes magazine estimated that finding the Higgs Boson cost over $13BB,
> conservatively.  The Higgs experiment was absolutely the opposite of a Big
> Data experiment - In fact, can you imagine the amount of money and time
> that would have been required if one had simply decided to collect all data
> at all possible energy levels?   The Higgs experiment is all the more
> remarkable because it had the nearly unified support of the high energy
> physics community, not that there weren’t and aren’t skeptics, but still,
> remarkable that the large majority could agree on the undertaking and
> effort.  The reason is, of course, that there was a theory - that dealt
> with the particulars and the details - not generalities.  In contrast,
> there is a GREAT DEAL of skepticism (me included) about the Brain Project -
> its politics and its effects (or lack therefore), within neuroscience.  (of
> course, many people are burring their concerns in favor of tin cups -
> hoping).  Neuroscience has had genome envy for ever - the connectome is
> their response - who says its all in the connections? (sorry
> ‘connectionists’)  Where is the theory?  Hebb?  You should read Hebb if you
> haven’t - rather remarkable treatise.  But very far from a theory.
>
> If you want an honest answer to your question - I have not seen any good
> evidence so far that the approach works, and I deeply suspect that the
> nervous system is very much NOT like any machine we have built or designed
> to date. I don’t believe that Newton would have accomplished what he did,
> had he not, first, been a remarkable experimentalist, tinkering with real
> things.  I feel the same way about Neuroscience.  Having spent almost 30
> years building realistic models of its cells and networks (and also doing
> experiments, as described in the article I linked to) we have made some
> small progress - but only by avoiding abstractions and paying attention to
> the details.  OF course, most experimentalists and even most modelers have
> paid little or no attention.  We have a sociological and structural problem
> that, in my opinion, only the right kind of models can fix, coupled with a
> real commitment to the biology - in all its complexity.  And, as the model
> I linked tries to make clear - we also have to all agree to start working
> on common “community models’.  But like big horn sheep, much safer to stand
> on your own peak and make a lot of noise.
>
> You can predict with great accuracy the movement of the planets in the sky
> using circles linked to other circles - nice and easy math, and very
> adaptable model (just add more circles when you need more accuracy, and
> invent entities like equant points, etc).  Problem is, without getting into
> the nasty math and reality of ellipses- you can’t possible know anything
> about gravity, or the origins of the solar system, or its various and
> eventual perturbations.
>
> As I have been saying for 30 years:  Beware Ptolemy and curve fitting.
>
> The details of reality matter.
>
> Jim
>
>
>
>
>
> On Jan 24, 2014, at 7:02 PM, Ivan Raikov <ivan.g.raikov at gmail.com> wrote:
>
>
> I think perhaps the objection to the Big Data approach is that it is
> applied to the exclusion of all other modelling approaches. While it is
> true that complete and detailed understanding of  neurophysiology and
> anatomy is at the heart of neuroscience, a lot can be learned about signal
> propagation in excitable branching structures using statistical physics,
> and a lot can be learned about information representation and transmission
> in the brain using mathematical theories about distributed communicating
> processes. As these modelling approaches have been successfully used in
> various areas of science, wouldn't you agree that they can also be used to
> understand at least some of the fundamental properties of brain structures
> and processes?
>
>   -Ivan Raikov
>
> On Sat, Jan 25, 2014 at 8:31 AM, james bower <bower at uthscsa.edu> wrote:
>
>> [snip]
>>
> An enormous amount of engineering and neuroscience continues to think that
>> the feedforward pathway is from the sensors to the inside - rather than
>> seeing this as the actual feedback loop.  Might to some sound like a
>> semantic quibble,  but I assure you it is not.
>>
>> If you believe as I do, that the brain solves very hard problems, in very
>> sophisticated ways, that involve, in some sense the construction of complex
>> models about the world and how it operates in the world, and that those
>> models are manifest in the complex architecture of the brain - then
>> simplified solutions are missing the point.
>>
>> What that means inevitably, in my view, is that the only way we will ever
>> understand what brain-like is, is to pay tremendous attention
>> experimentally and in our models to the actual detailed anatomy and
>> physiology of the brains circuits and cells.
>>
>>
>
>
>
>
> Dr. James M. Bower Ph.D.
>
> Professor of Computational Neurobiology
>
> Barshop Institute for Longevity and Aging Studies.
>
> 15355 Lambda Drive
>
> University of Texas Health Science Center
>
> San Antonio, Texas  78245
>
>
>
> *Phone:  210 382 0553 <210%20382%200553>*
>
> Email: bower at uthscsa.edu
>
> Web: http://www.bower-lab.org
>
> twitter: superid101
>
> linkedin: Jim Bower
>
>
>
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