Connectionists: Brain-like computing fanfare and big data fanfare

[Ivan Raikov]

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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