🚀 go-pugleaf

In article <35s56r$t3t@portal.gmu.edu>,
HARRY R. ERWIN <herwin@mason1.gmu.edu> wrote:
>What is consciousness? What is the mind? KIBO questions. Let me lay out 
>some ideas, though.
>.......
>Hence, objects of interest to the organism are dual to these (continuously
>varying) patterns of more-or-less coherent dynamics in the brain--the
>mind.  Note that these patterns are fundamentally semantic, although
>syntactic processing can be performed by the mind. Attention is the
>process by which these patterns emerge (are generated). The self is the
>internal pattern dual to the physically experienced body. Loss of
>consciousness is literally the loss of these patterns--loss of mind. 
>
Could you explain what you mean by "fundamentally semantic" as opposed
to (merely) "syntactic"?

>This suggests that if we develop a capability of monitoring these 
>coherent dynamics, we will have a way of objectifying the mind.
>
>--
>Harry Erwin
>Internet: herwin@gmu.edu 
>Just a dumb graduate student working on Katchalsky network models....
>(And if you believe that, I have a ranch in California to sell you.)

Andrzej

-- 
Andrzej Pindor                        The foolish reject what they see and 
University of Toronto                 not what they think; the wise reject
Instructional and Research Computing  what they think and not what they see.
pindor@gpu.utcc.utoronto.ca                           Huang Po

Andrzej Pindor (pindor@gpu.utcc.utoronto.ca) wrote:
: In article <35s56r$t3t@portal.gmu.edu>,
: HARRY R. ERWIN <herwin@mason1.gmu.edu> wrote:
: >What is consciousness? What is the mind? KIBO questions. Let me lay out 
: >some ideas, though.
: >.......
: >Hence, objects of interest to the organism are dual to these (continuously
: >varying) patterns of more-or-less coherent dynamics in the brain--the
: >mind.  Note that these patterns are fundamentally semantic, 

: Could you explain what you mean by "fundamentally semantic" as opposed
: to (merely) "syntactic"?

1. Non-symbolic, since these patterns are part of a spatially distributed,
continuously varying voltage level function (and are thus uncountable). 
Coherence then occurs when this function is sufficiently similar in
related regions that a non-linear amplification process is triggered and
entrains the voltage levels in nearby neurons into the pattern. 

2. Fundamentally semantic, since these patterns usually are the result of
integration of the continuous data field generated by external objects
(and expressed as an activation of apical dendrites of primary neurons)
against a measure defined by a continuous expectation field generated
internally (and expressed in several connected processes: direct
modulation of the activation levels of basal dendrites, generalized
modulation of synaptic efficacy by centripetal interneurons, and
longer-term hebbian learning at NMDA synapses). In other words, the images
of internal objects are generally (not always) dual to external objects
and so evolve and behave dually to the evolution/behavior of the external
objects.  Attention processes can (1) ignore these object images, (2) mark
them as generically expected, or (3) select them specifically for further
processing. Desimone's lab at NIMH has some recent results of interest on
this. 

--
Harry Erwin
Internet: herwin@gmu.edu 
Just a dumb graduate student working on Katchalsky network models....
(And if you believe that, I have a ranch in California to sell you.)

I probably need to add something to deal with those people who might 
argue that the brain can be simulated, hence a syntactic model is 
possible. 

I've noticed an interesting phenomenon on at least four scales in the
brain--mechanisms for amplifying patterns at a lower scale to a larger
pattern. These usually seem to involve stochastic resonance interacting
with carefully tuned dynamic systems. Thus there is a mechanism for
amplifying an output synchronism of a few neurons to a synchronism of
approximately 10^4 neurons. This involves a combination of non-linear
response on the cellular level with feedback mechanisms involving
principle cells and inhibitory interneurons. There appears to be a second
mechanism for amplifying a low level modulation of one or a few basal
dendrites into a cell-wide modulation. Bartlett Mel has evidence for a
third mechanism for amplifying synchronous stimulation of multiple
synapses in the dendritic tree into a modulation of the corresponding
dendrite. Finally, in the visual system there is a mechanism for
amplifying the synchronized arrival of two photons (a quantum mechanical
process) into a detection event at the level of the electrical synapse.
Karl Pribram, Stuart Hameroff, Paul Prueitt, and Roger Penrose seem to
believe there are other, similar processes, making up a pattern extending
from the quantum level to the level of the entire brain. I'm not sure
about this last, but there are enough of these between-scale connections
to make the question of effective simulatability very open at the least. 

I do not believe the brain can be simulated syntactically. The 
multiple-scale problem intrudes.

For those folks who posit a finite number of states for the universe based
on quantum mechanics. Please discuss how to go from quantum mechanical
theory to classical theory and vice-versa. Until you can do that, your 
argument is incomplete. Even afterwards, you will probably find that the 
simulation for the universe has to be bigger than the universe...

--
Harry Erwin
Internet: herwin@gmu.edu 
Just a dumb graduate student working on Katchalsky network models....
(And if you believe that, I have a ranch in California to sell you.)

herwin@mason1.gmu.edu says:
 
>Karl Pribram, Stuart Hameroff, Paul Prueitt, and Roger Penrose seem to
>believe there are other, similar processes, making up a pattern extending
>from the quantum level to the level of the entire brain. I'm not sure
>about this last, but there are enough of these between-scale connections
>to make the question of effective simulatability very open at the least.
.....
>For those folks who posit a finite number of states for the universe
based
>on quantum mechanics. Please discuss how to go from quantum mechanical
>theory to classical theory and vice-versa. Until you can do that, your
>argument is incomplete. Even afterwards, you will probably find that the
>simulation for the universe has to be bigger than the universe...
 
Penrose, Pribram, et al discussed the connection at the 3rd Appalachian
conference. If you didn't catch that, I'd be glad to try to summarize.
And if you did, but find a fault in it, I'd like to hear about it.
Either way, the proceedings will be out around November, and should
make for some invigorating reading.
 
But, yes, it seems that the simulation might be possible in principle,
but practically would require that every particle in the universe
and several more like it would be required.
 

--
dynasor@infi.net                                       The Doctor is on.

Dennis McClain-Furmanski (dynasor@infi.net) wrote:
: herwin@mason1.gmu.edu says:
:  
: >Karl Pribram, Stuart Hameroff, Paul Prueitt, and Roger Penrose seem to
: >believe there are other, similar processes, making up a pattern extending
: >from the quantum level to the level of the entire brain. I'm not sure
: >about this last, but there are enough of these between-scale connections
: >to make the question of effective simulatability very open at the least.
:  
: Penrose, Pribram, et al discussed the connection at the 3rd Appalachian
: conference. If you didn't catch that, I'd be glad to try to summarize.
: And if you did, but find a fault in it, I'd like to hear about it.
: Either way, the proceedings will be out around November, and should
: make for some invigorating reading.

I expect the proceedings to be out next September as in previous years.
The conference report is in preliminary draft, and I'll be turning it over
to Paul Prueitt tonight for his input. After that, Bruce MacLennan and
finally Karl P. November should see it published. Yes, it is invigorating. 

Cheers,
Harry Erwin
Internet: herwin@gmu.edu 
Just a dumb graduate student working on Katchalsky network models....
(And if you believe that, I have a ranch in California to sell you.)

HARRY R. ERWIN (herwin@mason1.gmu.edu) wrote:
: What is consciousness? What is the mind? KIBO questions. Let me lay out 
: some ideas, though.

  Sure. I'm an outsider, but I'll play too.

: The initial processing of sensory data involves both sensations of
: external objects (afference) and internal expectations (reafference).

  Ahh, the single person alone in the universe model... More as you get
to levels, below, but I suspect that when you have two or more people in
a tight feedback loop that it becomes difficult if not impossible to 
tell from the math where one "consciousness" stops and "another" starts.
(So, ok, guess who's a Distributed AI major.)  The really interesting
cases are the MORE THAN ONE cases, with many different time-scales for
feedback loops, crossing many more levels.

: These are merged via an integral transform (involving weightings defined
: on neural cell assemblies) to produce a distributed output signal. 

   Why not MANY signals/states on many separate parallel fronts? Some
electrical, some magnetic, some chemical, some genetic...

This
: can take two forms:chaos expressing novelty or distributed coherence
: (synchronization) signaling the existence of a known pattern. Detection of
: chaos can trigger an orienting reflex. The synchronized signal can either
: be processed simply as a signal that something known is being detected, or
: the associated sensory data can be processed further by downstream
: elements in a way similar to the processing of novel data. The gain of the
: system can be varied for optimum sensitivity. High gain tends to see
: artifacts as well as real patterns; low gain is insensitive. 

: Hence, objects of interest to the organism are dual to these (continuously
: varying) patterns of more-or-less coherent dynamics in the brain--the
: mind.

  Also objects of potential interest -- ie, a radiologist examining an 
X-ray looking for signs of some particular thing.

  Note that these patterns are fundamentally semantic, although
: syntactic processing can be performed by the mind. Attention is the
: process by which these patterns emerge (are generated). The self is the
: internal pattern dual to the physically experienced body. 

  If that last sentence has content, it eludes me.  How would you 
distinguish whatever it is you are affirming from some alternative?
If I'm physically experiencing someone else's body as well, am I them?
Are you saying that there is a mathematical transform between a 
body and ... a pattern, as real as, say, fourier tranform dual
representations? If so, you're saying the body can be represented
multiple ways, and you're calling one of those "the self" and the
other one "the body", even though they are simply different mathematical
representations of the same physical entity?  Or what?

  Loss of
: consciousness is literally the loss of these patterns--loss of mind. 

: This suggests that if we develop a capability of monitoring these 
: coherent dynamics, we will have a way of objectifying the mind.

: --
: Harry Erwin
: Internet: herwin@gmu.edu 
: Just a dumb graduate student working on Katchalsky network models....
: (And if you believe that, I have a ranch in California to sell you.)

   Overall, I agree that the nature of coherence is a critical concept,
as is ther question of how to deal with multiple interacting feedback
loops at multiple time scales and physical scales, even without including
other people in the picture.

   My tack is different, in terms of looking at this coherence as some
measure of the holy grail of "health",  with is increasingly showing 
up in situations where the behavior of the social context of "a person"
is as good, or a better predictor of "health outcomes" for "a person" than
any "internal" measures. 
   
   In terms of levels, however, as Marsden Bloise (?) pointed out
in J. of medicine and philosopy, circa 1982, the really interesting
medical problems involve situations where there is substantial vertical
interaction between the levels of this "curiously laminated" structure.
One can trace an economic downturn to a plant layoff to a family
disintegration to massive alcohol consumption to acid/base imbalance
problems.  Where do you "draw the line"?  What do you "fix" so it
will stay fixed?

  Why is it that a social support group has a noticeable effect on
immunosuppression and the outcome of chemotherapy?  Why should we
NOT be surprised by this?
  
  The problem seems to be more than just one (looking ahead to messages
up to this one) of getting from quantum mechanics to classical physics,
it's as if the levels, like network levels in the ISO network model, 
have completely different vocabularies and primitives and are almost
unreachable and incommensurate from each other.  

  IMHO, Turing's hypothesis is essentially false, for all practical
purposes, and, no, you CANNOT get from a sequential processor up to
any arbitrary level or model any arbitrary process, at least not in
the physical world we all live in.  I'm arguing a very strong
opposite, namely, that NO amount of processing, given unlimited 
computational power and an infinite time, in this physical universe
we live in, would get you from the atomic level to making a good
prediction of the existence of Haiti, Ronald Reagan, Roxanne, and
the oil depletion allowance. 

  To some extent, each level of the hierarchy builds on lower levels,
but, to some extent, each level is an independent agent with a life
of it's own that is invisible, incommensurate, and unknowable to 
lower levels, and maybe higher ones as well.    They affect each other,
sometimes, but do not, cannot, could not CONTROL each other, or
determine each other, except in cases where they all die.  There are
not only levels, there are, quite as importantly, GAPS between levels.

   No analysis at the pixel level alone would ever determine that your
"image" is one of Teddy Roosevelt, regardless how long you analyze
grey scales.  Teddy Roosevelt objects do not exist at the pixel level.

   I say this not to complicate the picture, but to think about what
it means.  To describe the brain/mind/body as having multiple interacting
levels and scales, physical and temporal, and then try to reduce it all
back to voltage levels seems, to me, to miss the point, namely, we
need a whole new vocabulary of primitives at each level of the system,
and a whole new formal calculus of how levels interact, what it means
to have simultaneous nested contexts, and how to even THINK about
systems with that type of qualitative complexity and structure, starting
with what's an "input" and what's an "output" of a system that feeds
various copies of its output back into its input stream, or are we
applying non-feedback concepts to heavy-feedback systems?

  For that matter, have we even gotten right whether the entity in
question is INSIDE the box, or OUTSIDE the box?    Where in the notes
is the true "fugue" located?  Where in the pixels is the power of a
great photograph located?  Maybe the only sense in which "you" have
a "self" is the sense in which I perceive you to have one.  What on
earth does it mean to "not be yourself today" then?


   Wade
  

-------------------------------------------------------------------------
R. Wade Schuette                        Internet: schuette@eecs.umich.edu
Ann Arbor, MI 48105 USA                 voice:  313.996.7479 (work)
-------------------------------------------------------------------------

        

I had a go at a similar bunch of concepts (entered under the heading "the 
colour red II"). If one has a detector of a commonly-encountered quality of
the stream of percept - "red" - then, if replicated sufficiently, each 
subdivision of the stream will arrive with a score for its possession of that
quality. Two such qualities are open to fuzzy logic boundary setting: in the
space spanned by the two dimensions, various areas come to be recognised as
flags for the presence of higher-level qualities. Systems come to draw on
these areas in the percept space as input into the generation of higher order
abstractions. These can build and build: a system the activation of which
as paralleled by "pleasurable social activity going on" lights up and so, in
coincidence, does another system: round+red+smell of rubber+.... = "ball"; 
and the two together serve to activate the motor skills which the toddler
has been acquiring. One notes that the brain appears to undergo "warming up"
of areas which *may* be needed: perhaps through chaotic signals, perhaps
by means of diffusable compounds, perhaps through ramifying, "voting" systems
which are built out of heirarchies whose sub units have become aroused.

My point is that this approach seems to allow three things which we know to
be true:

1: Hierarchical associations of data into high level abstractions.
2: Looped systems of feedback and self-reference by which such associations
   develop, are re-inforced or pruned and by which they learn.
3: Abstractions from associative logic into abstract, symbol based systems
   of reasoning, such that these can influence (and be influenced by)
   the more primary forms of association.

One of the difficulties of AI - at least as practiced by some of its 
proponents - is that it has strated with (3) and is wrestling as to how to 
get down to (1). Issues such as "grounding", as the representation of 
"common sense" knowledge, as pattern detection and the identification
of significant novelty all stem from this buttock-led approach.

_________________________________________________

  Oliver Sparrow
  ohgs@chatham.demon.co.uk

Oliver Sparrow discusses fuzzy logic as a way to approach concepts. 
Interestingly, chaotic neural networks turn out to be a fairly efficient 
way to implement fuzzy logic systems. They converge exponentially fast, 
and they support fuzzy decision-making fairly directly. Harold Szu spoke 
on this at Radford this month.

--
Harry Erwin
Internet: herwin@gmu.edu 
Just a dumb graduate student working on Katchalsky network models....
(And if you believe that, I have a ranch in California to sell you.)

I am amazed that simple neural nets can learn boolean logic quite effectively.
I have been playing with one over the weekend and it managed to handle the
equivalent of quite a deep array of NAND, AND and OR gates. This could
in principle, offer the thresholds and tapers against which other nets could
learn a fuzzy approach. The issue of objective function (ultimately, what
are these things trying to emulate) can be seen as an arbitrary falling into
one of a limited number of poles, followed by a genetic-algorithm like pruning 
of networks which fail to establish enough client or descendant groups. A lack 
of reinforcement at a somewhat higher level of abstraction than a Hebbian net,
however. 
_________________________________________________

  Oliver Sparrow
  ohgs@chatham.demon.co.uk