The biological
evolution of information processing
in the evolution of language &
learning,
and
my search for=
the
missing link between body and mind
Section 3:
A simplified
‘information processing hardware’ model for vertebrates:
to control action and to integrate =
vision
Rainer von
Königslöw, Ph.D.
=
Abstract=
=
I
speculate that there is a missing link, something that connects physical
activity to mental activity.
Furthermore, I speculate that this missing link is related to the
biological evolution of language and learning. I investigate questions that are u=
sually
addressed in the field of neuroscience with empirical investigations. I propose a paradigm that investig=
ates
these questions from the perspective of information processing, and thus al=
so
fits into the field of artificial intelligence. I propose a design that uses an
‘inner language’ to control action sequences and to integrate
visual perception into action. I
investigate how this ‘inner language’ facilitates and enhances
learning. The investigation
demonstrates and validates the feasibility and benefits of the ‘inner
language’ design with working prototypes.
=
=
=
TOC
=
Introduction: summary of previous sections. 4=
Chapter
1: Hardware for information
processing -- hard-wired brains and the analogy with the evolution of compu=
ters. 5=
Chapter
2: Brain-based information
processing. 7=
Chapter
3: The brain as a process con=
trol
system... 8=
Chapter
4: Integrating vision with the
brain-based process control system... 12=
Expanded TOC<=
o:p>
=
Introduction: summary of previous sections. 4=
Chapter
1: Hardware for information
processing -- hard-wired brains and the analogy with the evolution of compu=
ters. 5=
Topic
1: hard-wired brains and lear=
ning. 5=
Topic
2: computers as analogous to =
human
thinking. <=
/span>5=
Topic
3: brains as analogous to com=
puters. 5=
Topic
4: actions and learning in a =
von
Neumann brain.. 7=
Chapter
2: Brain-based information
processing. 7=
Topic
1: action, perception, and in=
formation
processing. 7=
Topic
2: evolution and information
processing. 8=
Chapter
3: The brain as a process con=
trol
system... 8=
Topic
1: controlling action with a
process control system... 8=
Topic
2: local control of action wi=
th
programmable logic controllers. 9=
Topic
3: coordinating low-level con=
trol
of joint rotations. 11=
Topic
4: high-level control of
coordinated action.. 11=
Chapter
4: Integrating vision with the
brain-based process control system... 12=
Topic
1: managing the direction and=
focus
of vision as actions controlled with a process control system... 12=
Topic
2: converting the video signa=
l from
the eye. <=
/span>12=
=
=
=
=
=
=
=
=
=
=
In
neuroscience, the brain is usually seen as a hard-wired machine, with neuro=
ns
as the active elements, somewhat like transistors, and axons and dendrites =
as
the wiring. The architecture =
is
seen as somewhat dynamic, where learning may involve new connections through
changes in wiring. This model=
suits
reactive learning quite well, but has problems with predictive learning.
=
=
=
Most
computer applications are based on modeling how humans would do it. This is most evident in artificial
intelligence. Only occasional=
ly
does someone claim that computers (with him/her as programmer) could do it
better than what is found in nature or society – and usually that cla=
im
does not last too long.
=
=
=
I though
it would be interesting to reverse the analogy. The Eniac was hard wired and used =
the
decimal system, because that is what we do. Toward the end of building the Eni=
ac, a
few of the designers including Arthur Burks and John von Neumann got togeth=
er
to design the next generation. One
of the main improvements was to get away from the patch panel and switches =
and
to use stored instructions to tell the computer what to do. The new design became known as the=
von
Neumann architecture. Computer
hardware processes both instructions and data. That was the beginning of programm=
ing. The basic design has survived thro=
ugh
quite a few subsequent generations of computers.
=
![](3D"ilang_s3v1_files/image002.gif")
<=
o:p>
Note: J=
ohn
von Neumann is probably better known as ‘Dr. Strangelove’, or f=
or
his role in inventing the theory of games.
=
=
In the
early 90s it struck me that the brain to computer analogy could be reversed,
i.e. that the brain might also use its own version of the von Neumann
architecture. The primary
implication is that there should be a memory containing stored instructions,
and that there should be a control unit that interprets and executes those
instructions.
=
![](3D"ilang_s3v1_files/image004.jpg")
<=
span
style=3D'font-size:14.0pt;font-family:Garamond'>
=
=
=
Using
this architecture, actions are controlled by stored instructions that are
fetched from memory. In the f=
irst
generation of post-Eniac computers, the instructions were in machine langua=
ge,
i.e. directly specified actions using the functions and capabilities of the
control unit. Subsequent
generations of programming languages used symbolic language-like
representations and became more powerful and easier to use. I suspect that evolution resulted =
in
similar improvements in the power and usability of brain instructions.=
=
I
started thinking of brain instructions as symbolic representations. Taking the next step, we can imagi=
ne the
instructions for a particular action sequence as being documents in a text
processor. This is how comput=
er
programs are written by programmers.
Problem solving might involve modifying the brain-instructions or
brain-programs before using them. So
we could have predictive learning without ever having done anything and wit=
hout
having made mistakes, just by thinking about the instructions underlying the
action sequence.
=
=
Early
generations of mainframe computers always took the night off to do maintena=
nce
and to compile and test new programs.
I can easily see sleep and dreaming as being involved in the same
cycle. We can even take the a=
nalogy
further. Early computers coul=
d only
operate for a limited period of time before they would crash. The same cycle was repeated for ea=
rly
generations of PCs. So maybe =
my
brain has a limited period of time before it needs to ‘reboot’.=
=
=
Most of
the above is pure speculation, but I like the analogy with the von Neumann
architecture and the idea that the brain hardware might process both instru=
ctions
and data.
=
=
=
=
=
Action
is at the centre of evolution, certainly for vertebrates. Improvements in actions lead to be=
tter
survival and reproduction. Pe=
rception
is required for most actions.
Improvements in perception lead to improvements in actions. I see the data from perception as =
information
that is required to control action. To utilize such information we require
appropriate information processing.
=
=
=
Most
studies in evolution look at biological hardware. Some look at behaviours. My investigation looks at an even =
more
abstract level, the evolution of information processing.
=
=
Information
processing happens in real time. In
a large information processing system it is almost impossible to monitor the
information flows. Even with a
simple system, such as a PC, I do not stick a probe into my computer to find
out what my word processor is doing.
Biological information processing vertebrates, and even more for hum=
ans,
involve extremely complex systems.
I have seen estimates of 1011 to 1012 neurons.=
Transistor counts in integrate circ=
uits
in computer central processors, which are somewhat analogous but with fewer
connections, are already above 1010.
=
=
I investigate
perception and action from a perspective of information processing. I use computers as an analogy, but=
rather
than looking at the hardware I am looking at the high-level architecture and
design of the software, including the data representations and the
transformations. This is simi=
lar to
a requirements analysis, a feasibility study, and an initial design for a l=
arge
and complex IT system.
=
=
I start
with a model of the management and control of physical action of vertebrate=
s.
=
=
=
=
=
I’ve
spent some time with process control.
Process control involves a computer that monitors and controls some
process in manufacturing or in chemical plants. Monitoring and control information=
is
sent regularly and very frequently, typically at millisecond intervals. I once designed and prototyped suc=
h a
system for the big mainframe computers used by banks. Little PC computers watched differ=
ent
aspects of the operations of the mainframe, such as memory usage and print
queues. This was in the mid
80’s, when PCs were still growing up. This project was both a failure an=
d a
success. A failure because no=
body
else had done it before, and the project did not follow the standard approa=
ch
and methodology for the bank. So I
was personally walked out of the building by a Vice President of the bank
because he vehemently objected to the methodology. A success, because the idea, the
approach, and the initial prototypes came to life again a short time later,=
championed
by the ‘inside’ expert.
It was picked up by a major computer company, and now is used all ov=
er
the world.
=
=
=
This
experience gave me the idea that process control might be generalized beyond
just chemical processes, and might well apply to muscle control for
action. The idea is as
follows: An organic computer =
is
responsible for each muscle. =
This
computer generates a firing pattern for neurons that are connected to the
muscle. This is similar to ma=
naging
actuators in a distributed process control system, where the muscles are the
actuators and the neurons act as wiring.&n=
bsp;
=
![](3D"ilang_s3v1_files/image006.jpg")
<=
span
style=3D'font-size:14.0pt;font-family:Garamond'>
=
=
I assume
synchronous firing of neural muscle controls at 30 cycles per second,
corresponding to 30 video frames per second, so that I can record the motio=
n.
=
Muscles
need very frequent input from neurons to maintain the correct tension, but
normally the firing is not synchronous.&nb=
sp;
30 per second is about 15 per heartbeat, or at least 10 per stride f=
or
even a very fast runner. Even=
in
sleep, this neural activity continues.&nbs=
p;
Muscles are not ‘shut off’ until they are needed. They maybe relaxed, but there is s=
till
some neural signals going to them. <=
/span>
=
![](3D"ilang_s3v1_files/image008.jpg")
<=
span
style=3D'font-size:14.0pt;font-family:Garamond'>
=
=
The
above is a highly idealized representation of neural control of muscles tha=
t in
turn control the rotation at the joint.&nb=
sp;
The schematic below is slightly more realistic, but makes for a more
complex model, so I shall use the schematic above.
=
![](3D"ilang_s3v1_files/image010.jpg")
<=
span
style=3D'font-size:14.0pt;font-family:Garamond'>
=
=
=
Local
control is likely focused on geometry problems. For instance, the lower arm needs =
to
know how much and at what rate to rotate through the elbow. This can be complicated since the =
upper
arm may be moving as well. Yo=
u can
observe your elbow at work when you hold a full cup straight ahead of you, =
with
the upper arm level with the floor.
Now move the upper and lower arms so that the elbow is well behind y=
ou
and the cup is right against your side.&nb=
sp;
I expect you can do that without spilling a drop. You and I both could not solve the
geometry problems involved in this simple task, but some information proces=
sing
inside the body does it for us.
=
=
=
Let us
imagine that I have decided to walk over to a chair and sit down. That involves both legs with rotat=
ion at
the knees and at the hip. More
detailed analysis would show that there are also motions at the ankles and =
with
the upper body. All this acti=
vity
has to be coordinated and synchronized by the biological equivalent of the
supervisory control and data acquisition system.
=
=
=
=
=
The eye
has to be directed and focused. The
video information flowing from the retina has to be processed and converted
into a form that can assist with improving action.
=
![](3D"ilang_s3v1_files/image012.jpg")
<=
span
style=3D'font-size:14.0pt;font-family:Garamond'>
=
=
=
Using
video information and / or images for process control is quite common. I have worked on a couple of proje=
cts
like this. One case dealt wit=
h the
production of sparkplugs. New=
ly formed,
partially completed sparkplugs were visually inspected as they were coming =
by
on a conveyer belt. Deviation=
s were
noted by comparing the outline of the images to a standardized image. Remedial action could be invoked
selected automatically, or the sparkplug could be rejected. In another project, the sheen and =
colour
of nylon thread was examined for control of chemical surface treatment.
=
=
In all
of these projects, the image could not be used ‘as is’. The pixels of the image had to be
processed to derive information such as shape or reflectivity.
=
=
=
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