dreaming reality: the neural servo

mervyn at xs4all.nl mervyn at xs4all.nl
Mon Jul 28 13:01:03 EST 1997


Here's the plain text version of a paper that discusses
my ideas about perception and the role of emotion (arousal).
Feel very free to comment! 
Have a nice day,

	Mervyn van Kuyen 
	www.xs4all.nl/~mervyn	Mervyn at xsall.nl

============================================================

 T H E   N E U R A L   S E R V O   M O D E L 
 Mechanism, Strategy and Tactics for Survival 

Abstract

In this paper it is suggested that simple feedback control: a) is an
effective means of increasing chances of survival for any organism b)
carries a hidden agenda for more complex systems and organisms. An
additional mechanism that exposes this hidden agenda has been explored and
tested in simulations, providing a neural network with a generic strategy
for survival in the real world.This additional mechanism is suggested to
model our system of (mental) arousal.

Keywords: arousal, EEG, neural network, servo control, survival 



Introduction: Learning computers? 

If computers can learn, what do we need programmers for? Well, computers
can't learn, but what we can do, is design programs that can learn.
Designing such a program can be very similar to programming a computer
game for a human player.  This is because playing a game is all about
learning. Most games offer a world (that is to some degree similar to the
world we usually live in) and an agent, controlled by the player. The
agent can have human appearance, but it can also be a helicopter or a
space ship.

Every game allows the agent a degree of control. For example, control over
the location of the agent. However, control is useless without knowledge.
Where should the agent go next? Which locations are dangerous? To answer
these questions the player has to acquire knowledge. The act of exerting
control is simple (moving the joystick), it is exerting useful control
that has to be learned.

Both people and computer programs can acquire useful control skills by
means of feedback. For most games, this feedback takes a very simple form.
An indication of how well you are playing is often simply the length of
your game. This would be all the feedback needed to train a neural network
as well.

A nice example of a neural network is a crossing with traffic lights and
induction sensors. This is the kind of network that only gives a green
light when you move your car close enough to the traffic light. The
knowledge that this network represents is very limited. The most important
rule is that two crossing roads should not get a green light at the same
time. One solution would be that one traffic light dominates.  This would
be practical if this light regulates a very sparse stream of traffic. In
other cases it is more practical to alternate between the two directions,
just like a network without sensors would do. 

The point of this exercise is to demonstrate that the structure of network
can represent knowledge and that the structure can exhibit useful control.
To make such a network learn, one can introduce evolution. Evolution
requires two things: variation and selection. Variation can be obtained by
introducing changes to the structure (knowledge) of the network. Selection
can be obtained by introducing a learning rule, for example 'keep those
changes that reduce the number of sensed cars'. 

This learning rule is quite effective, since it would acquire quite an
amount of knowledge, especially in a community without traffic regulation
except 'green is go'!  First the system shall find our basic rule: don't
give two green lights. Why? The resulting traffic jam and collisions
provide an increase in the number of sensed cars so that the system will
never preserve connections that do give two green lights at the same time.
It will preserve connections that give green lights in such a manner that
the crossing will become less crowded, implying less resistance for the
flow of traffic.  So, we have a learning system that would be useful in
real life!

Even more practical is it to simulate such a network, the traffic and the
manipulation (evolution) of the network inside a computer. The traffic
would represent the world (the problem) and the network the agent. This
allows us to train and test the network without causing serious accidents.

Now that you are more familiar with the mode of thought associated with
artificial intelligence, I shall continue with the introduction of a
control mechanism that is much simpler than this traffic control system:
the servo.



1 Mechanism for survival: The servo

The first reason why a servo can be suggested to be a fundamental
mechanism for survival is that it is active until it is satisfied. An
example of a servo is a thermostat that controls a central heating system.
You provide a preset, a goal temperature. All the thermostat has to do, is
measure the actual temperature and activate the heater if the actual
temperature is lower than the goal temperature: heat supply = reality -
goal (negative 'heat supply' could activate a cooling system).

The question is, who controls the heater? Well, the only thing that
changes is reality itself (as a result of the heat supply). For this
reason, another term for servo control is feedback control. Reality can
control itself by means of structures that exploit feedback.

Any organism advanced enough to establish (genetically or through
experience)  preferences or goal states (like having a full stomach) can
benefit from these control structures that automatically initiate actions
(like swimming) until all goal states are satisfied. This is the first
level of explanation that suggests that the servo could well be a
fundamental mechanism for survival.



2 Strategy for survival: The hidden agenda of the servo

The second level of explanation is hidden. Hidden in the sense that to
provide a step by step demonstration would take to much time. This is what
the study of complexity is all about: finding out what certain rules do if
applied to large scale games and vast amounts of moves. In other words,
doing computer simulations and observing the results... 

The rule that has been tested during this study is the single rule that is
incorporated in the structure of any servo: be active until satisfied.
Another feature of a true servo controller is that its actions propel
reality towards a satisfactory state (no difference between reality and
goal).

When we use a neural network to simulate a servo that gives us an
important advantage: the goal state of such a 'neural' servo is not fixed
like in a servo. A neural servo can become satisfied not only by
propelling reality toward its goal state, but also by propelling its goal
state toward reality. This probably sounds quite abstract, but it
describes the most interesting device I have ever seen! That is because
when we train a neural net to become a servo, we are not limited to just
one 'control channel', but we can build a network of hundreds of servos
connected by internal circuits! The only thing we have to do, is enable
evolution to search for connections that satisfy the system.

Initially the system has no goal state 'in mind' for reality. This means
that if the system were able to blow up its sensed environment (if there
would be a detonator switch it could connect to) it would do so with great
satisfaction. However, this is not the hidden agenda of the servo I want
to discuss here.

Since a neural servo is probably not able to destroy its environment, it
shall make a trade-off between the learning and control effort. For
example, if a neural servo has to control a central heating system it
would acquire connections that generate a goal temperature that is equal
to the actual temperature. The effectiveness of this approach relies on
the fact that each time the actual temperature changes it will be much
easier to control the room temperature (after the structure has come to
represent a goal temperature) than to change this knowledge embodied by
the network.

In effect, a neural servo tends to keep its environment the way it got to
know it. This means that if the neural servo is a legitimate model for
human intelligence, a child inevitably (since its lack of control over its
environment) has to learn all about its environment, while later on it
will use the control it gains (by learning to communicate and by
developing senso-motorical skills) to preserve or recreate the kind of
environment it has grown up in. If its initial environment has been a
socially and physically healthy one, this tendency provides an effective
strategy for survival (for any social organism).

So far, we have explored the hidden agenda of the servo mechanism merely
by means of a thought experiment. The neural servo seems to be able to
provide a master plan for life: get to know your environment until you
have gained enough control to preserve (or recreate) it the way you found
it.



3 Tactics for survival: Arousal unlocks the hidden agenda

Compared to the results of the thought experiment, the results emerging
from actual simulations may appear very limited. Their strength, however,
lies mainly in their modeling power for the effects of arousal, not in the
absolute intelligence or control gained by the simulated neural servo.
This additional mechanism, arousal, is suggested to be the key to the
servo's hidden agenda.

The first neural servo that has been simulated, could exert no control at
all, while it was exposed to a repetive series of patterns through an
array of six input channels.  The simulated network consisted of six servo
controllers ('thermostats') which could be connected internally by a
maximum of 1200 connections between 36 neural nodes.  These neural nodes
generated an outgoing pulse whenever their input per saldo exceeded a
certain threshold.

This network, growing by means evolution (selecting for connections that
satisfy the system), turned out to be unable to learn effectively. It
could progress only until it failed. After any 'bad luck', the system
could not return to its old level of performance by destroying the last
added connections. In order to understand this effect, we'll have to
return to the basic concept of feedback control.

A servo is satisfied only if reality and goal state are equal: input =
reality - goal.  So task of this neural servo was to generate goal states
that absorbed reality before it got a chance to pour into the network. If
it had a lucky day, the prototype could find a few appropriate goal states
before it made a bad connection. Fortunately, these lucky runs provided an
interesting insight: Every latest improvement operated upon reverberating
patterns that had already been transformed by the structure that was
already in place! 

Although it is impossible to imagine the effect of hundreds of feedback
loops, delays and logical operations on a reverberating pattern, it is
possible to see why the prototype could not maintain its effectiveness
after a failure. All the operations acting at once simply didn't have the
same effect as when improvements kicked in, one by one, being collected by
evolution!

Solving this problem was easy enough. Connections already had a property
called 'physical strength', which was increased whenever the system became
more satisfied.  This way older connections had high strength, new ones
low strength (this allowed evolution to destroy weak connections whenever
the system became less satisfied, so that the system returned to its
original structural state). So, any mechanism that could re-enable all
connections in order of their physical strength, would cause the
operations to transform the patterns in the right sequence!

An existing system that could do this job very well, is our arousal
system.  One of the things our arousal system does, is creating
fluctuating electric potentials across wide areas of the brain. These
oscillations are often assumed to be a mechanism for synchronizing
neuronal firing. I would suggest that these potentials act as offsets that
effectively disable and re-enable connections in order of their physical
strength, effectively downsizing a network to a more primitive state
before 'releasing' more and more recent parts of the network again.

For the simulated neural servo this additional system did the trick: it
learned to absorb repetitive series of four patterns for up to 85%. The
hidden agenda of the servo turned out to be within reach as well:
confronted with two patterns, alternating at random intervals, the neural
servo preffered 'flipping' back to the first pattern and learn that single
pattern above learning both pattern and exerting no physical control. This
is about the most basic variation of 'get to know your environment until
you have gained enough control to preserve (or recreate) it the way you
found it'. So, the 'hidden agenda' is a real phenomenom, and arousal is
all we need to unlock it.



3.1 Tactics for survival: Arousal in action

The electric potentials in the brain (measured with an EEG) are only one
observable aspect of our system of arousal. This system connects many
things:

                    Level of arousal: 
                                      Low 
                                           High 
                    pulse rate: 
                                      slow 
                                           fast 
                    respiration: 
                                      slow 
                                           fast 
                    brainwaves: 
                                      slow 
                                           fast 
                    heat release: 
                                      slow 
                                           fast 
                    digestion: 
                                      fast 
                                           slow 
                    growth: 
                                      fast 
                                           slow 


Let's put the model to the test. When we are excited, our arousal is high.
We breathe fast to acquire more oxygen, our heart beats fast in order to
get the oxygen where it is needed: to the muscles. Heat generated by the
muscles is released quickly to avoid overheating. Digestion and growth are
put on hold. What happens in the brain?  The electric oscillations in our
brain are small and fast. So, according to the neural servo model this
results in having a nearly complete network ready and being able to
perform many (small) operation sequences per second. A limitation of this
mode of functioning is that large operation sequences are not done. This
means that context switches are very hard to follow: it is difficult to
operate a telephone when you have already started to panic.

At the opposite side of the spectrum we find 'sleep'. When we sleep, our
arousal is low. We breathe slowly, we hardly consume energy. Heat release
is minimal.  Digestion recombines complex molecules in order to keep our
body in good shape.  The electric oscillations in our brain are slow and
big. The deeper our sleep, the more primitive is the part of our brain
that still functions: we go back to our childhood. Any signal that we pick
up reverberates through this primitive brain. The lack of reality causes
more and more feedback to occur, until we fall back into an even more deep
sleep. In the morning when there is a sufficient supply of reality pouring
into our senses, the brain can finally launch a successful attack,
creating a phantasy world that keeps matching reality until we are fully
'awake': dreaming correctly or correcting reality.

A lack of sleep has the same effects as panicing. We are unable to refresh
our phantasy world, which requires a very long sequence of operations to
build (slow brainwaves). We then become aware of the fact that we create
reality from within: we 'start' halucinating. 



4 Future work

To conclude this paper I will outline, in a very condensed form, some more
issues that could be addressed by this model:

  One question that has remained unanswered sofar, is whether the neural
servo is a biological reality. Many observations seem to be in favour of
this model. It turns out, for example, that people tend to go back to
their early childhood during deep sleep.  This supports the suggestion
that low arousal (sleep) would temporarily downsize our neural networks to
more primitive states.

  Another question could be the role of arousal in communication. For
example, our breathing rate is controlled by our arousal, so it could well
be a means for communicating our moods. Note that a rizing tone of speech
relates to increasingly powerful breathing, rizing arousal. This may seem
trivial,but why should evolution put any effort into giving people some
universal protocol for communication when it can get emotional messages
across for free?

  Consciousness, finally, could be suggested to 'stand or fall' with the
way arousal allows more or less complete networks to transform the
patterns that travel within them. Remember that new connections do not add
'new ingredients' but that they are more like food processors: they
transform a pattern each time it passes by! For this reason it is likely
that more advanced (recent) connections often inhibit or correct the
transformations of their precursors. This way a picture arises of a
network that can allocate each subset of itself (a more or less primitive
state) time in order to generate a certain pattern: a pattern that should
match reality to the greatest possible extent.  According to this model
the ability to manipulate one's own arousal by any means would extent
diversity of the patterns that can be grown within one's mind.  It would
be a matter of experience, however, whether or not these manipulations
result in sensible patterns: patterns that match reality or have any other
meaning. This is where we might have stumbled upon a criterion for
consciousness. Maybe we lose consciousness only if our arousal makes such
a shift that a very unusual sequence of operations is performed, resulting
in a pattern that is increasingly mismatching reality? 



         Copyright (c) 1997 Mervyn van Kuyen -- All rights reserved. 



          Updated July 21 (1997)
   





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