"Tim Tyler"  wrote in message
news:c64q45$1o2s$1{at}darwin.ediacara.org...
> irr  wrote or quoted:
> > "Tim Tyler"  wrote in message
> > > irr  wrote or quoted:
> > > > "Tim Tyler"  wrote in message
> > > > > IRR  wrote or quoted:
>
> > > > > * You need to sequence the genome in question before you can
measure
> > > > >   its complexity;
> > > > >
> > > > > The third makes the metric less asethetically
attractive.  My
approach
> > > > > would probably be to say something along the lines of:
> > > > >
> > > > > "Always use FORTRAN-77 as your language". [...]
> > > >
> > > > IMO this third problem -- choosing a language with
which to quantify
> > > > complexity -- is still *the* showstopper when it comes
to biology.
> > >
> > > I like the answer I gave.
> > >
> > > I almost always give this answer.
> > >
> > > So far - IMO - I have had no serious complaints ;-)
> >
> > Better check your audience ;-).
>
> Do you have an objection?  If so, what is it?
>
> > > > While we might all agree that the primate brain is an incredibly
> > > > complex organ, it's not at all agreed upon what it is we mean by
this.
> > > > For example, a Kolmogorov measure fails miserably in
classifying the
> > > > brain as complex, after all you're really only talking about two
dozen
> > > > or so different recognized cell types stamped out in enormous
> > > > repetition with iterated connections between them -- in
other words,
a
> > > > digital representation of the brain is incredibly compressible.
> > >
> > > IMO - this makes no sense at all :-|
> > >
> > > An acceptable digital version of the brain would handle the same I/O -
and
> > > produce similar inputs from similar outputs.  This sounds like a job
for
> > > a huge computer with an *extremely* lengthy description to me - and of
> > > course a correspondingly enormous Kolmogorov complexity.
> >
> > Certainly a huge computer, but really an extremely lengthy description?
> > Check out the top 500 list (top500.org) -- the fastest computers in the
> > world are and will continue to be iterations of the single processor
system
> > you're likely reading this reply on right now.  While such massively
> > parallel systems -- including the human brain -- are increadibly
impressive
> > to look at, they are remarkably regular. [...]
>
> The human brain is not "remarkably regular" - and as such requires a
> lengthy description.
>
> Some computer systems are very regular - at least when they are turned
> on - but they are not very much like brains.
> -- 
> __________
>  |im |yler  http://timtyler.org/  tim{at}tt1lock.org  Remove lock to reply.
>

>From Erdi's "The Complexity of the Brain: Structural, Functional and Dynamic
Modules":
"Experimental facts from anatomy, physiology, embriology, and psychophysics
give evidence of highly ordered structure composed of 'building blocks' of
repetitive structures in the vertebrate nervous system.  The building block
according to the modular architectonic principle is rather common in the
nervous system.  Modular architecture is a basic feature of the spinal cord,
the brain stem reticular formation, the hypothalamus, the subcortical relay
nuclei, the cerebellar and especially cerebral cortex."  and on and on....

Going back to my original message, the neurohistology and neuroanatomy (that
is, the number of differentiated cell types and their organization) is
incredibly well laid out with astonishing regularity and organization.  This
is not remarkable.  What is remarkable is that such a regular structure can
exhibit amazingly complex features (look at, e.g. any of Christof Koch or
Francis Crick's recent work, among a wide field of other 'computational
neuroscientists').

These complex features that arise from a regular structure are very real and
active points of interest these days.  This is popularly referred to as
emergence, and one major question is if this emergence corresponds to the
appearance of language, consciousness, etc.  Now going back to my 'original
original' message, none of these complex features are apparent when looking
at the characteristic neuroanatomical structure, and so here again is
another example where a swath of different complexity metrics will fail to
meet our standards.

On the other hand, we could look instead at other features, such as the
interconnectiveness of various parts of the nervous system (scale free?  I'm
not sure) and perhaps get a better measure of complexity for a given metric.
I wouldn't argue against this, and Koch and many others argue convincingly
for this.  But the bottom line is, as I've been trying to convince you of,
the solution depends entirely on how you define your space and choose to
measure the complexity of that space.

Though it might apply great in theory or in determining regularity of
strings of numbers, your argument of "though the definitions may differ in
detail... it tends not to matter which one you use" just flat out fails when
it comes to applications in biology.
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