Connectionists: Brain-like computing fanfare and big data fanfare

[Thomas Trappenberg]

Thanks John for starting a discussion ... I think we need some. What I
liked most about your original post was asking about "What are the
underlying principles?" Let's make a list.
Of course, there are so many levels of organizations and mechanisms in the
brain, that we might speak about different things; but getting different
views would be fun and I think very useful (without the need to offer the
only and ultimate).

Cheers, Thomas Trappenberg


PS: John, I thought you started a good discussion before, but I got
discouraged by your polarizing views. I think a lot of us can relate to
you, but lhow about letting others come forward now?



On Fri, Jan 24, 2014 at 9:02 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.
>>
>>
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