"KP_PC" <k.p.collins at worldnet.att.net> wrote in message
news:sxZ6b.133485$3o3.9448048 at bgtnsc05-news.ops.worldnet.att.net...
| "yan king yin" <y.k.y at lycos.com> wrote in message
| news:72de81ae.0309031548.39b66b2d at posting.google.com...| | [...]
| [...]
| When the problem is approached
| as a data-driven databasing problem, it's
| OK to look at an 'element' because the pre-
| viously-created database takes care of the
| larger integration that is commonly 'forsaken'.
There is a =hierarchy of detail= with respect to
the =global= neural architecture. As the
'elements' this hierarchy are considered,
each 'element's functionality must conform
to the already-worked-out more-global
functionality.
It's flat-out easy to discern the information-
processing 'rules' that the global neural
architecture - the highest, most-inclusive,
'level of the hieraqrchy of detail - impose
upon nervous system function. These 'rules'
are directly-readable in the neural archictecture.
So start building the data-driven databasing
at that 'level', and as one procedes to
succeeding 'levels', what's already been
worked out constitutes a functional delimita-
tion for the possibilities that can be considered
at each succeeding 'level' of detail.
This was the approach used in the develop-
ment of NDT. Globally-relevant features such as
the great decussations, ramp architecture,
'protopathic' and 'epicritic' conjunctions, etc.
as is discussed in AoK, constitute a 'ruleset'
that determines what's permissible within the
functionality at succeeding 'levels' of neural
architectural detail. Relying upon such 'rule'-
bound delimitation renders the functional
possibilities of the more-detailed 'elements'
successively-comprehensible.
One cannot just look at an ion gate and say
=anything= with respect to its functionality.
Rather, one can only look at that ion gate
with respect to its functional [physical] context
within higher [more-comprehensive] 'levels'
of the neural architecture.
Once stuff like the great decussations, etc.,
have been worked out, it cannot be that more-
detailed stuff 'un-does' the higher-'level' stuff,
because, if it were so, then the neural archi-
tecture would be 'at war with itself' [it would
be 'dis-eased'], and unable to direct the
behavior of its host organism in a way that
would allow the host organism to successfully
compete for its survival.
The stuff of this discussion is the single most-
important consideration in doing Neuroscience.
Neuroscience simply cannot be successfully
done in any other way.
It's 'funny'. I thought all of this would be obvious
to even casual readers of AoK [it's explicitly-stated
in Ap10], but, clearly, it has not been obvious. This
has been the case because, although the global
neural architecture has long been 'taught' in
Neuroscience curricula, it's been 'taught' in a rote-
memorization way with-out any emphasis upon
functional analysis of its Geometric [neural topo-
logical] ramifications.
[On top of all of this, has been my own major
shortcoming - being over-sensitive to the facts
that made it impossible for me to go through a
'normal' course in grad school - basically, I just
couldn't afford to, and admitting such Poverty
renders one 'untouchable' - so I 'over-comp-
ensated - FWIW, I admit it - sadly.]
When I rewrite AoK - if I ever gain the Freedom
to do so - I'll make all of this stuff that's implicit
throughout AoK, as it was originally written, highly-
explicit.
I'm smiling as I write this - considering what
must've been the reactions of folks who've
received my former discussions.
"Where'd he come up with that?"
:-]
Everything I've ever discussed has been rigorously-
founded in the global neural-architectural-detail
hierarchy - the data-driven databasing that I've
discussed in this and my prior post in this thread.
I never just 'pull stuff out of the air'. Rather, I read
'the datbase' from the 'level' of a neural 'element'
in question out to globality, and discuss the func-
tionality of that 'element' in the context of the,
typically-huge, strongly-delimiting, 'ruleset' im-
posed by 'overlying' [higher-'level', more global]
neural architecture.
This is the =only= way that the data yielded by
highly-detailed experiment can be comprehended.
Otherwise, the data just accumulate, non-integrated,
which overwhelms by virtue of its immense, discon-
nected, 'bulk'.
Anyway,
ken [k. p. collins]
| [...]
