Low carb diets

"Wayne S. Hill" wrote in message
...
OmegaZero2003 wrote:

"Wayne S. Hill" wrote...
OmegaZero2003 wrote:

I would not argue with Steve; he is onto something. He
put it all together and formulated another view of
reality consistent with certain other current views, yet
enabling a look at complexity_from_simplicity that has
heretofore not been appreciated in its scope of
applicability.

That's not clear to me.

Have you read his book?

I haven't read it, but have discussed this at length with
someone who has read it, attended Wolfram's lectures, and
discussed it with Wolfram.

Nonlinearity can arise in many different forms. Aside from
quadratic/cubic forms, which you might call "local"
nonlinearities (because the "slope" of the interaction
varies locally),

It does not ahve to; the form and whether it is local or
non-local are orthogonal.

Heh: we're definitely talking past each other.

My point is that there are linear systems.

Yawn.

This makes little sense. It is the complexity of a system
that determines the breadth and depth of a system_state
tree.

Again, we're talking past each other.

A complex system, or a dynamical system need not exhibit
emergent phenomena. The systems that do exhibt emergent
phenomena however, are usually complex dynamical sytems.

I don't know if I've mentioned this in this thread, but the
term "emergent" is not accepted by the bulk of NLD researchers
(mathematicians or physicists).


I would not agreee with that. it may (certainly is I will say) be the case
that what is characterized *as* emergent* is not agreed to.

There is a difference.

That there is a definition/theory of emergence is indisputable. That such
has been defined with mathematical characterizations of systems is also
true.

What usually is confused or, better, *conflated* by those whom you may be
speaking, is "emergence" and "synergistic phenomena".




That something varies smoothly (not descrete steps I presume
you mean), does not mean it does not have distinguisable
states!!!!!! That is what intergation and differentiation
are all about.

Once again, we're talking past one another.

Or VV!! Chaotic systems far from equilibrium. See
Prigogine.

Yeah, yeah.

Can you point me to a ref. where you are reading/getting
this relationship from?

Sorry, I just made it up (but it happens to be true).

Well - I think it is mostly true as I said. But my original
point is that there are linear systems (hell - that is what
LP is for!!).

But - in that case...

I think we should have been discussing the (a)/(b) dichotomy
I mentioned above. Whether nature is discontinuoous or
continuous.

Ack!

True, but I'm referring to much simpler ensembles of
neurons. The computational capability of a NN is directly
traceable to the threshold and saturation characteristics
of the neurons.

Well -err - not necessarily. The computational capability
of brain or any subset depends on how yu think it
characterizes, or *represents* information!!

But a tiny NN is very simple, and it's quite clear how it
stores information.

No is isn't!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

It isn't clear at all how brain *represents* information. This is one of
the biggest challenges facing neuroscience; how to span the gap between the
psychological phenomena/overt behavior (verbal behavior etc.) and the NCCs
(the neural correlates of consciousness)!!!!!!!!!!!!

And all those silly extrapolations of how much the brain can compute if the
brain can compute all day (based on such simplistic notions as you seem to
think are veridical), have been shown to be silly!

See Chalmer's page on such at:
http://www.u.arizona.edu/~chalmers/biblio.html

BTW, I have read ALL of those papers. This is my life's work.


If the neuron activation function were
linear, it would only be able to store y=Ax+b, which contains
very little information.

Right, and this is really important: beyond a narrow
range, the cost of responding linearly to external stimuli
would be too taxing to the organism. Consequently, the
organism lets that mechanism saturate, and turns on a
different one.

Or goes crazy! (Becomes chaotic)

See, I make a living working on systems that are chaotic, so I
don't view them as crazy.

I was using the term loosely.

For example, the human brain NEEDS
to be chaotic to be functional. Limit cycles are the abnormal
dynamics of brains (epilepsy, etc.).

I agree.




I don't see as strong a link as you do evidently. There are
far more important aspects that affect robustness and
effectiveness of a system than saturation and threshold.

I wasn't trying to imply that threshold and saturation are
fundamental to robustness of dynamics of nonlinear systems,
but instead that it's fairly easy for self-organizing (e.g.,
biological) systems that have these characteristics to develop
robust dynamics.


Hmmm - OK - let me think about that.


Here is another thought. Man-made complex systems are
engineered, usually, to clamp to a safe value(s), all
those parameters that may compromise safety or
efficiency/waste-control.

This is a simple form of saturation.

Yo can clamp well-before a saturation level!

Clamping is functionally a sudden saturation.

No! Not theoretically or practically.

Control systems are clamped at a setpoint that assures continued, NORMAL
operation, in the vast majority of cases.


Yeesh, enough already!

--
-Wayne

"OmegaZero2003" wrote in message
s.com...

"Wayne S. Hill" wrote in message
...
OmegaZero2003 wrote:

"Wayne S. Hill" wrote...
OmegaZero2003 wrote:

I would not argue with Steve; he is onto something. He
put it all together and formulated another view of
reality consistent with certain other current views, yet
enabling a look at complexity_from_simplicity that has
heretofore not been appreciated in its scope of
applicability.

That's not clear to me.

Have you read his book?

I haven't read it, but have discussed this at length with
someone who has read it, attended Wolfram's lectures, and
discussed it with Wolfram.

Nonlinearity can arise in many different forms. Aside from
quadratic/cubic forms, which you might call "local"
nonlinearities (because the "slope" of the interaction
varies locally),

It does not ahve to; the form and whether it is local or
non-local are orthogonal.

Heh: we're definitely talking past each other.

My point is that there are linear systems.

Yawn.

This makes little sense. It is the complexity of a system
that determines the breadth and depth of a system_state
tree.

Again, we're talking past each other.

A complex system, or a dynamical system need not exhibit
emergent phenomena. The systems that do exhibt emergent
phenomena however, are usually complex dynamical sytems.

I don't know if I've mentioned this in this thread, but the
term "emergent" is not accepted by the bulk of NLD researchers
(mathematicians or physicists).


I would not agreee with that. it may (certainly is I will say) be the case
that what is characterized *as* emergent* is not agreed to.

There is a difference.

That there is a definition/theory of emergence is indisputable. That such
has been defined with mathematical characterizations of systems is also
true.

What usually is confused or, better, *conflated* by those whom you may be
speaking, is "emergence" and "synergistic phenomena".




That something varies smoothly (not descrete steps I presume
you mean), does not mean it does not have distinguisable
states!!!!!! That is what intergation and differentiation
are all about.

Once again, we're talking past one another.

Or VV!! Chaotic systems far from equilibrium. See
Prigogine.

Yeah, yeah.

Can you point me to a ref. where you are reading/getting
this relationship from?

Sorry, I just made it up (but it happens to be true).

Well - I think it is mostly true as I said. But my original
point is that there are linear systems (hell - that is what
LP is for!!).

But - in that case...

I think we should have been discussing the (a)/(b) dichotomy
I mentioned above. Whether nature is discontinuoous or
continuous.

Ack!

True, but I'm referring to much simpler ensembles of
neurons. The computational capability of a NN is directly
traceable to the threshold and saturation characteristics
of the neurons.

Well -err - not necessarily. The computational capability
of brain or any subset depends on how yu think it
characterizes, or *represents* information!!

But a tiny NN is very simple, and it's quite clear how it
stores information.

No is isn't!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

It isn't clear at all how brain *represents* information. This is one of
the biggest challenges facing neuroscience; how to span the gap between
the
psychological phenomena/overt behavior (verbal behavior etc.) and the NCCs
(the neural correlates of consciousness)!!!!!!!!!!!!

And all those silly extrapolations of how much the brain can compute if
the
brain can compute all day (based on such simplistic notions as you seem to
think are veridical), have been shown to be silly!

See Chalmer's page on such at:
http://www.u.arizona.edu/~chalmers/biblio.html

BTW, I have read ALL of those papers. This is my life's work.

Since I know you are not gonna wade through 2000 papers, here is the part
that pertains:

http://www.u.arizona.edu/~chalmers/biblio/4.html#4.2

What is key is *how* the brain represents information and that is a subject
of *intense* debate and research!!!





If the neuron activation function were
linear, it would only be able to store y=Ax+b, which contains
very little information.

Right, and this is really important: beyond a narrow
range, the cost of responding linearly to external stimuli
would be too taxing to the organism. Consequently, the
organism lets that mechanism saturate, and turns on a
different one.

Or goes crazy! (Becomes chaotic)

See, I make a living working on systems that are chaotic, so I
don't view them as crazy.

I was using the term loosely.

For example, the human brain NEEDS
to be chaotic to be functional. Limit cycles are the abnormal
dynamics of brains (epilepsy, etc.).

I agree.




I don't see as strong a link as you do evidently. There are
far more important aspects that affect robustness and
effectiveness of a system than saturation and threshold.

I wasn't trying to imply that threshold and saturation are
fundamental to robustness of dynamics of nonlinear systems,
but instead that it's fairly easy for self-organizing (e.g.,
biological) systems that have these characteristics to develop
robust dynamics.


Hmmm - OK - let me think about that.


Here is another thought. Man-made complex systems are
engineered, usually, to clamp to a safe value(s), all
those parameters that may compromise safety or
efficiency/waste-control.

This is a simple form of saturation.

Yo can clamp well-before a saturation level!

Clamping is functionally a sudden saturation.

No! Not theoretically or practically.

Control systems are clamped at a setpoint that assures continued, NORMAL
operation, in the vast majority of cases.


Yeesh, enough already!

--
-Wayne

OmegaZero2003 wrote:

"Wayne S. Hill" wrote...
OmegaZero2003 wrote:

My original point to the OP on the topic was a retort to
the statement that *all* systems are nonlinear.

That is not true.

Nonsense. The OP never said any such thing. 8-p

Here is the context; I see you were pointing to biological
systems.

I wasn't the OP.

--
-Wayne

"Wayne S. Hill" wrote in message
...
OmegaZero2003 wrote:

"Wayne S. Hill" wrote...
OmegaZero2003 wrote:

My original point to the OP on the topic was a retort to
the statement that *all* systems are nonlinear.

That is not true.

Nonsense. The OP never said any such thing. 8-p

Here is the context; I see you were pointing to biological
systems.

I wasn't the OP.

OK - nevermind.

I thought it was you that said that about biological systems?



--
-Wayne

"Ignoramus32303" wrote in message
...
Some systems are linear, within some limits, and within a certain
accuracy.

There is no perfect linear system.

What is "perfect"?

Fist - ther is a rigorouus *mathematical* defintion of what it takes for a
system to be characterized as linear by definition.

You need to find out what that is (I gave a lot of info/refs for you to do
so easily).


Most systems that we talk about as
linear, fail at being linear as you increase the input, the failure
and non-linearity growing with the input.

Many do; not all.

Sometimes, to become
sufficiently non-linear, the input has to grow to unreasonable levels,

Yeah - like an (audio) amp being turned way up to saturation whenre you hear
screeches in the output.

reasonable defined as what we experience in real life.

Well - some audio systems are inadvertently turned up too loud!


I believe that I have given a fairly accurate summary that should be
supported by consensus.

Fairly accurate is not accurate enough, apparently. The devil is in the
details and there are systems that fall within the mathematical definition
that are not "llinearized", subclassed (as with the audio amp), or
approximated.


i

"Ignoramus32303" wrote in message
...
In article m,
OmegaZero2003 wrote:

"Ignoramus32303" wrote in message
...
Some systems are linear, within some limits, and within a certain
accuracy.

There is no perfect linear system.

What is "perfect"?

that gives a response exactly proportional to the output, to any
output.

Good!

I should have mentioned an obvious one: optics!

See:
http://www.wkonline.com/a/Linear_Sys...712 92885.htm


Fist - ther is a rigorouus *mathematical* defintion of what it takes for
a
system to be characterized as linear by definition.

You need to find out what that is (I gave a lot of info/refs for you to
do
so easily).


Most systems that we talk about as
linear, fail at being linear as you increase the input, the failure
and non-linearity growing with the input.

Many do; not all.

Sometimes, to become
sufficiently non-linear, the input has to grow to unreasonable levels,

Yeah - like an (audio) amp being turned way up to saturation whenre you
hear
screeches in the output.

try getting the amp to amplify 1 megawatt of power...

reasonable defined as what we experience in real life.

Well - some audio systems are inadvertently turned up too loud!


I believe that I have given a fairly accurate summary that should be
supported by consensus.

Fairly accurate is not accurate enough, apparently. The devil is in the
details and there are systems that fall within the mathematical
definition
that are not "llinearized", subclassed (as with the audio amp), or
approximated.

Yes, details are important.

i

OmegaZero2003 wrote:

"Wayne S. Hill" wrote...

I wasn't the OP.

OK - nevermind.

I thought it was you that said that about biological
systems?

Yeah, in a follow-up.

--
-Wayne

OmegaZero2003 wrote:

"Wayne S. Hill" wrote...

But a tiny NN is very simple, and it's quite clear how it
stores information.

No is isn't!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

A tiny artificial neural network (which is what I've been
referring to all along)? Sure it is. I'm not talking about
brains here (although a friend of mine has mapped the function
of the sense of smell of a crayfish brain, and he thinks he
knows how it works in detail).

Clamping is functionally a sudden saturation.

No! Not theoretically or practically.

Control systems are clamped at a setpoint that assures
continued, NORMAL operation, in the vast majority of cases.

sigh

--
-Wayne

"Wayne S. Hill" wrote in message
...
OmegaZero2003 wrote:

"Wayne S. Hill" wrote...

But a tiny NN is very simple, and it's quite clear how it
stores information.

No is isn't!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

A tiny artificial neural network (which is what I've been
referring to all along)? Sure it is. I'm not talking about
brains here (although a friend of mine has mapped the function
of the sense of smell of a crayfish brain, and he thinks he
knows how it works in detail).

Did you bother to see the papers on computing and representation I
referenced?

I do not care what the neurophsyiological response of a crayfish CNS is;
there ain't any instruments yet that can tell just what processes and
properties in *any* part of a NN exactly *represent* a "piece" of
information - even given what the definition of "information" is (beyond a
difference).

Ask your friend how the smell is represented to the crayfish? What is it
like to be a crayfish smelling a pizza for example?

Do you know what I am talking about when I use the word: "representation"?
It is key.

Otherwise , your friend is seeing Neural Correlates Of Smelling. Those are
correlates and not the representation of the qualia of the smell.

Let me give a quick example. We know how the VC (visual cortex) works in
detail. Trust me on this. Yet, we now next to nothing about what it is that
the NNet (together with its associated environmental context - the
neurochemicals, fields etc. - the soup the neurons are immersed in) does to
*represent*, say, the image of me performing a 1000 pound squat in a meet.
Or the image that Michael Jordan used to visualize himslef in any number of
patterns in which he scored.

The image is the representation and it is an image presented to a conscious
process that is made aware, or is aware, of that *represenation* qua (as)
the real thing!

This is a key issue in cognitive neuroscience - the binding problem is an
associated issue - how do all those things (smell, taste, thoughts etc.)
come together to represent a *whole* to the conscious being?

Much of that issue arises from the unknown manner in which a neural net in
situ (as I qualified above - in the soup), represents that information and
performs "computations" upon it.

In terms of computational neuroscience and ANNs(artificial neural net),
remember that the ANN is a couple orders of magnitude less sophisticated (at
least) (using simple I/O transforms and connection schemes used to build
multi-layer ANNS) than the real thing in situ.







Clamping is functionally a sudden saturation.

No! Not theoretically or practically.

Control systems are clamped at a setpoint that assures
continued, NORMAL operation, in the vast majority of cases.

sigh

--
-Wayne

"OmegaZero2003" wrote in message
s.com...

"Wayne S. Hill" wrote in message
...
OmegaZero2003 wrote:

"Wayne S. Hill" wrote...

But a tiny NN is very simple, and it's quite clear how it
stores information.

No is isn't!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

A tiny artificial neural network (which is what I've been
referring to all along)? Sure it is. I'm not talking about
brains here (although a friend of mine has mapped the function
of the sense of smell of a crayfish brain, and he thinks he
knows how it works in detail).

Did you bother to see the papers on computing and representation I
referenced?

Here is the page(s) with live links to the online papers.

http://www.u.arizona.edu/%7Echalmers/online.html

The Consciousness and Physics set and the Consciousness and Artificial
Intellgence set are appropo.

If you merely look through the titles, you will absorb a lot - that is - you
will see that the characterization of any *real* live NN *in situ* involves
a huge amount of parameters and processes, from the neurochemical and field
distributions and desities (and how that afffects signalling ) to
oscillatory effects on processing and on and on.

Lots of progress, but the upshot is that neurosci has not come up with a
theory of how the brain (or parts of the brain) represent "information" such
that it can be cognized/processed/computed by some conscious entity (like a
crayfish).

And if you tell the story that knowing a simple I/O scheme (based on the
go-no_go/thresholding/etc)of the black box model of a neuron (or group of
neurons), then you are missing, oh - perhaps 99% of what is going on!

Signaling, computation and representation are accomplished through a host of
mechansims that no theory has provided adequate explanation for yet.






I do not care what the neurophsyiological response of a crayfish CNS is;
there ain't any instruments yet that can tell just what processes and
properties in *any* part of a NN exactly *represent* a "piece" of
information - even given what the definition of "information" is (beyond a
difference).

Ask your friend how the smell is represented to the crayfish? What is it
like to be a crayfish smelling a pizza for example?

Do you know what I am talking about when I use the word: "representation"?
It is key.

Otherwise , your friend is seeing Neural Correlates Of Smelling. Those
are
correlates and not the representation of the qualia of the smell.

Let me give a quick example. We know how the VC (visual cortex) works in
detail. Trust me on this. Yet, we now next to nothing about what it is
that
the NNet (together with its associated environmental context - the
neurochemicals, fields etc. - the soup the neurons are immersed in) does
to
*represent*, say, the image of me performing a 1000 pound squat in a meet.
Or the image that Michael Jordan used to visualize himslef in any number
of
patterns in which he scored.

The image is the representation and it is an image presented to a
conscious
process that is made aware, or is aware, of that *represenation* qua (as)
the real thing!

This is a key issue in cognitive neuroscience - the binding problem is an
associated issue - how do all those things (smell, taste, thoughts etc.)
come together to represent a *whole* to the conscious being?

Much of that issue arises from the unknown manner in which a neural net in
situ (as I qualified above - in the soup), represents that information and
performs "computations" upon it.

In terms of computational neuroscience and ANNs(artificial neural net),
remember that the ANN is a couple orders of magnitude less sophisticated
(at
least) (using simple I/O transforms and connection schemes used to build
multi-layer ANNS) than the real thing in situ.







Clamping is functionally a sudden saturation.

No! Not theoretically or practically.

Control systems are clamped at a setpoint that assures
continued, NORMAL operation, in the vast majority of cases.

sigh

--
-Wayne