Method to Generate Self-Organizing Processes in Autonomous
Mechanisms and Organisms
ELAPSE-TIME-QUANTIZING, TIME-SENSING, AUTOADAPTATION-
THEOREM OF ERICH BIERAMPERL;
THE "ALGORITHM OF THE LIFE";
WHAT DOES THE TETRAGRAMMATON ("JHWH") MEAN
IN EPISTEMOLOGY AND PROPOSITIONAL CALCULUS?
A NEW UNIVERSAL THEORY ?
US-Patent Nr.: US6172941 (filing date 16/12/1999)
EP Patent application EP01146406A1 (filing date 03/12/1999)
Abstract
A method to generate
recognition, auto-adaptation and self-organization in
autonomous mechanisms and organisms.
A number of sensing elements generate analog signals whose
amplitudes are classified into different classes of perception intensity.
The currently occurring elapse times between phase transitions are recorded
and compared with prior recorded elapse times in order to find covariant time
sequences and patterns. A motion actuating system can be coupled to the
assembly,
which is controlled by pulse sequences that have been modulated in accordance
with
the covariant time sequences. In this way the mechanism or organism in motion
is
prompted to emulate the found covariant time sequences, while being able to
recognize
its own motion course and adapting itself to changes of environment.
BACKGROUND
This invention describes a method for generating processes that facilitate the
self-organization of
autonomous systems. It can be applied to mechanistic fields as well as to
molecular/biological
systems. By means of the invention described herein, it is possible for a system
in motion to recognize
external events in a subjective way through self-observation; to identify the
surrounding physical
conditions in real time; to reproduce and to optimize the system's own motions;
and to enable a
redundancy-poor process that leads to self-organization.
Robot systems of the usual static type are mainly based on deterministic path
dependent regulating
processes. The digital outputs and values that control the robot's position
are stored in the memory of
a central computer. Many degrees of freedom can be created by a suitable arrangement
of coordinating
devices. Position detectors can be devices such as tachogenerators, encoders,
or barcode rulers
scanned by optical sensors that provide path dependent increment pulses. The activation mostly takes
place by
means of stepper motors.
It is also well-known that additional adaptive regulating processes based on
discrete time data are used
in path dependent program control units. These data are produced by means of
the SHANNON-
quantization method, utilizing analog-to-digital converters to sample the amplitudes
of sensors and
transducers. They serve to identify the system's actual value (i.e. its current
state). Continued
comparison of reference values and actual values are necessary for correction
and adjustment of the
regulating process. Newly calculated parameters are then stored in the memory.
This kind of adaptive
regulation is necessary, for example, in order to eliminate a handling robot's
deviations from a pre-
programmed course that are caused by variable load conditions.
If a vehicle that is robot-controlled in this way were to be placed into an
autonomous state, it would
generally be impossible to determine its exact position reference (i.e. coordinates)
by means of
tachogenerators or encoders. For this reason controlling values (or commands)
cannot be issued by a
computer - or preprogrammed into a computer - in an accurate manner. This is
true not only for robot-
controlled automobiles, gliding vehicles, hovercraft or aircraft, but also for
rail-borne vehicles for which
the distance dependent incremental pulses are often inaccurate and therefore
not reproducible. This is
usually caused by an uneven surface or worn or slipping wheels. Explorer robots,
which are used to
locate objects or to rescue human beings from highly inaccessible or dangerous
locations, must therefore
be controlled manually, or with computer supported remote control units. A video
communication
system is necessary for such cases in order to be able to monitor the motion
of the robot. However, in
many applications of robotics, this is inadequate. A robot-controlled automobile,
for example, should be
capable of avoiding dangerous situations in real time, as well as being capable
of adapting its speed to
suit the environment, without any human intervention. In such cases, it is necessary
for the on-board
computer to recognize the situation at hand, then calculate automatically the
next steps to be carried out.
In this way the robot-controlled vehicle ought to have a certain capability
for self-organization. This is
also true for other robot-controlled systems.
With regards to autonomous robot systems, techniques already exist to scan the
surroundings by means
of sensors and to analyze the digital sensor data that were acquired using the
above-mentioned discrete
time quantization method (see Fig. 1); and there already exist statistical calculation
methods and
algorithms that generate suitable regulating parameters. Statistical methods
for handling such regulating
systems were described in 1949 by Norbert WIENER. According to the SHANNON theorem,
the
scanning of the external environment must be done with at least double the frequency
of the signal
amplitude bandwidth. In this way the information content remains adequate. In
order to be able to
identify the robot's own motions, very high sampling rates are necessary. This
amplitude quantization
method currently in widespread use requires the correlation of particular measurement
data to particular
points in time (Ts) that are predetermined using the program counter. For this
reason this should be
understood as a deterministic method. However, practical experience has shown
that even ultrahigh-
speed processors and the highest sampling rates cannot provide sufficient efficiency.
The number of
redundant data and the amount of computing operations increase drastically when
a moving sensor-
controlled vehicle meets new obstacles or enters new surroundings at variable
speed. Indeed, C.
SHANNON's quantization method does not allow recognition of an analogue signal
amplitude in real
time, especially if there are changing physical conditions or variable motions
for which the acquisition of
additional information regarding the instantaneous velocity is necessary. This
is also true if laser
detectors or supersonic sensors are used, for which mainly distance data are
acquired and processed.
Therefore, although this quantization method is suitable for analyzing the trace
of a motion and for
representing this motion on a monitor (see Pat. AT 397
869), it is hardly adequate
for recognizing
the robot's own motion, or for reproducing it in a self-adaptive way.
Some autonomous mobile robot systems operate with CCD sensors and OCR software
(i.e. utilising
image processing). These deduce contours or objects from color contrast and
brightness differentials,
which are interpreted by the computer as artificial horizons or orientation
marks. Examples of this
technology are computer-supported guidance systems and steering systems that
allow vehicles to be
guided automatically by centre lines, side planks, street edges and so on. CCD
sensors - when one
observes how they operate - are analog storage devices that function like well-known
bucket brigade
devices. Tightly packed capacitors placed on a MOS silicon semiconductor chip
are charged by the
photoelectric effect to a certain electrical potential. Each charge packet represents
an individual picture
element, termed "pixel"; and the charge of each pixel is a record
of how bright that part of the image is.
By supplying a pulse frequency, the charges are shifted from pixel to pixel
across the CCD, where they
appear at the edge output as serial analog video signals. In order to process
them in a computer, they
must be converted into digital quantities. This requires a large number of redundant
data and
calculations; this is why digital recording of longer image sequences necessitates
an extremely large high
speed memory. Recognizing isomorphous sequences in repetitive motions is only
possible with large
memory and time expenditure, which is why robotic systems based on CCD sensors
cannot adequately
reproduce their own motion course in a self-adaptive way. With each repetition
of the same motion
along the same route, all regulating parameters must be calculated by means
of picture analysis anew. If
environment conditions change through fog, darkness or snowfall, such systems
are overburdened.
Pat. AT 400 028 describes a system for the adaptive regulation of a motor driven
vehicle, in which
certain landmarks or signal sources are provided along the vehicle's route in
order to serve as bearing
markers that allow the robot to keep to a schedule. Positions determined by
GPS data can also serve this
purpose. When the system passes these sources, the sensor coupled on board computer
acquires the
elapsed times for all covered route segments by means described in
Pat. U.S.
4,245.334, which details
the manner of time quantization by first and second sensor signals. The data
acquired in this way serve
as a reference base for the computation of regulating parameters that control
the drive cycles and brake
cycles of the vehicle when a motion repetition happens. The system works with
low data redundancy,
corrects itself in a self-adaptive manner, and is capable of reproducing an
electronic route schedule
precisely. It is suitable, for example, for ensuring railway networks keep to
schedule. However, in the
system detailed in the above-mentioned patent, it is not possible to identify
external objects and
surroundings.
It is an object of the present invention to provide an extensive method for
the creation of autonomous
self-organizing robot systems or organisms, which enables them to identify external
signals, objects,
events, physical conditions or surroundings in real time by observing from their
own subjective view.
They will be able to recognize their own motion patterns and to reproduce and
optimize their behavior in
a self-adaptive way. Another object of this invention is the preparation of
an autonomous training robot
for use in sports, that is capable of identifying, reproducing and optimizing
a motion process (e.g. that
has been trialed beforehand by an athlet) as well as: determining the ideal
track and speed courses
automatically; keeping to route schedules; representing its own motion, speeds,
lap times, intermediate
times and start to finish times on a monitor; and which is capable of acoustic
or optical output of the
acquired data.
SUMMARY OF THE INVENTION
The requirements outlined in the previous paragraph are solved generically by
attaching analog sensors
or receptors onto the moving system (for example, a robot system) which scan surrounding signal
sources whose amplitudes are subdivided by defining a number of threshold values. This creates
perception zones. The elapsed times of all phase transitions in all zones are measured by means of
analog or digital STQ quantization, and the frequency of the time pulses is modulated automatically,
depending on the relative instantaneous speed which is determined by the phase displacement of equi-
valent
sensors.Therefore the counted time pulses correlate approximately with the length-values
d(nnn).
With this method, the scanning of signal amplitudes is not a deterministic process:
it is not carried out
at
predetermined times with predetermined time pulses. The recording, processing
and analysis of the
elapsed times takes place according to probabilistic principles. As a result,
a physically significant
phenomenon arises: the parameters describing the external surroundings are not
objectively measured by
the system, but are subjectively sensed as temporal sequences. The system itself
functions as observer
of
the process. In the technical literature - in the context of deterministic timing
- elapse times are also
called "signal running times" or "time intervals ". According
to the present invention, the so-called
STQ elapse times in a signal-recognition process are quantized with every transition
of a phase amplitude
through a threshold value (which is effected by starting and stopping a number
of timers). This produces
a stream of time data. Every time elapsed between phase transitions in the "equal
zone", as well as the
time elapsed between transitions through a low threshold value then a higher
threshold value (and vice
versa),
can be recorded.
The present invented method differentiates between three principles of STQ quantization
(or,
respectively, elapse time measurements):
STQ(v) = sensitivity/ time quantum of velocity = Tv1,2,3...
This is the elapsed time determined by the signal amplitude that occurs when
a first sensor (or receptor)
S2 and an equivalent second sensor (or receptor) S1 moves along a corresponding
external signal
source Q, measured from the rising signal edge at the phase transition iTv1.1 of the first sensor signal to
the rising signal edge at the phase transition iTw1.1 of the second sensor signal; and likewise from
iTv2.1 to iTw2.1, from iTv3.1 to iTw3.1. (These transitions correspond to equivalent threshold values
P1,2,3... .) STQ(v) times can also be measured from falling signal edges. They serve as parameters for
the
immediate relative velocity (vm) of the system in motion.
STQ(i) = sensitivity/time quantum of interarrival = Tw1,2,3...
This is the elapsed time determined by the signal amplitude of a sensor (or
receptor) S in the field of a
corresponding external signal source Q; and/or determined by the signal amplitude
of a sensor (or
receptor) S that is moving along several equivalent external signal sources
Q1,2,3... This elapsed time
is measured from the rising signal edge at the phase transition iTw1.1 to the
falling signal edge at the
phase transition iTw1.2, likewise from the rising edge at iTw2.1 to the falling
edge at iTw2.2, and from the
rising edge at iTw3.1 to the falling edge at iTw3.2 etc.; or, equivalently,
from the falling signal edge at
the phase transition iTw1.2 to the rising signal edge at the phase transition
iTw1.3; and from the falling
edge at iTw2.2 to the rising edge at iTw2.3, from the falling edge at iTw3.2
to the rising edge at iTw3.3,
and so on (These transitions correspond to the equivalent threshold values P1,2,3..).
If the time counting
frequency for the STQ(i)-quantized elapse times Tw(1,2,3...n) is modulated in
proportion to the
immediate relative speed vm (which is detected by means of STQ(v) parameters),
then the counted time
pulses correlate to the relative distances through which the sensor coupled
system is moving. Therefore,
of course, the adapted elapse times are not identical to real physical measured
times that would have
been acquired from those relative lengths by usual timers. However, with absolute
physical invariance
between the system in motion and the surroundings (i.e. synchronism), no STQ
parameter can be acquired.
STQ(d) = sensitivity/time quantum of differentiation = Td1,2,3...
This is the elapsed time determined by the signal amplitude of a sensor (or
receptor) S within range of a
corresponding external signal source (Q1,2,3..), measured from the rising signal
edge at the phase
transition iTw1 of a rising amplitude trace to the rising signal edge at the
next higher phase transition
iTw2, and from the rising edge at iTw2 to the rising edge at iTw3, from the
rising edge at iTw3 to the
rising edge at iTw4, and so on; or, equivalently, from successive falling edges
when amplitude traces are
falling. (These transitions correspond to the equivalent threshold values P1,2,3,4..)
STQ(d) elapse times
are differentiation parameters for the slope of signal amplitudes (and consequently
for their frequency);
furthermore they serve as a plausibility check and verification of other corresponding
STQ data.
With this measurement, the relative motion between sensor and signal source
is not taken into account.
In the case of no relative motion between sensors and sources, changes in the
source field are detectable
and recognizable by recording STQ(i) and/or STQ(d) data. If the source field
is invariant, a recognition
is only possible if STQ(i) or STQ(v)- data are derived from variable threshold
values (focusing). If there
is absolute physical invariance, no STQ-quantum can be acquired, and recognition
is impossible. STQ(v)-
data are recorded in order to recognize the spatial surroundings under relative
motion, and/or to identify
relative motion processes so as to be able to recognize the self-motion (or
components of this motion);
as well as to reproduce any motion in a self-adaptive manner.
If the method presently being described is applied in a mechanistic area, the
above-mentioned
perception area zones may normally be set by a number of electronic threshold
value detectors with pre-
definable threshold levels, and the STQ(i) and STQ(d) elapse time data are acquired
by programmable
digital timers. The elapse timing process is actuated at an iT phase transition
as well as halted at an iT
phase transition. Then the time data are stored in memory.
Moreover, these STQ(v) elapse times are recorded by means of electronic integrators,
in which the
charge times of the capacitors determine those potentials that are applied as
analog STQ(v) data to
voltage/frequency converters, in order to modulate the digital time pulse frequencies
for the adaptive
measurement of STQ(i) and STQ(d) data, in a manner which is a function of the
relative speed vm.
In non-mechanistic implementations of the method presently being described,
it is intended that the so-
called perception area zones, as well as the threshold value detectors and the
previously described STQ-
quantization processes, are not formed in the same manner as in electronic analog/digital
circuits, but in a
manner akin to molecular/biological structures.
In other general implementations, it is intended that those time stream patterns
that consist of currently
recorded STQ data be continuously compared with prior recorded time stream patterns
by means of real
time analysis, in order to identify external events or changes in physical surroundings
with a minimum of
redundancy, as well as to recognize these in real time.
In yet another possible general implementation, it is intended that autonomously
moving systems,
that are equipped with sensors and facilities capable of the kind of time stream
pattern recognition
mentioned above, have propulsion, steering and brake mechanisms that are regulated
in such a manner,
that the autonomously moving system (in particular, a mobile robot system) is
capable of reproducing
prior recorded STQ time stream patterns in a self-adaptive way. When repeating
this movement, a
processor deletes unstable or insufficiently co-ordinated time stream data from
memory, while assigning
only those time stream data as instruction, which allows reproduction of the
motion along the same
routes in an optimal co-ordinated manner.
In addition, it is intended that the time base frequency for the above mentioned
STQ elapse timing is
increased or decreased in order to scale the time sequences proportionally,
whereby the velocity of all
movements is proportionally scaled too.
Finally, it is intended to focus the perception zones defined by threshold values,
in order to facilitate
recognition of invariant source fields and/or to ensure that motion courses
are repeated uniformly, if
convergence cannot be achieve sufficiently often. (This is object of an additional
patent application).
SHORT DESCRIPTION OF THE FIGURES:
Fig. 1 shows a diagram of SHANNON's deterministic method of discrete
time quantization of signal
amplitude traces.
Figs. 2a-c are graphic diagrams of the quantization of signal amplitude
traces by means of acquisition
of STQ(v), STQ(i) and STQ(d) elapse times, according to the herein described
non-deterministic method
Figs. 3a-c illustrate this non-deterministic quantization method in connection
with serial transfer of
acquired STQ(d)- elapse times, as well as time pulse frequency modulation of
simultaneously acquired
parameters of the immediate relative speed (vm).
Figs. 3d-g illustrate, in accordance with the presently described invention,
a method to compare the
currently acquired STQ time data sequences with prior recorded STQ time data
sequences, in order to
detect isomorphism of certain time stream patterns.
Fig. 4b shows vm dependent action potentials which propagate from a sensory
neuron (receptor) along a
neural membrane to the synapse where the covariance of STQ sequences is analysed.
Fig. 4c shows a number of vm dependent action potentials, which propagate
from a group of suitable
receptors along collateral neural membranes to synapses, at which the "temporal
and spatial facilitation"
of AP's is analysed together with the covariances of these STQ sequences in
order to recognize a
complex perception.
Fig. 4d shows a postsynaptic neuron that produces potentials with inhibitory
effects.
Fig. 4e and Fig. 4f show the general function of the synaptic
transfer of molecular/biologically recorded
STQ information to other neurons or neuronal branches.
Fig. 5 shows a configuration where the described invented method has
been applied to generate an
autonomous self-organizing mechanism, and where the STQ time data are acquired
by means of
electronics.
Fig. 6a shows a configuration of a concrete embodiment of the present
method, where (as in Figs. 2a
- 2c) the acquired STQ(v), STQ(i) and STQ(d) time data are applied to the recognition of certain spatial
profiles,
structures or objects when the system is in motion at arbitrary speed.
Figs. 6b-e illustrate several diagrams and schedules in accordance with
the particular embodiment in
Fig. 6a, in which the sensory scanning and recognition of certain profiles can
occur under invariable or
variable speed course conditions.
Figs. 7a-d show several configurations of sensors and sensor structures
for the recording of STQ(v)
elapse times, which serve as parameters of the immediate relative velocity vm.
Figs. 8a-f illustrate a configuration, as well as the principles under
which another embodiment of the
invention functions, in which the acquisition of STQ time data (see Figs. 2a
- 2c) is used to create an
autonomous self-adaptive and self-organizing training robot for use in sport.
This embodiment is capable
of reproducing and optimizing motion processes that have been pre-exercised
by the user. It is also
capable of determining the ideal track and speed courses automatically; of keeping
distances and times;
of recognizing and warning in advance of dangerous impending situations; and
of representing on
a monitor the self-motion, in particular the speed, lap times, intermediate
times, start to finish times and
other relevant data. In additional, this embodiment is capable of displaying
these acquired data in an
optical or acoustic manner.
Fig. 9 shows a schematic diagram of the automatic focusing of certain
perception zones or threshold
values, through which it is intended to improve and optimize the recognition
capability and the auto-
covariant behaviour of the system in motion. (This point is object of an additional
patent application).
Fig. 10 illustrates in a general schematic view the production of time
data streams by amplitude
transitions at certain sensory perception areas or sensitivity zones (or threshold
values, respectively) in
autonomous self-adaptive and self-organizing structures, organisms or mechanistic
robot systems, where
a multiplicity of types of sensors or receptors can exist.
DETAILED DESCRIPTION OF THE INVENTION
Fig. 1 shows a diagram of SHANNON's deterministic method of discrete
time quantization of signal
amplitude traces, which are digitized by analog/digital converters. In the usual
technical language this
method is called "sampling". This deterministic quantization method
is characterized by quantized data
(a1,a2,a3 ...an) which correlate to certain points in time (T1,T2,T3, ...Tn)
that are predetermined from
the program counter of a processor. In present day robotics practice, this currently
used deterministic
method requires very fast processors, high sampling rates and highly redundant
calculations for the
processing and evaluation of data. If one wants to acquire sensor data from
signal amplitudes of external
sources for the purpose of getting information about the spatial surroundings
of a system in which a
sensor coupled processor is installed, SHANNON's method is incapable of generating
suitable data for
the immediate relative speed and temporal allocation, data which are necessary
to optimize the
coordination of the relative self-motion. A recognition of its own motion in
real time therefore is not
possible. For this reason, this currently used deterministic method is inadequate
for the generation of
highly effective autonomous robot systems.
Figs. 2a - c show three different graphs of direct "sensory quantization"
of signal amplitude traces by
means of the herein described invented method. In contrast to the quantization
method shown in Fig. 1,
in this method no vertical segments of amplitude traces are scanned; there are
only elapse time
measurements carried out in three different complementary ways. As is easy seen,
it is necessary
to predefine certain numbers of threshold values 1 (P1, P2, ...Pn) in
order to provide different sensory
perception zones. Each residence time within a zone and time interval between
zones is recorded, as well
as the elapse time between the transition from a lower to a higher threshold
value and vice versa.
Fig. 2a shows the first of these three types of sensory time quantization.
It is designated STQ(v) elapse
time (i.e. sensitivity/time quantum of velocity), and produces a parameter for
the relative moment speed
vm. It can also be understood as the time duration between the phase transitions
of two parallel signal
traces at the same threshold value potential. That is similar to the standard
term "phase shift". In the
graph, the measured STQ(v) elapse times are designated with Tv(n). The phase
transitions at the
amplitude trace V, which is produced when the sensor (or receptor) 2
passes along a corresponding
external signal source 4, are designated iTv(n.n); the phase transitions
at the amplitude trace W, which
are produced when the sensor (or receptor) 3 passes along the same signal
source, are designated with
iTw(n,n). In the ideal case, the sensors 3, 4 are close together compared
to the distance c between
external signal source and sensors, c remains approximately constant, and both
sensors (or receptors)
display identical properties and provide an analogue signal; then two amplitude
traces V and W are
produced at the outputs of the mentioned sensors (the sensor amplifiers or receptors,
respectively) which
are approximately congruent. (Deviations from ideal conditions are compensated
by an autonomous
adaptation of the sensory system in a continuously improved way, which is described
later). When
sensor 2, in the designated direction, moves along the signal source
4, then the signal amplitude V
passes through the predefined threshold potential P1 at phase transition iTv(1.1). The rising signal edge
actuates
a first timer that records the first STQ(v) elapse time Tv(1).
The continually rising signal amplitude V passes through the threshold potentials
P2, P3 and P4; the
phase transition of each of these activates further timers used for recording
of further elapse times Tv(2),
Tv(3) and Tv(4). Meanwhile, sensor 3 has approached signal source 4
and produces the signal amplitude
trace W. When W passes through the threshold potential P1 at the phase transition
iTw(1.1), the rising
signal edge stops the timer, and the first STQ(v) elapse time is recorded and
stored. The same procedure
is repeated for the elapse times Tv(2), Tv(3) and Tv(4), when the signal amplitude
passes through the
next higher threshold values P2, P3 and P4. If V begins to fall, it first passes
through the threshold value
P4 on the falling shoulder of the amplitude trace. Now, the falling signal edge
activates a timer that
records the next elapse time Tv(5). At the further phase transitions iTv(3.2)
and iTv(2.2), where the
threshold values P3 and P2 are passed downwards, there are also timers which
are actuated when the
signal edges fall, in order to measure the elapse times Tv(6), Tv(7). If the
signal amplitude V rises
again, the STQ(v) parameters are recorded by the rising signal edges again.
The same procedure is
applied to stopping the timers at the phase transitions of the signal amplitude
W. This produces the time
displacement.
Fig. 2b shows another type of sensory STQ quantization. It is called
STQ(i) elapse time (i.e. sensitivity/
time quantum of interarrival). Simply, it is the time Tw a mobile system needs
for a relative length. It can
also be understood as the time duration between phase transitions of a signal trace at same threshold
potentials.
If the time counting frequencies corresponding to the relative speed parameters
Tv, (i.e., the
STQ(v) elapse times) are proportionally accelerated or decelerated, the recorded
modulated time pulses
correlate with the relative lengths. With absolute physical invariance between the sensor and the signal
sources (i.e., synchronism), no STQ(v) parameter can be acquired, but if an equivalent signal intensity is
changing, STQ(v) data are even obtainable when there is no relative motion. Therefore, during motion,
these data are necessary not only for recording variable signals, but also for
scanning spatial
surroundings.
In this figure, measured STQ(i) elapse times are designated with Tw(n). The
phase transitions, which are
produced by the amplitude trace W when the sensor (or receptor) 5 is moving along the corresponding
adjacent signal sources 6 and 7, are designated with iTw(n.n). As soon as the sensor (or receptor) 5
passes
in the marked direction along the signal source 6, the signal amplitude W goes
through the
pre-defined threshold potential P1 at phase transition iTw(1.1). The rising
signal edge activates a first
timer for the recording of the first STQ(i) elapse time Tw(1). Thereafter, the continually rising signal
amplitude W passes through the pre-defined threshold potentials P2, P3 and P4, and when these show a
phase transition, further timers are activated in order to record further elapse times Tw(2), Tw(3) and
Tw(4). Meanwhile, sensor 5 begins to move away from the vicinity of the
signal source 6. The falling
amplitude trace passes through the threshold potential P4, and upon the phase
transition iTw(4.2) the
falling signal edge stops the timer that was recording the STQ(i) elapse time Tw(4). Simultaneously, the
same falling signal edge activates another timer which measures the elapsed time Tw(5) up to the arrival
of the next rising signal edge. But this signal edge rises when sensor 5 passes along the equivalent signal
source 7. However, previously, the signal amplitude falls under the threshold
values P3 and P2, and
when these show the phase transitions iTw(3.2) and iTw(2.2), the timers recording
the STQ(i) elapse
times Tw(3) and Tw(2) are stopped. Simultaneously, additional timers recording
the elapse times Tw(6)
and Tw(7) are activated. They stop again at the phase transitions iT(2.3), iTw(3.3),
iTw(4.3) and iTw(5.1),
when the signal amplitude goes upwards again (but not before the sensor motion along signal source 7
starts). After those phase transitions, new timers start recording the next elapse times Tw(8), Tw(9),
Tw(10), Tw(11), and so on.
Fig. 2c shows a third type of sensory STQ quantization that is completely different to those of Figs. 2a
and 2b. It is termed STQ(d) elapse time (i.e., sensitivity/time quantity of differentiation); and it can be
understood as the time duration Td, measured between a first phase transition at a first predefined
threshold potential up to the next phase transition at the next threshold potential, which can be either
higher or lower than the first one. STQ(d) elapse times are parameters for the
slope of signal amplitude
traces, and consequently they are parameters for their frequency. By fast comparison
of STQ(d) elapse
times, signal courses can be recognized in real time; therefore, for the creation
of intelligent behavior,
STQ(d) quanta are just as imperative as STQ(v) quanta and STQ(i) quanta. The quantization of STQ(d)-
elapse times is possible under all variable physical states and arbitrary relative motion between sensor
and external sources, in which STQ(v) and STQ(i) elapse times are also quantizable.
If the STQ(d)
elapse times are acquired cumulatively and serially, then they can be used in
the verification and
plausibility
examination of STQ(i) elapse times (which are likewise acquired).
In the graph, the measured STQ(d) elapse times are designated with Td(n). The
phase transitions which
are produced by the amplitude trace W when the sensor (or receptor) 8 is in the field of a corresponding
signal source 9, are designated with iTw(n.n). When sensor 8 moves along
the corresponding signal-
source 9 in the direction shown, at first the signal amplitude W passes
through the pre-defined threshold
value P1 at the phase transition iTw(1.1). Of course, this also happens when
the field of this signal
source is active and/or variable, although the sensor and the corresponding signal source are in an
invariant opposite position. The rising signal edge activates a first timer that records the first STQ(d)
elapse time Td(1). When the rising amplitude trace W passes through the next
higher threshold value P2
at the phase transition iTw(2.1), this timer is stopped and the measured STQ(d)
elapse time Td(1) is
stored. Simultaneously, the next timer is activated, and records the elapse
time up to the next phase
transition at iTw(3.1), upon which it is stopped; then the next timer is activated up to the next transition
iTw(4.1), upon which it is stopped again, and so on. (All the measured elapse times are stored in
memory). At the phase transition iTw(4.1) the next timer is activated by threshold
potential P4.
However, since the amplitude trace does not reach the next higher threshold
value before falling to P4
again, no STQ(d) can be acquired with the last timer. Thus in this position
only the quantization of
STQ(i) elapse times, as described in Fig. 2b, can take place. The next STQ(d) elapse time Td(4) can
only be acquired when the signal amplitude falls below the threshold value P4 at the transition iTw(4.2),
upon which the next timer is activated, and stopped when the phase transition
at the next lower threshold
value P3 occurs. Simultaneously, the next timer is activated, and so on.
In mechanistic applications, where the analysis of signal amplitudes requires
the quantization of STQ(d)
elapse times, STQ(d) data are often acquired in combination with STQ(i) data. If it is intended to use this
quantization method to enable a robot to recognize its own motion from a subjective view (by detecting
and scanning the spatial surroundings when one moves along external signal sources),
then STQ(v) and
STQ(i) data are predominantly acquired. However, if the main intention is to
recognize external, non-
static optical or acoustic sources such as objects, pictures, music or conversations etc., then the
proportion of STQ(d) parameters increases, while the proportion of STQ(v) parameters decreases. In the
case of physical invariance (i.e. when there is no relative motion) no speed parameters can be derived
from any sensor signals, and only STQ(d) and STQ(i) elapse times are quantized.
Figs. 3 a - c illustrate an important aspect of the performance of the
present method, in connection with
serial transfer of acquired STQ(d) elapse times, as well as in connection with
time pulse frequency
modulation in relation to simultaneously acquired STQ(v) parameters which represent the instantaneous
relative speed (vm). However, this instantiation of the method is only suitable where mainly STQ(d)
elapse times are measured, together with those STQ(i) elapse times (see also Fig. 2c) which are
produced at the phase transitions when maximal threshold value near the maximum of the amplitude
are reached, or when the minimal threshold value near the minimum of the amplitude is reached. In this
case, all measured elapse times can be represented as serial data sequences. But if each phase
transition at each threshold potential generates STQ(d) elapse times as well as STQ(i) elapse times
(see also the notes for Fig. 5), then these data are produced in parallel, and therefore they have to be
processed in parallel.
Fig. 3a shows how a simple serial pulse sequence can be sufficient for
data transport of acquired
STQ(d) elapse times, if the threshold potentials P1, P2, P3... that define the
phase transitions 1.1, 2.1,
3.1... from which the STQ elapse times are derived, are "marked" either by codes or by certain
characteristic frequencies. In this figure, these "markers" are pulses with period t(P1), t(P2), t(P3)... and
frequencies f(P1), f(P2), f(P3).... These are modulated according to the respective
threshold potentials.
These identification pulses (IP) serve to identify the pre-defined threshold
values P1, P2, P3...., (or the
perception zones 1, 2, 3..., respectively). Only these identification pulses,
in cooperation with invariable
time counting pulses (ITPC) with the period tscan, or in cooperation with variable (vm modulated) time
counting pulses (VTCP) with the period t.vscan (see also Figs. 3b, 3c), enable the actual acquisition of
the STQ(d) elapse times Td(1), Td(2), Td(3), Td(4),... (or, respectively, the STQ(i) elapse times Tw(1),
Tw(2), Tw(3), Tw(4),.... that are produced at amplitude maxima or minima), as
we have already
described. Variable VTCP pulses with the period t.vscan, which are automatically
modulated relative to
the acquired STQ(v) parameters (i.e., the instantaneous moment speed vm), are used to scan the signal
amplitudes that are derived from external sources, in a manner proportional to speed. This reduces the
redundancy of the calculation processes considerably (see also Fig. 3c). The STQ(d) elapse times that
are acquired in such a vm-adapted manner by VTCP pulses are designated with Td (1,2,3,....); the
STQ(i) elapse times, acquired in the same manner, are designated with Tw(1,2,3...).
Fig. 3b shows the measurement of STQ(d) elapse times with invariant ITPC pulses with period tscan
and constant frequency fscan. This takes place as long as no STQ(v) parameter is acquired, e.g. when no
relative motion is present between sensor and signal sources, and therefore when no relative speed (vm)
can be measured.
Fig. 3c shows the measurement of STQ elapse times with modulated VTCP
pulses. These time counting
pulses depend on the instantaneous relative speed vm (or on the acquired STQ(v) parameter,
respectively) as well as their period t.vscan and frequency ƒscan in a manner that is proportion to vm. If
vm is very small or tends to zero, then the counting frequency ƒscan is likewise reduced to the minimum
frequency fscan (as seen in Fig. 3b). As shown in Fig. 2a, each STQ(v) parameter
is acquired by means
of a second adequate "front" sensor (or receptor). Vm is thus already
recorded even before the actual
STQ(d) and/or STQ(i) elapse time measurement. Therefore it is possible automatically to modulate
ƒscan for the measurement of Td(1,2, ...n) time data according to the acquired STQ(v) parameters,
in order to reduce the number of t.v calculations as well as to minimize memory requirements. Thus, a
largely redundancy-free analysis results.
Although the time impulses counted with this method are approximately covariant
with the relatively
covered lengths (d), it can be proved that they nevertheless represent modified
time data, and not
distance data. As with the origin of those data, the further processing and analysis of such modified
STQ elapse times Td(n) is dependent on probabilistic principles. The time data Td(n) are effectively
"subjectively
sensed".
In mechanistic systems the modulation of time counting frequencies in a manner
proportional to distance
traveled is done chiefly by means of programmable oscillators and timers, as
illustrated in Fig. 5.
However, in complex structured biological/chemical organisms, this self-adaptive process (a part of the
so-called "autonomous adaptation") is generated mainly by proportional alteration of the propagation
speed of timing pulses in neural fibers, as shown in Figs. 4a -d. However, autonomous adaptation and
self-adaptive time base-altering processes of the type described can also be
formed differently. They can
exist on molecular, atomic or subatomic length scales. The author names this
principle "temporal auto-
adaptation".
Figs. 3d - g show the conceptual basis for the comparison of currently
acquired STQ time data
sequences with prior recorded STQ time data sequences, as well as their statistics-based analysis. The
vm-modulated time data Td(n), shown in Fig. 3d having
the sequence 32 30 22 23 20 (cs = cycles), are
compared datum by datum with prior recorded time data Td'(n),
having the sequence 30 29 22 24 19,
which were likewise recorded in a vm-modulated manner. The comparison process is actually a
covariance analysis. When the regression curves of both time data patterns converge, covariance exists.
For these purposes, in mechanistic systems, coincidence measurement devices, comparator circuits,
software for statistical analysis methods or "fuzzy logic" can be
used.
The probability density parameters are added up, and as soon as the total value
within a certain period
exceeds a pre-defined threshold 10, then a signal 11 is produced that indicates that the sequence was
"recognized". This signal predominantly serves to regulate adaptively the actuators in mechanistic
systems (or motor behavior in organisms, respectively). Moreover, the signal shows that "autonomous
adaptation" has taken place prior to these time data patterns being recorded.
In respect of the motoric
behavior of any mechanistic or biological organism, it is true that recognition
of signal sequences goes
hand in hand with automatic adaptation (or "autonomous adaptation", respectively). This principle is
hereby termed "motoric auto-adaptation" or "auto-emulation".
Fig. 3g shows this auto-adaptation process in a schematic and easily
comprehensible manner. A
currently acquired Td
time data sequence is continually
compared with prior recorded Td' time data
sequences, and if approximate covariance appears, then the sequences fit like
a key into a lock. As
described in the following sections, this process produces a type of "bootstrapping" or "motoric
emulation", which constitutes a basic characteristic of redundancy-free autonomous self-organizing
systems and organisms. Admittedly, the covariance analysis of two time data patterns in mechanistic/
electronic systems is relatively complicated (see also Fig.5). But this is not
so in molecular/biological
organisms and other systems. In such systems, this "bootstrapping"
appears as a so-called "synergetic
effect", which is approximately comparable with rolling a number of billiard balls into holes arranged in
some pattern. (The name "synergetic" was first used by H. HAKEN in the year 1970.) Successful
potting is determined by speed and direction. If the speed and direction are altered, no potting will take
place. An attempt can also fail if the positions of the holes was somehow changed whilst the initial
positions of the balls were kept constant, even if their speed and direction
were covariant with the
original speed and direction (and when the covariance does not adequately take
into account the
changing pattern).
In a similar way, a current STQ time data sequence, acquired by an autonomous
self-organizing system,
produces a characteristic fingerprint pattern, and whenever a previously recorded reference pattern is
detected that is isomorphic to the currently recorded pattern, then auto-adaptation
and auto-emulation
results. This phenomenon is inherent in all life forms, organisms and elementary
structures as a
teleological principle. If no covariant reference pattern is found, the auto-adaptive
regulating collapses
and the system behaves chaotically. This motion changes from chaotic back to ordered as soon as
currently recorded STQ time patterns begin to converge to prior recorded STQ time patterns that the
analyzer finds to be covariant.
Figs. 4a - d illustrate a model for the acquisition and processing of
STQ(d) and STQ(v) elapse times
(see also Figs. 3a-g) and for temporal and motoric auto-adaptation in a molecular/biological
context.
The basic elements of the model have already been described in the neurophysiology literature by
KATZ, GRAY, KELLY, REDMAN, J. ECCLES and others. The present invention is of special
originality because temporal and motoric auto-adaptation is effected here by
means of STQ quanta,
which are described for the first time here. Such systems consist mainly of
numerous neurons (nerve
cells). The neurons are interconnected with receptors (sensory neurons), which
enables the recording
and recognition of the neurons' physical surroundings. In addition, the neurons cooperate with effectors
(e.g.muscles) which serve as command executors for the motoric activity. The expression "receptor"
or "sensory neuron" corresponds to the mechanistic term "sensor".
An "effector" is the same as an
"actuator", which is a known term in the cybernetics literature. Each
neuron consists of a cell membrane
that encloses the cell contents and the cell nucleus. Varying numbers of branches
from the neurons
(axons, dendrites etc.) process information off to effectors or other neurons. The junction of a dendritic
or axional ending with another cell is called a synapse. The neurons themselves can be understood as
complex biomolecular sensors and time pulse generators; the synapses are time
data analyzers which
continually compare the currently recorded elapse time sequences with prior
recorded elapse time
patterns that were produced by the sensory neurons and were propagated along
nerve fibers towards the
synapses. In turn, a type of "covariance analysis" is carried out there, and adequate probability density
signals are generated that propagate to other neighboring neural systems or to effectors.
Fig. 4a shows a so-called "action potential" AP that is produced
at the cell membrane by an abrupt
alteration of the distribution of sodium and potassium ions in the intra and
extra-cellular solution, which
works like a capacitor. These ionic concentrations keep a certain balance as
long as no stimulus is
produced by the receptor cell. In this equilibrium state, a constant negative potential 12, termed the "rest
potential", exists at the cell membrane. As soon as a receptor perceives a stimulus from an external
signal source, Na+ ions flow into the neutral cell, which causes the distribution
of positive and negative
ions to be suddenly inverted, and the cell membrane " depolarizes".
Depending on the intensity of the
receptor stimulus, several effects are produced:
(a) If the threshold P1 is not exceeded, then a so-called "electrotonic
potential" EP is produced which
propagates passively along the cell membrane (or axon fiber), and which decreases
exponentially
with respect to time and distance traveled. The production of EP is akin to
igniting an empty fuse
cord. The flame will stretch itself along the fuse, becoming weaker as it goes
along, before finally
going out. EP's originate with each stimulation of a neuron.
(b) If the threshold P1 is exceeded, then an "action potential" AP
(as in Fig. 4a) is produced which
propagates actively along the cell membrane (or axon fiber) with a constant amplitude in a self-
regenerating manner. The production of AP is akin to a spark incident at a blasting fuse: the fiercely
burning powder heats neighboring parts of the fuse, causing the powder there
to burn, and so on,
thus propagating the flame along the fuse.
AP's are used in the quantization of STQ(d) and STQ(v) elapse times. They are
practically equivalent to
identification pulses IP with periods t(P1), t(P2), t(Pn)..., which are shown in Fig. 3a. AP's signal
the occurrence of the phase transitions from which STQ(d) and STQ(v) elapse times derive. In addition,
the AP' indirectly activate the molecular/biological "timers" that are used for recording these elapse
times. But AP's do not represent deterministic sampling rates for amplitude scanning; and they do not
correspond to electronic voltage/frequency converters. Moreover, their amplitude is independent of the
stimulation intensity at the receptor, and they do not represent the time counting pulses used in the
measurement of elapse times. Rather, the recording of STQ elapse times is effected and modulated by
the velocity with which the action potentials propagate along the nerve fibers
(axons) and membrane
regions.
The time measuring properties of AP's are described in detail in the following
section:
If an EP, in answer to a receptor stimulus, exceeds a certain threshold value
(P1) 13, then an AP is
triggered. The amplitude trace of an AP begins with the upstroke 14 and ends with the repolarisation 15,
or with the so-called "refractory period", respectively. At the end
of this process, the membrane potential
decreases again to the resting potential P0, and the ionic distribution returns
to equilibrium. Not each
receptor stimulus generates sufficient electric conductivity to produce an AP. As long as it remains under
a minimal threshold value P1, it generates only the electrotonic potential EP (introduced above). (For a
better understanding of elapse time measurements in biological/chemical structures, see Fig. 2c and
Fig. 3a). The first AP, which is triggered after a receptor is stimulated, generates
initially (indirectly) the
impulse that activates the first timer that records the first STQ(d) elapse
time, when the signal amplitude
W passes through the threshold value of the potential P1 at phase transition iTw(1.1). This signal
represents simultaneously an identification pulse IP. The first AP corresponds to the first IP in a
sequence of IP's that represents the respective threshold value status or perception zone in which the
stimulation amplitudes were just found. As long as the stimulus at the receptor
persists, an AP 16a,
16b... is triggered in temporal intervals whose duration depends on the
respective thresholds in which
the stimulus intensities have just been found.
These temporal intervals correspond to those IP periods t(P1), t(P2),... that
are required for serial
allocation and processing of STQ elapse times (see Fig. 3a). The AP frequency is stabilised through the
so-called "relative refractory period" (i.e. downtime) after each
AP, during which no new depolarisation
is possible. Because the relative refractory period shortens itself adaptively
in proportion to the increase
in stimulation intensity at the receptor (e.g. if the EP reaches a higher threshold value P2 (or perception
zone) 13a), there is a similarity here with "programmable bi-stable multivibrators" found in the usual
mechanistic electronics. The downtime (refractory period) after an AP is shown
as the divided line 19.
Fig. 4a illustrates an "absolute refractory period" t(tot) following
a repolarisation. No new AP can be
created during this time, irrespective of the stimulation intensity at the receptor rises. The maximum
magnitude of a recognizable receptor stimulus is programmed in this way. Of importance is the fact that
both the duration of the relative refractory period as well as character of the absolute refractory period
are subordinate to auto-adaptive regularities, and are therefore continually adapting to newly appearing
conditions in the organism. Consequently, the threshold values P0, P1, P2.... from which STQ quanta
are derived are themselves not absolute values, but are subject to adaptive
alteration like all other
parameters; including, in particular, the physical "time".
We shall now elaborate upon what happens after the first STQ(d) elapse time
at P1 is recorded via the
first AP: If the stimulation intensity (with a theoretical amplitude W) increases from the lower threshold
P1 to the next higher threshold P2, then the following AP triggers indirectly the recording of the second
STQ(d) elapse time as soon as a phase transition occurs through the next higher
threshold P2. The same
process is repeated in turn for the threshold values P3, P4, ... and so on.
In each case, the AP functions
simultaneously as an identification pulse IP, as described in Fig. 3a. It therefore recurs in threshold-
dependent periods as long as a perception acts upon the receptor (i.e. for as long as the receptor is
perceiving something).
As an example, consider also Fig. 3a: As long as the stimulation intensity remains
in the zone P2, the
AP 17, 17a, 17b.... recurs in short temporal periods. These periods (or
intervals) are similar to those
periods of IP identification pulses (with period t(P2)) that are required for serial recording of the STQ
elapse times Td(2) and Tw(2). When the increasing stimulation intensity reaches the threshold value P3
(or perception zone 3) 13b, the AP's recur in even shorter time periods 18a, 18b, 18c... This
corresponds to the IP identification pulses with the period t(P3), shown in
the figure, which are indirectly
required for serial timing of the STQ elapse times Td(3) and Tw(3). An even
larger stimulation intensity,
for example in P4 (perception zone 4), would generate an even shorter period for the AP's. This would
correspond approximately to t(P4) in Fig. 3a. The maximum possible AP pulse frequency is determined
by t(tot). Shorter refractory periods, after the depolarization of APs, also produce smaller AP-
amplitudes. This property simplifies the allocation of AP's in addition.
In the following, the generation of the actual time counting pulses for STQ
quantization is detailed. These
pulses are either invariable ITPC or vm-proportional VTCP, as illustrated in
Fig. 3a. The time counting
pulses for the quantization of elapse times are dependent on the velocity with
which the AP propagate
along an axon. This velocity is in turn dependent on the "rest potential"
and on the concentration of Na+
flowing into the intracellular space at the start of the depolarization process,
as soon as perception at the
receptor cell causes an electric current to influence the extra/intra-cellular
ionic equilibrium.
With the commencement of stimulation of a receptor (at the outset of a perception),
only capacitive
current flows from the extra-cellular space into the intracellular fluid. This generates an "electrotonic
potential" EP, which propagates passively. If this EP exceeds the threshold P1, then an AP, which
propagates in a self-regenerating manner along the membrane districts, is produced.
The greater the
capacitive current still available after depolarisation (or "charge reversal")
of the membrane capacitor,
the greater the Na+ ion flow into the intracellular space, and the greater the
available EP current that can
flow into still undepolarized areas. The rate of further depolarization processes in the neuronal fibres,
and consequently the propagation speeds of further AP's, are thus increased
proportionally.
The charge reversal time of the membrane capacitor is therefore the parameter
that determines the value
12 of the resting potential P0. When a stimulus ("excitation")
starts from the lowest resting potential 12,
then the Na+ influx is the largest, the EP-rise is steepest and the electrotonic flux is maximum. If an AP
is triggered, then its propagation speed is in this case also maximum. But when a receptor stimulus starts
from a higher potential 12a, 12b, 12c...., then the Na+ influx is partially inactivated, and the steepness
of the EP-rise as well as its electrotonic flux velocity is decreased. Therefore,
the propagation speed of
an AP decreases too.These specific properties are used in molecular/biologic
organisms to produce
either invariant time counting impulses ITCP, with periods tscan, or variable time counting impulses VTCP
with periods t.vscan. In the latter case, the VTCP's are modulated in accordance with the relative speeds
vm (via the STQ(v) parameters), and therefore have shorter intervals (see Figs. 3b, 3c). The STQ(v)-
quantum is determined by the deviation of the respective starting-potential
from the lowest resting-
potential P0, which serves as a reference value, and is measured by the duration
of the capacitive
charging of a cell membrane when a stimulus occurs at the receptor.
The duration of the charging is inversely proportional to the velocity of the
Na+ influx through the
membrane channels into the intracellular space. A cell membrane can be understood as an electric
capacitor, in which two conducting media, the intracellular and the extracellular
solution, are separated
from one another by the non-conducting layer, the membrane. The two media contain
different
distributions of Na/K/Cl ions. The greater the "stimulation dynamics" (see below) that first influences
the outer molecular media - corresponding to sensor 2 in Fig. 2a - and, subsequently, the inner
molecular media - which corresponds to sensor 1 in Fig. 2a - the faster is the Na+ influx and the
shorter the charging time (which determines the parameter for the relative speed
vm), and the faster is
the AP propagation velocity v(ap) in the neighbouring membrane districts. The
signals at the inner and
outer sides, respectively, of the membrane, correspond to the signal amplitudes V and W. The velocity
v(ap), therefore, indirectly generates the invariant time counting pulses ITCP or the variable vm-
proportional
time counting pulses VTCP.
These variable VTCP pulses are self-adaptive modulated time pulses that are
correlated to the relative
length. As explained in the following (contrary to the traditional physical
sense), no "invariant time"
exists -- only "perceived time" exists. Of essential importance also
is the difference between "stimulation
intensity " whose measurement is determined by the AP frequency and therefore by the refractory period,
and the "stimulation dynamics", whose measurement is defined by the charge duration of the cell
membrane and therefore also by the speed of the Na+ influx. "Stimulation
dynamics" is not the same as
"increase of the stimulation intensity". It is a measure of the temporal/spatial
variation of the position of
the receptor relative to the position of the stimulus source, and therefore of the relative speed vm. The
stimulation intensity corresponds to signal amplitudes, from which vm-adaptive STQ(d) elapse times
Td (1,2,3...) are derived, while the stimulation
dynamics is defined by the acquired STQ(v) parameters.
Fig. 4b and Fig. 4c show the analysis of STQ elapse times in a
molecular/biological model in an easily
comprehensible manner. The results of the analysis are used to generate redundancy-free
auto-adaptive
pattern recognition as well as autonomous regulating and self-organization processes. The organism in
the particular example shown here is forced to distinguish certain types of foreign bodies that press on
its "skin". It must reply with a fast muscle reflex when it recognizes a pinprick. But it should ignore the
stimulus when it recognizes a blunt object. A continuous vm-adaptive recording of STQ(d) elapse times
by means of VTCP pulses is necessary to do this. The frequency of these time
counting impulses is
modulated in accordance with the STQ(v) parameters of the stimulus dynamics
(vm). These STQ(v)
parameters are required for the recording of the STQ(d) elapse times Td (1,2,3...) from the signal
amplitude at the current stimulus intensity. The difference between "stimulation intensity" and
"stimulation dynamics" is easily seen in this example. A stimulus can even show a different intensity if
no temporal-spatial change takes place between signal source and receptor. A
needle in the skin can
cause a different sensory pattern even when its position is not changing if,
for example, it is heated. This
sensory pattern is determined by the signal amplitude, and consequently by the AP frequency and by the
STQ(d) quanta. As long as the needle persists in an invariant position, the AP propagation velocity is
constant, because the membrane charging time is constant too. During the prick into the skin, there is a
"dynamic stimulation", and the STQ(d) quantization of the signal amplitude
is carried out in a manner
that depends on the pricking speed vm. It should be noted that two temporally
displaced signal
amplitudes (at the inner and outer membrane surface) always exist during this dynamic process. The
STQ(v) parameters are derived from this. The AP propagation velocities and the acquired STQ(d) time
patterns are adapted accordingly ("temporal auto-adaptation").
The STQ(d) time patterns Td(1,2,3,4,.....), measured
adaptively according to the vm, are constantly
compared to and analysed together with the previously measured and stored STQ(d)
time patterns
Td'(1,2,3...). This time comparation process occurs continuously in the so-called synapses, which are
the junctions to axional endings of other neurons. The probability density values that are produced at the
synapses, and which are used to represent the convergence of both regression curves, are communicated
for further processing to peripheral neural systems, or to muscle fibres in
order to trigger motoric reflex.
Fig. 4b shows the vm-dependent propagation of an AP from a sensory neuron
(receptor) 20 along an
axon to a synapsis, where a comparison of acquired time sequences takes place through molecular
"covariance analysis". This receptor functions like a "pressure sensor". If a needle 21 with a certain
dynamics impinges on the outer side of the cell membrane, then this stimulation causes triggering of
AP's
23 as described in Fig. 4a. The AP's propagate in the axon 22 with
a STQ(v)-dependent speed vap.
The sequence (a'.....v') represents the signal amplitude values that are produced
by the pinprick. The
sequence begins with the phase transition at the first threshold value P1, continues over P2, P3, P4 (at
which point the stimulus maximum is attained), and finally to the phase transitions
through P3 and P2.
The intensity zones for stimulus perception are designated with Z1, Z2, Z3 and
Z4. The periods t(P1),
t(P2), t(P3), t(P4)......, and the magnitudes of the AP's serve to identify
the particular threshold in which
the stimulation intensity is currently to be found. Their temporal sequence
is therefore a type of "code".
AP's are not time counting pulses. Besides their coding function, they also serve as (indirect) activating
and deactivating pulses for the recording of STQ(d) elapse times. The actual vm-dependent
measurement of the STQ elapse times Td(1), Td(2), Td(3), Tw(4) and Td(4)... (see Fig. 2c), as well as
the comparison of these with previously recorded elapse times, takes place in
the synapse 24.
At the presynaptic terminal of the axons, the AP's 23 arrive with variable
velocities vm(n...), according to
the dynamics of the needle prick as well as the measured STQ(v) parameters. This variable arrival
velocity at the synapses is the key to producing the adaptive time counting impulses VTCP (see Fig. 3c)
with vm-modulated frequency ƒscan. The synapse is separated from the postsynaptic membrane by the
"synaptic cleft", and the postsynaptic membrane, for its part, is interconnected with other neurons; for
instance, to a "motorneuron" 25. This neuron generates a so-called
"excitatory postsynaptic potential"
(ESPS) 27 that is approximately proportional to the convergence probability
g. If this EPSP (or,
equivalently, the probability density g) exceeds a certain threshold value,
then, in turn, an action
potential AP 28 is triggered. This AP is communicated via motoaxon 26 to the "neuromuscular
junction", at which a muscle reflex is triggered. The incoming AP sequences 23 generate the release of
particular amounts of molecular transmitter substance from their repositories
- tiny spherical structures
in the synapse, termed "vesicles". In principle, a synapse is a complex
programmable timedata processor
and analyzer that empties the contents of a vesicle into the presynaptic cleft when the recurrence of any
prior recorded synaptic structure is confirmed within a newly recorded key sequence. The synaptic
structures and vesicle motions are generated by the dynamics (vap) of the AP
ionic flux, as well as by
its frequency. AP influx velocities v(ap) correspond to the STQ(v) elapse times,
and AP frequencies
correspond to the STQ(d) elapse times. The transmitter substance is reabsorbed by the synapse, and
reused later, whereby the cycle continues uninterrupted.
We now present a detailed description of Fig. 4b (referring also to Figs. 4e
and 4f). The ionic influx of
the initial incoming AP 23 (a') activates the spherical structures (vesicles) containing the ACh transmitter
molecules. These molecules are released in the form of a "packet".
The duration of this ACh packaging
depends on the dynamics (represented by the velocity v(ap)) of the AP ionic
influx at the presynaptic
terminal, and therefore on the stimulus dynamics (represented by vm) at the receptor 20. Each
subsequent incoming AP, namely b', c'..., in turn causes neurotransmitter substances in the vesicle to be
released toward the synaptic cleft. Each of the following are elapse time counting and covariance
analyzing characteristics:: the duration of accumulation of neurotransmitter
substance T(t); the velocities
v(t) with which the neurotransmitter substances move in the direction of the
synaptic cleft; the effects
induced by the neurotransmitter substances at the synaptic lattice at the synaptic cleft; the duration of
pore opening; and so on. By means of AP's acting on synaptic structures, not only are the actual time
counting frequencies ƒscan generated (to be used in vm-dependent measurement of STQ(d) elapse times
as described in Fig. 2c), but also time patterns are stored and analysed.
If the pattern of a current temporal sequence is recognised by the synapse as
matching an existing stored
pattern, a pore opens at the synaptic lattice, and all of the neurotransmitter content of a vesicle is
released into the subsynaptic cleft. The released transmitter molecules (mostly ACh) combine at the
other side of the cleft with specific receptor molecules of the sub-synaptic membrane of the coupled
neuron. Thus, a postsynaptic potential (EPSP) is generated, which then propagates
to other synapses,
dendrites, or to a "neuromuscular junction". If the EPSP exceeds a
certain amplitude, then it triggers an
action potential (AP) of the described type, which then triggers, for example, a muscle reflex. If the
potential does not reach this threshold, then the EPSP propagates in the same manner as an EP (i.e. in
an electrotonic manner); an AP is not produced in this case.
Of special significance is the summing property of the subsynaptic membrane.
This characteristic,
termed "temporal facility", results in the summation of amplitudes
of the generated EPSP's, if they arrive
in short sequences within certain time intervals. Each release of neurotransmitter molecules into the
synaptic cleft designates an increased probability density occurring during the comparison of
instantaneous vm-proportionally acquired STQ time patterns to prior vm-proportionally recorded STQ-
time patterns. Increased probability density causes a higher frequency of transmitter substance release
and therefore a higher summation rate of the EPSP's, which in turn produces,
at a significantly increased
rate, postsynaptic action potentials (AP). Therefore, a postsynaptic AP is effectively
a confirmation
signal that flags the fact that isomorphism between a previously and currently recorded time data pattern
has been recognized. On the basis of this time pattern comparison, the object that caused the perception
at the receptor cell is thereby identified as "needle"; and the command to "trigger a muscle reflex" is
conveyed
to the corresponding muscle fibres.
Parallel and more exact recognition processes are executed by the central nervous
system CNS (i.e. the
brain). From the sensitive skin-receptor neuron 20, a further axonal branching 29 is connected via a
synapse 30 to a "CNS neuron". In contrast to the "motorneuron" which actuates the motoric activity of
the organism directly, a CNS neuron serves for the conscious recognition of a receptoric stimulation
sequence. An AP 31, produced at the postsynaptic cell membrane 30, can spread
out along dendrites in
the axon 30a, as well as to several other CNS neurons; or, alternatively,
indirectly via CNS neurons to
a motorneuron, then on to a neuromuscular junction.
The parameters controlling the recording of STQ time quanta in the synapses
25 and 30 can differ with
different synaptic structures. (Indeed, the synaptic structures themselves are generated by continuous
"learning" processes). This explains how it is possible for a needle
prick to be registered by the brain,
while eliciting no muscular response; or how a fast muscle reflex can be produced
while a cause is
hardly perceived by the brain. The first case shows a conscious reflex, the other case an instinctive
reflex. The former occurs when the CNS synapse 30 cannot find enough isomorphic structures (in
contrast to the synapse 25), transmitter molecules are not released with sufficient frequency, and
subsequently
no postsynaptic AP 31 and no conscious recognition of the perceived stimulus
can take
place. Numerous functions of the central nervous system can be explained in
such a monistic way; as
well as phenomena such as "consciousness" and "subconscious". Generally, auto-adaptive processes
are deeply interlaced in organisms, and are therefore extremely complex. In order to be capable of
distinguishing a needle prick from the pressure of a blunt eraser, essentially more time patterns are
necessary; in addition, more receptors and synapses must be involved in the
recognition process.
Fig. 4c illustrates the process by which moderate pressure from a blunt
object (e.g. a conical eraser on a
pin) is recognized, resulting in no muscle reflex. The blunt object 32 presses down with a certain
relative velocity vm onto a series of receptors in neural skin cells 33, 34, 35, 36 and 37. Several
sequences of AP's 39, 40, 41, 42 and 43 are produced after the individual adjacent receptors (see also
Fig. 4b) are stimulated. These action potentials propagate along the collateral
axons 38 with variable
periods t(P1,2,3..) and velocities vap(1..5), which result on the one hand from
the prevailing stimulation
intensity, and on the other hand from the respective stimulation dynamics. Since each receptor stimulus
generates a different pattern of STQ(v) and STQ(d) quanta, various AP sequences a'.....m' emerge from
each axon. All sequences taken together represent the pattern of STQ elapse times which characterises
the pressure of the eraser on the skin. These variable AP ionic fluxes reach
the synapses 44, 45, 46, 47
and 48, which are interconnected via the synaptic cleft with the motoneuron
49. As soon as the currently
acquired STQ time data pattern shows a similarity to a prior recorded STQ time data pattern, each
individual synapse releases the contents of a vesicle into the subsynaptic cleft. Simultaneously, this
produces an EPSP at the subsynaptic membrane of the neuron. These EPSP potentials are mostly below
the threshold. The required threshold value for the release of an AP is reached only when a number of
EPSP's
are summed. This happens only when a so-called "temporal facilitation"
of such potentials
occurs, as described in the previous paragraph.
In the model shown, the individual EPSP's 50, 51, 52, 53 and 54
effect this summing property of the
subsynaptic membrane. These potentials correspond to receptor-specific probability density parameters
g1, g2, g3, g4 and g5, that represent the degree of isomorphity of time patterns. Simultaneous
neurotransmitter
release in several synapses, for example in 45 and 47, causes
particular EPSP's to be
summed to a total potential 56, which represents the sum of the particular
probability densities
G = g1+g3. This property of the neurons (i.e. the summing of spatially separated subliminal EPSP's
when release of neurotransmitter substance appears simultaneously at a number of parallel synapses on
the same subsynaptic membrane) is termed "spatial facilitation".
In the described model case, the summed EPSP 56 does not, however, reach
the marked threshold (gt),
and therefore no AP is produced. Instead, the EPSP propagates in the sub-synaptic
membrane region 49
of the neuron, or in the following motoaxon 55, respectively, as a passive electrotonic potential (EP).
Such an EP attenuates (in contrast to a self-generating active AP) a few millimetres along the axon, and
therefore has no activating influence on the neuromuscular junction, and consequently no activating
influence on the muscle. The stimulation of the skin by pressing with the eraser
is therefore not
sufficient to evoke a muscle reflex.
It would be a different occurance if the eraser would break off and the empty
pin meet the skin receptors
with full force. In this case, neurotransmitter substances would be released simultaneously in all five
synapses 50, 51, 52, 53 and 54, because the acquired STQ time patterns Td(1,2,3..), with very high
probability, would be similar to those STQ time patterns Td'(1,2,3... ) already
stored in the synaptic
structures that pertain to the event "needle prick". The EPSP's would
be summed, because of their
temporal and spatial "facilitation", to a supraliminal EPSP 56, and a postsynaptic AP would be
produced that propagates along the motoaxon 55 in a self-regenerating manner (without temporal and
spatial attenuation) up to the muscle, producing a muscle reflex.
As in Fig. 4b, in the present example a recognition process takes place in the
central nervous system
(CNS) that proceeds in parallel. From the skin receptor cells 33, 34, 35,
36 and 37, collateral axonal
branches extend to CNS synapses that are connected to other neurons 58. Such branches are termed
"divergences". The subdivision of axons into collateral branches in different neural CNS districts, and
the temporal and spatial combination of many postsynaptic EPSP's, allows conscious recognition of
complex perceptions in the brain (for example, the fact of an eraser pressing
onto the skin). Since this
recognition has to take place independent of the production of a muscle reflex,
the sum of individual
EPSP's must be supraliminal in the CNS. Otherwise, no postsynaptic AP - i.e. no signal of
confirmation - can be produced.
As an essential prerequisite for this, it is necessary that auto-adaptive processes
have already occurred
which have formed certain pre-synaptic and sub-synaptic STQ time structures
in the parallel synapses
58. These structures hold information (time sequences; i.e. patterns)
pertaining to similar sensory
experiences (e.g. "objects impinging on the skin" - amongst these, a conical eraser). Obviously the
threshold for causing an AP in the postsynaptic membrane structure of the ZNS Neurons 58 (and
therefore also in the brain) has to be lower than in the motoneuron membrane 49 described previously.
Therefore also the sum of these EPSP's must be larger than the sum of the EPSP's g1, g2, g3, g4 and
g5. Isomorphisms of STQ time patterns in the CNS synapses of the brain have
to be more precisely
marked out than those in the synapses of motoneurons, which are only responsible
for muscle reflexes.
The structure of the CNS synapses must be able to discern finer information,
so it must be more subtle.
The production of a sub-synaptic AP represents a confirmation of the fact that
a currently acquired
Td(1,2,3...) time pattern is virtually isomorphic to a prior recorded reference time pattern Td'(1,2,3...),
which,
for example, arose from a former sensory experience with an eraser impinging
at a certain
location on the skin. If such a former experience has not taken place, the consciousness
has no physical
basis for the recognition, since the basis for time pattern comparison is missing. In such a case, therefore,
a learning process would first have to occur. Most of the time, however, sensory experiences of a visual,
acoustic or other type, arising from a variety of receptor stimulation events, are co-ordinated with the
pressure sensing experience.
This explains why CNS structures are extremely intensively interlaced. CNS neurons,
as well as moto-
neurons, have up to 5000 coupled synapses, which are interconnected in a multifarious manner with
receptor neurons and axonal branches. There are complex time data patterns for lower and higher task
sites, which are structured in a hierarchical manner. We have already described simple Td(1,2,3....) and
Td'(1,2,3...) analysis operations. Blood circulation,
respiration, co-ordination of muscle systems, growth,
seeing, hearing, speaking, smelling, and so on, necessitate an extremely large
number of synaptic
recorded "landscapes" of the organism's STQ time patterns, produced by a variety of receptors; and
which continually have to be analysed for isomorphism with time patterns currently being recorded.
Accordingly, temporal and motoric auto-adaptation occurs in deeper and higher hierarchies and at
various levels.
Fig. 4d illustrate the counterpart to the EPSP (Excitatory Postsynaptic
Potential): the "Inhibitory
Postsynaptic Potential " , or IPSP. As seen in the figure, the IPSP potentials 61, 62, 63, 64 and 65 at
the subsynaptic membrane 60 are negative compared to the corresponding EPSP's. IPSP's are produced
by a considerable proportion of the synapses to effect pre-synaptic inhibition instead of activation. The
example here shows an IPSP packet 67 propagating from the motoaxon 66
to a neuromuscular junction
(or muscle fibre, respectively) which prevents this muscle from being activated
- even if a supraliminal
EPSP were to reach the same muscle fibre at the same time via a parallel motoaxon.
Positive EPSP's ion fluxes and negative IPSP's ion fluxes counterbalance each
other. The main function
of the IPSP's is to enable co-ordinated and homogeneous changes of state in the organism, e.g. to enable
exact timing of motion sequences. In order to ensure, for example, a constant
arm swing, it is necessary
to activate the bicep muscles, which then flex the elbow with the aid of EPSP's;
but to inhibit the
antagonistic tricep muscles (which extend the elbow) with the aid of IPSP's. Antagonist muscles must be
inhibited via so-called "antagonistic motoneurons", while the other muscle is activated via "homonym
motoneurons". The complex synergism of excitatory (EPSP) synapses and inhibitory (IPSP) synapses
act like a feedback system (servoloop) and enables optimal timing and efficiency
in the organism. One
can compare this process with a servo-drive, or with power-steering, which ensures
correct co-ordination
and execution of current motion through data-supported operations and controls. If data are missing, the
servoloop collapses. Disturbances in a molecular biological servoloop that is supported by STQ time
data structures lead to tetanic twitches, arbitrary contractions, chaotic cramps
and so on.
From the point of view of cybernetics, each excitatory synapse generates a "motoric
impulse" (EPSP),
while each inhibitory synapse generates a "brake impulse" (IPSP).
The continued tuning of the
complicated servoloops, and the balance which results from continuous comparison
of prior sensory
experiences (the stored reference time patterns) with current sensory experiences (the time patterns
currently being recorded), creates "perfect timing" in the organism.
Fig. 4e shows the basic construction of a synapse. Axon 68 ends
at the pre-synaptic terminal 69, which
is also termed "bouton". The serial incoming AP's cause the vesicles
to be filled with neurotransmitter
molecules. When the filling process is finished, the vesicles begin to move
in the direction of the pre-
synaptic lattice 71. If a currently acquired time pattern is approximately isomorphic to an existing time
pattern (see also Fig. 4b), then a small canal opens at an attachment site on the lattice, which releases
the entire contents of the vesicle into the narrow synaptic cleft 72. This process is termed "exocytosis".
The
sub-synaptic neural membrane 73 supports specific molecular receptors
73a, to which the released
transmitter molecules bind themselves.
For a certain period, a pore opens, through which the transmitter substance
diffuses. The conductivity of
the postsynaptic membrane increases and the EPSP (following postsynaptic depolarisation) is triggered.
The duration of opening of the pores and the recognition of complementary receptors by the molecules
are likewise determined by auto-adaptive processes and evaluation of STQ time
pattern structures.
However, these molecular processes represent deeper sub-phenomena in comparison
to synaptic
processes. Structures for temporal and motoric auto-adaptation, which depend on quantization of STQ-
elapse times, also exist at the molecular and atomic levels.
Fig. 4f shows the filling of a vesicle 70 with neurotransmitting
substances, and its subsequent motion
towards a pre-synaptic dense projection at the lattice 71. The start of the filling process 74 can be seen as
the activation of a stopwatch. The rate v(t) of the filling is proportional to the dynamics of the AP ionic
flux into the synapse. The periods T(t...) of the filling follow the periods t(P1,P2,...) of the arriving AP's;
these times, therefore, represent vm-adaptive quantized STQ(d) elapse times Td(1,2,3...). The direction
of filling is shown at 75. The direction of motion of a vesicle is shown at 76. If the current velocity v(t),
the duration of the vesicle packaging T(t), the quantity of transmitter molecules,
the current vesicle
motion and other currently significant STQ parameters have characteristics which
correlate to an
existing synaptic STQ structure, then a filled vesicle binds itself onto an "attachment site" 77 at the
lattice. Ca++ ions flow into the synapse, a pore at the para-crystalline vesicle lattice opens, and the entire
molecular neurotransmitter content is released into the synaptic cleft 72. At the postsynaptic membrane
of the target neuron, these molecules are fused with specific receptor molecules.
Such receptors have
verification tasks. They prevent foreign transmitter substances (that originate
from other synapses) from
producing wrong ESPS's at this neuron.
To complete the discussion of Fig. 4, we relate the descriptions of Figs. 4a,
4b, 4e and 4f to the STQ-
configurations of Figs. 3a - g. For argument's sake, we assume once again that a pinprick impinges onto
a receptor cell (see also Fig. 4b).
The IP sequences shown in Fig. 3a correspond to the AP's 23 which are
produced by stimulating a
receptor cell 20 with a needle 21. Their periods t(P1), t(P2),... serve to classify the respective zones of
stimulation intensity (P1, P2...) or perception intensity (Z1, Z2... ). Each AP 23, arriving into a synapse
69, activates the adaptive quantization of STQ(d) elapse times, depending on the velocity vap of the
propagation of the AP along the axon. Elapse timing with modulated time base is triggered as soon as a
vesicle begins to fill. Finished filling (packaging) signifies "elapse
timing stop, STQ(d)- quantum
recorded". The elapse times Td(1),
Td(2),
Td(3),
Td(4).... thus recorded
generate the significant synaptic
structures. Invariant time counting pulses ITCP (see Fig. 3b) with frequency fscan correspond to
constant axonal AP propagation with velocity vap, if no dynamic stimulus appears at the skin receptor
cell (for example, if a needle remains in a fixed position and generates a constant stimulation intensity).
In this case, the receptor membrane senses no relative speed vm; the AP's propagate
with constant
velocity vap along the axon 22; and the synapse quantizes the STQ(d) elapse
times with invariant time
counting frequency fscan.
Time counting pulses VTCP (see Fig. 3c) with variable frequency ƒscan are then
applied, if dynamic
stimulation affects the receptor. The AP's propagate along the axon with STQ(v)-dependent velocities
vap(n...), modulated by the variable dynamics vm(n...) which are measured as
an STQ(v) parameter by
the membrane. Adaptive alteration of all of the following processes occurs in
a similar manner: the
variation of time counting periods t(P1... .n) corresponding to the points 2.1, 3.1, 4.1 in Fig. 3c; the
velocities v(t....) of AP ionic flux into the synapse; the vesicle filling times T(t...); the amounts of
transmitter molecules contained in the vesicles; the motion of these molecules in the direction of the
vesicle lattice; the structure of this lattice; and many other parameters of
the presynaptic and subsynaptic
structures.
A synapse has features that enable the conversion of the AP influx dynamics
into vap-proportional
molecular changes of states. This is like the variable VTCP time counting pulses seen in Fig. 3c. The
process can be compared with variable water pressure driving a turbine, through which a generator
produces variable frequencies depending on pressure and water speed: higher
water pressure is akin to
higher stimulation dynamics vm at the receptor, higher AP propagation velocity
vap along the axon, and
higher VTCP time pulse frequency ƒscan in the synapse (which in turn affects not only the rate v(t) with
which vesicles are filled, but also many other synaptic parameters). According to these processes, the
STQ(d) time sequence Td(1, 2, 3, 4...) is recorded in the synapse with vm-modulated time counting
frequencies ƒscan(1,2,3...); as a consequence, the physical structure of the
synapse is determined by this
time sequence.
Fig. 3d shows a currently acquired time data sequence 32 30 22 23 20 that is
equivalent to the recorded
time pattern Td(1,2,3..), and which leaves a specific molecular biological track in the synapse 24. The
prior acquired time data sequence 30 29 22 24 19 in Fig. 3e corresponds to the synaptic structure that
has been "engraved" through frequent repetition of particular stimulation
events and time patterns
Td'(1,2,3...).The manifested synaptic
Td' structure can be considered also as a bootstrap
sequence that
was generated by continuous learning processes and perception experiences, and which, for example,
serves as a reference pattern for the event "pinprick". If a newly acquired Td bootstrap sequence - which
is given by the current properties of the vesicle filling, as well as other significant time dependent
parameters - approximately keeps step with this existing Td' (bootstrap sequence (or with a part of it ),
then "covariance" is acknowledged in the synaptic structure. This opens a vesicle attachment site at
the synaptic lattice and results in the release of all transmitter molecules that are contained in a
vesicle, whereupon an EPSP is generated at the sub-synaptic membrane 25. The potential of an EPSP
corresponds to the probability density parameters shown in Fig. 3f, which are significant for the
currently evaluated covariance. If such "probability density parameters" sum within a certain time
interval to a certain threshold potential 27, an AP 26 is produced. This AP serves as confirmation of
the event "pin recognized", and produces a muscle reflex.
The comparison of the current elapse time pattern with prior recorded elapse
time patterns, as shown in
Fig. 3c, takes place continuously in the synapses. Each recognized covariance of a new time sequence,
that is recorded by "temporal auto-adaptation", sets a type of "servoloop
mechanism" in motion. It
initiates a process that we term "motoric auto-adaptation", and which
can be understood as the actual
"motor" in biological chemical organisms, or life forms, respectively. Structures of temporal and
motoric auto-adaptation, which are based on STQ quantization, exist also at the lowest molecular level.
Without elapse time-supported servoloops, co-ordinated change in biological systems would be
impossible. This applies especially to the motion of proteins; to the recognition
and replication of the
genetic code; and to other basic life processes. The creation of higher biological/chemical
order and
complex systems such as synapses or neurons presupposes the existence of an STQ quantization
molecular sub-structure, from which simple acknowledgement and self-organization processes at a
lower level derive. Indeed, there are innumerable hierarchies of auto-adaptive phenomena on various
levels. Simple phenomena on a molecular level also include: fusion of receptor
molecules; the formation
of pores, ion canals and sub-axonal transportation structures (microtubules);
and the formation
of new
synapses and axonal branchings.
By this token, recognition of stimulation signal sequences by synaptic time
pattern comparison (as an
involuntary reflex or as a conscious perception), as discussed in the description of Figs. 4a - c, is an
STQ-epiphenomenon. Each such auto-adaptive STQ-epiphenomenon, for its part,
is superimposed from
STQ-epiphenomena of higher rankings; for example, the analysis of complex "time
landscapes" in order
to find isomorphism. STQ-epiphenoma such as regulation of blood circulation, body temperature,
respiration, the metabolism, seeing, hearing, speaking, smell, the co-ordination of motion, and so on, are
for their parts superimposed from STQ-scenarios of higher complexity, including consciousness,
thought, free will, conscious action, as well as an organism's sensation of
time. In all these cases, the
central nervous system looks after convergent time patterns that are placed
like pieces of a jigsaw puzzle
into an integrated total sensory scenario.
If, in any hierarchy, within a certain "latency time" (i.e. time limit)
and despite intensive "searching", no
time subpattern covariant with the STQ time pattern can be found, then the organism displays chaotic
behaviour. This behaviour restricts itself to that synaptic part in which the
non-convergence has
appeared. As soon as a covariant time pattern is found, the co-ordinated process
of temporal and motoric
auto-adaptation (and auto-emulation) resumes. (This can be likened to servo-steering that has collapsed
for a short time.) However, the "chaotic behaviour" is itself quantized as an STQ time pattern, and is
recorded by the affected synapses in such a manner that no neurotransmitter substance release occurs
despite arriving AP's. Via subaxonal transportation structures (i.e. the microtubules)
such information
streams back borne on transmitter molecules which travel in the inverse direction
along the axon.
Microtubules are used to generate new synapses and synaptic connections at the neurons and neural
networks in which a collapse of an auto-adaptation process has occurred. The production of new
synapses proceeds to the generation of dendrites; i.e., axonal branches that carry processing information
from neurons. In this way the auto-adaptive neural feedback mechanism regenerates itself, and the STQ
time pattern that was acquired during the short termed "chaotic behaviour"
becomes a new reference
basis for the recognition of future events. Thus, the CNS learns to record new
events and experiences;
and learns to evaluate time patterns which were unknown previously.
Fig. 5 shows a configuration in which the described invented method is
applied to generate an
autonomous self-organizing mechanism, in particular a robot, in which the STQ
quanta are acquired
by means of mechanistic sensor technology and electronic circuits. In contrast
to Figs. 4a - f, in the
particular case shown here, nearly exclusive STQ(i) elapse times together with STQ(v) elapse times
(which are required for the measurement of the relative instantaneous speed vm) are quantized. The time
data streams, designated as Tw, are obtained from these vm-adaptive STQ(i) elapse time measurements.
It would nevertheless be advantageous to acquire also STQ(d) quanta, which can
serve to verify the
recorded time data stream Tw.
In contrast to molecular/biological organisms, in mechanistic systems it is not possible to place
a comparably large number of sensors adjacent to one other on narrow sites. It is therefore necessary to
acquire as many STQ elapse times as possible from the available mechanistic sensor technology, in
order to attain a sufficiently large reference base for the subsequent statistical analysis. It is also worth
reiterating that, as described in Fig. 3a, in multiple STQ(i) quantization, parallel and simultaneous time
data are produced, so that this data must also be processed in a parallel manner.
This figure shows a block diagram for a mobile autonomous robot that has the ability to reproduce
motion sequences in an auto-adaptive manner, and to optimize the timing of its own motion sequences
by continuous scanning and recognition of the physical surroundings. The robotic
system is equipped
with equivalent adjacent sensors 79 and 80, which produce analog
output signals, and that are inter-
connected with threshold detectors 81a,b,c,d,e... and 87a,b,c,d,e... . When sensor 79 (the "V-sensor")
moves along the corresponding external signal source 78a in the designated direction, its signal
amplitude first breaks through the lowest potential P1, which is determined by the threshold detector 81a
(see
description of Fig. 2b). The Flip-flop IC 82a (output set to = H ) is
thereby triggered. (A Schmitt-
trigger IC and a monoflop IC should be preadded in order to generate short pulses
at each phase
transition.) The subsequent resettable precision integrator IC (1) 83a provides a continually ascending
analog output signal which modulates the output frequency ƒ of the programmable oscillator IC (VCO)
84a. The frequency ƒ is communicated to the input of a digital TICM (a multiple time counting and
storing IC 86 (C1)) and whereby the current vm-adaptive time counting
frequency ƒscan(1) (see also
Figs. 3b,c) is produced. The integrator IC (1) 83a therefore carries
out the STQ(v) quantization. It
acquires the elapse time Tv(1) in the form of a potential increase, which is then converted by the
VCO(1) 84a into a time counting frequency ƒscan(1), and which is inversely proportional to the relative
velocities vm(n...) with which the robotic system is moving relative to the
spatial surroundings.
After the neighbouring sensor 80 (the "W-sensor") extends to
the perception field of the signal source
78a, its signal amplitude first breaks through the lowest potential P1,
which is determined by the
threshold detector 81a (see description of Fig. 2b). As a result, the rising edge of the subsequent Schmitt-
Trigger IC 88a produces an impulse in the subsequent IC 89a, whereby the STQ(i) quantization of
the vm-modulated elapse time Tw(1) is commenced in the TICM 86(C1). Because a reset pulse
simultaneously goes to the Flip Flop 82a, causing the analog level of the analog output of the
integrator(1) 83a to be held fixed, the pulse frequency ƒ(1) persists
as a momentary vm-dependent time
counting base ƒscan (1) at the output of TICM 86(C1), and remains unchanged
until the next STQ(v)-
parameter is quantized. This quantization happens whenever the signal amplitude of the sensor 79 drops
below the potential P1, which is determined by the threshold detector 81a (whence the flip flop IC 82a
is triggered by the falling signal edge), or when the sensor 79 expands into the perception field of
another
signal source 78b,c,d,e...
Simultaneously an impulse is again produced by IC's 87a, 88a and 89a,
which stops the measurement of
the elapse time Tw(1) in the TICM 86(C1), and stores the counted vm-modulated time pulses into the
time data memory (C1). In the memory area C1 are stored the Tw time data that refer to the lowest
potential P1; e.g. Tw(1), Tw(8), Tw(15) etc. Quantization of all STQ elapse times that refer to the
higher potentials P2, P3, P4, P5 etc. is handled in the same manner as for P1.
When the signal amplitude
from sensor 79 passes through the threshold potentials P2, P3, P4, P5....
(determined by detectors IC's
81b, c, d e...), the outputs of flip flops 82b,c,d,e... are sequentially triggered to = H and therefore the
subsequent integrator IC's 83b,c,d,e... generate continuously rising analog output levels, which serve to
steadily decrease the frequencies ƒscan (produced by the VCO's 84b,c,d,e ..) until the signal amplitudes
from sensor 80 goes through the higher threshold potentials P2, P3, P4,
P5..(determined by detector IC's
87b,c,d,e...), when sensor 80 expands to the perception area of
the signal source 78a.
As a result, the Schmitt trigger IC's 88b,c,d,e... are affected, and
the mono flop IC's 89b,c,d,e... produce
impulses that start the acquisition of vm-adaptive elapse time data Tw(1, 2, 3, 4...n) in the TICM 86
(C2,C2,C3, ...Cn). The recording of these data is carried out while the momentary vm-adaptive time
counting frequencies ƒscan(1,2,3,4,. ..n) are valid, because simultaneously
transmitted reset impulses to
the flip flop IC's 82b,c,d,e... hold the output levels at the integrator
IC's 83b,c,d,e... fixed, whereby the
current output frequencies ƒ(1,2,3,4 ...n) are programmed at the VCO's 84b,c,d,e... In the same manner
the consecutive quantization of further elapse times T( takes place when the sensors 79, 80 move along
subsequent signal sources 78b,c,d,e... All quantized STQ(i) time date
are filed in the TICM 86(C....n).
In the memory area C2 (see the corresponding Fig. 2b) are filed the elapse times
Tw(2), Tw(7), Tw(14)..
that refer to the perception area (potential) P2; in the memory area C3 are
filed the elapse times Tw(3),
Tw(6), Tw(13)... that refer to the next higher potential P3; in the memory area C4 are filed the elapse
times Tw(4), Tw(5), Tw(12)... that refer to the next higher potential P4...; and so on. The Tw-sequences
currently streaming into the TICM are generated by the current motion of the sensor-coupled
autonomous mechanism (e.g. "robot vehicle") along some track. In the
case shown, the positions of the
sensors are temporally deviating according to the positions of the external
signal sources (physical
surroundings).
In the case of absolute physical invariance between the mobile robot system
and the surroundings (so-
called synchronism), no STQ parameter and no Tw-sequence can be acquired. If such physical
invariance
is not occurring, then it is possible for the autonomous vehicle to recognize
its own motion
along the track by continuous comparison of currently acquired STQ elapse time
patterns Tw(1,2,3,4...n)
with prior recorded STQ elapse time patterns Tw'(nnnnn); and it is also possible for it to perfect the
recognized motions continually in an auto-adaptive manner. A prerequisite for this is that the vehicle is
equipped with a drive and brake system controlled by data which are calculated on the basis of conti-
nuous
statistical time data analyses.
(Compare also Figs. 3d and 3e): As soon as the regression curve of a currently
recorded time data
sequence Tw(1,2,3...) in the TICM 86 converges
to the regression curve of a previously recorded time
data sequence Tw'(nnnn) that was acquired through a prior similar motion on the same track, the drive
system 98 (as well as the brake system 99) is actuated by impulses 96, 97, which induce the autonomous
vehicle to perform its motion courses along the external signal sources 78a,b,c,d,e... in a manner such
that the current motion course is temporally and spatially approximately isomorphic
to that former
motion course from which the referential time data sequence Tw'(nnnn..) is derived.
For this purpose,
the TICM 86, in which the current time data are recorded, and the memory 92, in which the prior
recorded time data Tw'(nnnn..) are stored, are interconnected with a covariance analyser 90 and
discriminator logic 91, which verifies the elapse time data and tests them for plausibility. Invalid time
data are deleted and/or interpolated, whereby no breakdown of a data-supported
servoloop can occur.
Analyzer 90 and discriminator 91 continuously scan the memory
92 with very high frequency to find
approximately covariant time data patterns. Significant data sequences are transferred to the interpreter
93 that decides the respective probability density and the value of covariance. If significant covariance
exists, then the processor 94 calculates the appropriate actuating data for keeping an isomorphic course
of motion. These data reach the control module 95, where they are transformed
into impulses 96, 97 for
the drive and brake system 98, 99.
It is advantageous to extend this arrangement by incorporating energetic impulses
for a steering and
contra-steering system 100,101, 102, 103 that are based on the same functional principles as above, and
that are required to keep to the spatial motion course determined by the same Tw time patterns as above.
A prerequisite for perfect functioning of such an arrangement is the utilisation
of extremely fast pro-
cessors for the operation of the subsystems 90, 91, 93, 94, and 95.
The current motion course of the
autonomous vehicle can be made approximately isomorphic to the referential motion course only if the
recognition of the significant Tw'(nnnn) sequences (i.e. the reference data), the recording and analysis of
the current Tw sequences (actual data), the computation of the control parameters and the application of
the energy impulses 96, 97 all occur nearly in real time. The vehicle
would then display behaviour
similar to a "power servoloop" of the known type. This similarity
can be confirmed simply by increasing
or decreasing the base frequency fn of the clock 85, whereby the entire temporal course in all motion
phases is accelerated or decelerated, in an absolutely synchronous manner.
Each external intervention that tries to alter or disturb the motion course
is counteracted automatically
by
the drive mechanism of the autonomous vehicle. Therefore, an autonomous mechanism
working
along these principles is comparable with a "live organism". Since
in the system components 90, 91, 93,
94 and 95 a tendency is programmed that continuously optimizes the analysis and interpretation of
acquired time parameters (for example, to allow only "authentic data"; i.e. those Tw'(nnnn) time data
that pertain to the shortest and most efficient path to follow). In such a mechanism, there would then
exist the tendency not only for temporal and motoric auto-adaptation, but also
for optimization. (This is
inherent in molecular/biological structures of organisms (see description to
Figs. 4a - f).) The system is
also capable of determining priorities, as well as of deciding in favour of Tw time data sequences that
correspond to some other regression curve, if an irregular track deviation that cannot be stabilized by the
control module 95 is recognized; whereupon, for example, the vehicle emulates a new motion course
and a new speed time curve (timing). The memory of the TICM 86 can store any alternative motion
scenario in the form of Tw
time data patterns, which are accessed if a certain
course deviation makes it
necessary to do so. In this way, crash situations are recognized as soon as
the danger becomes
apparent,
and can be avoided, since the vehicle is ready to react in an autonomous manner.
The system goes out of control ("chaotic condition") only when no
segmental regression curve derived
from prior recorded Tw-sequences can been found that converges to a segmental regression curve
derived from currently recorded Tw-sequences. The
author terms this process "motoric auto-adaptation",
or "auto-emulation". In order to be able to identify temporal-spatial
deviations of the physical
surroundings from the subjective view of the autonomous system, it doesn't suffice in most cases just to
scan external structures, land marks and light conditions by means of optical or photoelectric sensors
passively. It is usually necessary to sense also height deviations by means of inclination sensors; uneven
surfaces by means of pressure detectors or acceleration sensors; stationary acoustic sources by means
of microphones; gradients by means of magnet field sensors; and positions by means of GPS; in order to
acquire sufficient STQ parameters for a reference base.
All recorded Tw'(nnnn..) time data streams are stored
in the memory of the TICM. One can conclude
from this that the adaptability and self-organisation capability of an organism (or autonomous auto-
adaptable
mechanism) increases in proportion to the quantity of all available sensors,
or, respectively, to
the number of STQ parameters that are available for the auto-adaptation process.
Another important
point is that in an autonomous system, there can be no timing without an accompanying time recording
(=STQ quantization). Auto-adaptive processes and mechanisms of the described type will be
indispensable for many future tasks in the high technology sector; for example, in the development of
autonomous robot systems.
An example of such a task is the following. An automobile that must find its
way through traffic
autonomously, safely and efficiently, must be capable of holding lateral and frontal distance margins, as
well as speed courses, fixed. This automobile, moreover, would have to be able to execute autonomous
overtaking procedures, and to recognize dangerous situations in advance and avoid them. This is only
possible if the onboard computer of the vehicle is interconnected with a multiplicity
of different sensors
that record a diverse variety of signal sources; and if the vehicle is equipped
with extremely fast and
efficient hardware and software that can process the STQ time data required for auto-adaptation,
approximately in real time. Future types of microprocessors could be enhanced with hardware structures
that perform the functions described above.
Fig. 6a shows a configuration of a simple embodiment of an aspect of the
invention, in which the
STQ(v), STQ(i), and STQ(d) quantization methods introduced in Figs. 2a - c are applied to the
recognition of spatial profiles or structures. In the application shown here, a robot arm, on which two
adjacent metal sensors 104, 105 are installed at a distance b apart, must be capable of distinguishing the
profile of the metal rail 106 while moving at various speeds along any of the rails 106, 107, 108.
If the sensor head is moving at height h in the designated direction, then the
v sensor 104 (S2), and
then
the W-sensor 105 (S1) in turn, approach the low sensitivity area designated here as perception intensity
zone 1. The lowest threshold value P1 is passed through by the signal amplitude, and the acquisition
logic 109 - mainly consisting of elements 81, 82, 83, 84, 85, 86, 87, 88, and 89 (shown in Fig. 5) -
begins to acquire v- modulated STQ(i), STQ(d) time sequences Tw(1,2,3...n) and Td(1,2,3 ...n), which
are stored in the TICM memory (A) 110. The same time data acquisition process
recurs when sensors
104, 105 meet the next higher perception area zones 2 and 3, and when
the signal amplitudes break
through the potentials P2 and P3, which are preset in the threshold value detectors.
Within the analyzer 112, in order to identify the metal rails 106 unequivocally (which would thereby
show the characteristic profile), Tw and Td time data streams flowing into the memory 110 must be
continually compared with the particular significant Tw',
Td' time data pattern (B) 111 that has been
preprogrammed as a "reference" pattern. Invalid or irregular time
data are recognized, then deleted or
corrected by the discriminator unit 113. This unit is programmed with the capability of improving the
allocation and processing of data automatically (e. g. verifying and checking the time data in an auto-
adaptive manner) as was already described with reference to Fig. 5. If a profile has been "recognized",
then the analyzer 112 transmits a confirmation signal to an actuator
unit of the robot, which sets a
mechanism in motion that lifts the identified metal rail up from the ground,
puts it on a conveyor belt,
and so on.
Figs. 6b - e show various diagrams and charts pertaining to Fig. 6a.
Fig. 6b shows a sensometric diagram of the scanned rail profile 106.
The measurement of its dimensions
d1...d7 is effected exclusively utilizing STQ quanta, i.e. within the time domain.
Three sensitivity zones
P1, P2 and P3 are preset (in the threshold detectors as well) for profile identification.
At the phase
transitions (iT)A, (iT)B, (iT)C, (iT)D, (iT)E, (iT)F, (iT)G and (iT)H, digital precision timers are activated
or stopped. Since the variable time counting frequency ƒscan with which these timers are counting
is automatically adapted (modulated) by the current scanning velocity vm (see also Figs. 3a - g and
Fig.
5), the actual dimensions d1...d7 correlate significantly with the Tw,
Td elapse times that are
already stored in the memory 110. As seen from the diagram, the distances AB-(d1)
and BC-(d2) are
obtained from STQ(d) elapse times; and the distances CD-(d3), DE-(d4), EF-(d5), as well as
BG-(d6) and AH-(d7), are obtained from STQ(i) elapse times. It is to be emphasized once again that
all of the (iT)n... are volatile phase transitions, and never "time points" in the classic physical under-
standing.
Fig. 6c shows vm diagrams of two motion courses of the sensors S1 and
S2 along the metal profile being
scanned. In the first case, the robot arm on which the two sensors are installed moves with an invariant
speed of 1000mm/s over the profile (dash dot graph 114). In the other case, the arm decelerates from a
speed of 1000mm/s at the first phase transition A to 690mm/s at the last phase transition H. The
deceleration is not linear, and is shown in the graph 115.
Fig. 6d shows a fictitious frequency and time data table for Fig. 6c, with a constant vm relative speed of
1000m/s at all phase passageways (iT) A...H. Consequently, the vm-modulated time counting frequency
ƒscan is 10 kHz during the entire scanning process. Because, in the case shown here, the recording of
STQ(v) elapse time takes place with a fixed clock timing base of 200cs/b, the scanning process leads to
vm-adapted STQ(d) sequences of 273cs, 738cs, 620cs and 262cs for distances AB, BC, CD, DE and EF
and to vm-adapted STQ(i) sequences of 1876cs and 2200cs for the distances BG and AH. The current
Tw-Td sequence, consisting of vm-adapted STQ(d) and STQ(i) elapse times, is compared in the analyzer
112 with the referential stored Tw'-Td' sequence 270, 270, 740, 620, 260, 1880, 2200, which serves as
the significant time pattern, for this metal profile, that is already stored in the memory 111. If the analyzer
decides that "covariance" is occurring, then a confirmation signal is transmitted to an actuator unit. The
analyzer consists of comparators and/or "fuzzy logic"-IC's which ignore scattering in the boundary values
(for example, decimal places are rounded up). Apart from these correction measures, tolerances,
plausibility
criteria and allocation criteria can also be programmed by software.
Fig. 6e shows the same frequency and time data chart as Fig. 6d, but
with variable scan speed course
(vm). The relative velocity of 1000mm/s at phase transition (iT)A decreases
to 690mm/s at the last phase
transition (iT)H. The vm deceleration is not linear. In accordance with the
graph 115, at the phase
transitions (iT) A,B,C,D,E,F,G,H, the momentary speeds (vm1,2,3...) are measured to be 1000, 985,
970, 930, 820, 750, 720 and 690mm/s. The vm-adaptive modulation of the time counting frequency
ƒscan(1,2,3...), described above, produces phase transition values of 10, 9.85, 9.70, 9.30, 8.20, 7.50,
7.20 and 6.90kHz, which are then used to quantize the STQ(i)- and STQ(d) elapse
times. Since the
STQ(v) quantizations also take place with the clock time base 200cs/b, the same
Tw-Td elapse time
sequence for the distances AB, BC, CD, DE, EF, BG and AH results, as seen in the chart of Fig. 6d. It
is obvious from this chart that the recognition of the metal profile is guaranteed, whether the vm speed
course is linear or not.
Figs. 7a - d show various configurations of sensors used in the quantization of STQ(v) elapse times, or
for the recording of the relative speed parameters (vm), respectively. The first three configurations show
sensor constellations for 2-dimensional records of external events. Fig. 7d shows a special configuration
applicable for random 3-dimensional records of the physical surroundings.
Fig. 7a shows a sensor constellation in which a bearing, carrying the
sensors S1 and S2 on the same
axis at a distance b apart, moves itself in the designated direction along an arbitrary track; or rotates
itself about a point in space that is equidistant from both S1 (V-sensor) and S2 (W-sensor). This sensor
system
has only one degree of freedom.
Fig. 7b shows a sensor constellation in which a supporting surface, carrying
on the same axis two V-
sensors
S2 and one W-sensor S1 equidistant from each other as shown, moves itself arbitrarily
in either
of the two opposite directions shown along some arbitrary track; or rotates
itself about a point in space
that is equidistant from the v-sensors S2. The sensor constellations shown in Figs. 7a and 7b are
sufficient for most robotic applications in traffic technology.
Fig. 7c shows a configuration with a number of equivalent v-sensors S2
arranged as segments around a
central w-sensor S1 on a circular supporting surface having radius b. In this
constellation, the supporting
surface can move itself in any direction in the plane on an arbitrary track;
or can rotate itself about a
point in space that is at any distance from the sensors. This sensor configuration therefore has 2 degrees
of freedom.
Fig. 7d shows a sensor configuration with a number of v-sensors S2 arranged
as segments on spherical
supporting surface, with radius b, around a central w-sensor S1. The sensor
constellation can move itself
to any arbitrary position in 3-dimensional space, or can rotate in each direction
around a solid spatial
point A at arbitrary distance from the sensors. This configuration has 3 degrees of freedom. The sensor
constellations shown in Figs. 7c and 7d come into consideration primarily for autonomous reconnaissance
robots or flight objects, wherein energetic impulses could be applied in an arbitrary direction (e.g. by
means
of auxiliary rockets).
Figs. 8a - f illustrate the configuration and functioning principles
of a further embodiment of the
invention presented herein, in which the STQ quantization methods described in Figs. 2a, b ,c are used
to create an autonomous auto-adaptive self-organising training robot for use in sports; a so-called
"electronic hare". This system has autonomous brake, drive and steering mechanisms, and an analyzer
that continuously compares the currently recorded vm-adaptive STQ(i)- and STQ(d)
time data patterns
Tw and Td(1,2,3...) with
previously recorded vm-adaptive STQ(i)- and STQ(d) time data patterns Tw'
and Td'(1,2,3....), respectively, which serve as reference patterns. It is thereby capable of reproducing
and optimizing a motion course that has been pre-trained by the user; of automatically finding ideal routes
and speeds; of keeping distances and times; of recognizing and warning of dangerous situations; and
of representing its own motion, as well as information about speed, lap times,
intermediate times,
start to finish times, and so on, on a monitor. It is, moreover, capable of
outputting these data in an
optical or acoustic manner.
Fig. 8a shows a training robot 116 in front of a long distance skier 117. The robot vehicle envisaged for
this application would be fitted with a ski undercarriage, allowing it to move with ease along snow-
covered
ground. It must be reasonably manoeuvrable in order to be able to match a human
skier travel-
ling in a long loop. The robot must be also able to create a new track on the
same route where the former
one has been covered by snow, and is therefore no longer visible. The training robot is especially suitable
as an aid for blind skiers. The autonomous vehicle recognizes skiing circumstances for the blind skier,
speaking out aloud hints, reports, warnings and so on by means of speech synthesis, which frees the
skier and allows them more enjoyment. The robot vehicle 116 has a large
number of sensors and
electronic components, in the manner introduced in Fig. 5. It performs the same
motion emulation,
auto-adaptation and auto-optimization, often carrying out several practical tasks simultaneously. It
acquires vm-adapted STQ(i)- and STQ(d) elapse time patterns from a multiplicity of sensors, compares
these patterns with corresponding reference time patterns, selects the significant time data, and analyses
and calculates parameters for the discrete energy impulses that manipulate the
drive, brake and steering
mechanisms. In the following, the essential components of the system, comprised
of any of three specific
types of sensors (optical, magnet field or GPS-positioning sensors) are described.
Figs. 8b-d illustrate the recording of STQ(v), STQ(i) and STQ(d) elapse times (pertaining to Fig. 8a)
with use of optical or acoustic sensors. The fundamental principles of its function have already been
detailed in the description of Figs. 2a - c and Fig. 5. In the present figures,
the training robot (the
"electronic hare") 116 is moving with variable speed in front
of a long-distance skier 117 in the loipe
118. Optical or acoustic signal sources 119, 120, 121, 122, 123, 124, 125, 126, 127, 128 and 129 have
been placed along the track in some arbitrary configuration, which are perceived by the corresponding
sensors 130a, b,...n. At each phase transition through the threshold zones P1, P2, P3, P4, P5 etc., the
designated STQ(v)- , STQ(i)- and STQ(d) elapse times are recorded. They generate
the current vm-
adaptive Tw'-Td'(1,2,...n)
time data pattern, which is stored in the TICM. It is not crucial that the signal
sources be fixed (e.g. they may be spotlights that illuminate the track for evening events). Signal sources
can also be produced through differences in light intensity, contrast or colour, occurring beside trees,
masts, buildings, slopes or significant land marks in daylight. Headlights could even be installed on the
training robot itself, whereby the optosensoric recording of the reflected light and the evaluation of the
light structures of the spatial surroundings may be used for recognizing its
own motion. The same set-up
may be used also with ultrasound sensors. On the other hand, acoustic signal
sources could equally well
be of natural origin; for example, the sounds of a brook running beside the
loipe, or a waterfall.
Generally, any volatile combination of light and shadow, or any noise source,
can be decisive in the
recognition of a certain object . The particular identity of the object may be determined by comparison
of vm-adaptively recorded STQ(i)- and STQ(d) elapse time patterns with the Tw'-Td'(1,2,3...n)
patterns,
which are stored in the TICM and which represent each individual external object.
In order to simplify
the present description and demonstration, it is assumed that the signal sources 119 ...129 in Fig. 8b are
lamps installed along the robot's route, making it possible for the robot to use the loipe at twilight or
in darkness. According to the primary domain of application of such a robot, the training robot 116 skis
with precision behind the skier 117 along the skier's track, with all
STQ time data vm-adaptively
recorded and stored in the TICM working memory (see also Fig. 5). The distance
between robot and
user is precisely controlled by a distance sensor. However, in order to be able to invoke the robot
vehicle's drive, brake and steering mechanism, STQ time data that could serve as reference data must
already have been loaded into the TICM prior to the journey. Therefore, as a first step, the acquired time
data are stored in the TICM reference memory; i.e., Tw-Td(1,2,3...)
are mapped to Tw'-Td'(1,2,3...)
initially. Subsequently, the emulation of the skier is repeated several times,
with increasing processing
speed as the robot learns more about the skier, and with variable speed and track courses; whereupon
more and more covariant Tw'-Td' time data patterns are contained in the reference data memory, which
the robot's discriminator and analyser can access (see also Fig. 5).
The interpretation and optimization program is put into action, which filters
through only "authentic"
Tw'-Td' time data that
are deemed to pertain to the best and most efficient trajectory of motion, and
which eliminates at the same time those data recognized as "irrelevant". This resembles a "learning
process" that the robot vehicle has to undertake until it can finally ski "autonomously"; i.e. relatively
freely, and in accordance with self-appropriated patterns and self-decided criterions, without any remote
control or regulation by a pre-programmed algorithm. Upon reaching this stage,
the training robot
functions as a "trainer" or "pilot" who has the task of
helping the user find ideal speeds, the best track and
optimal timing. This optimal information that is communicated to the user is only that which has been
learned by the robot itself.
The training robot continues to improve itself also during this "practical
work" (i.e. while helping
the user), in continually optimising and supplementing the STQ reference data
stored in the TICM. The
ability to identify and recognize trajectories of motion or external signal
courses and objects is always
upgradeable. It depends on the quantity and variety of sensors used, as well as on the memory capacity
of the TICM. Thus it is possible to induce the robot vehicle to recognize dangerous situations and to
warn the user acoustically or optically; and to keep distances and times more exactly. In the present
application, the vehicle performs automatic tracking and motion emulation along
a loipe, even if the
original track has been covered by snow and is no longer visible. Additionally,
the robot vehicle has a
monitor on which its own motion relative to its spatial surroundings can be visualised; as well as
electronic measures to output speeds, lap times, intermediate times, total times or other relevant data in
an optical or acoustic manner. An essential property of the robot vehicle shown here is that a simple
adjustment (increase or decrease) of the central clock frequency can synchronously accelerate or
decelerate the entire temporal course of all motion components (see also Fig.
5). For instance, this
property is necessary in order to adapt the speed of the training robot in all
sections according to the
physical fitness of the user. This can happen manually by a remote control device, or automatically; for
example, by a frequency or blood pressure data transponder.
Fig. 8e shows the recording of STQ(v) and STQ(d) elapse times for the robot in Fig. 8a in the case
when magnetic field sensors are installed. The signal source here is assumed
to be the earth's magnetic
field. In the example shown here, where the track forms a closed loop, the quantization
of STQ(i) elapse
times is inefficient, and therefore not undertaken. In the illustrated picture, the training robot ("hare") 116
is moving autonomously with variable speed in front of the long distance skier 117 along the loipe 118.
Various vehicle position readings are produced along the track, with variable gradients to the earth's
magnetic field 132. The magnitude of these gradients are acquired by the magnet field sensor 131.
In
this particular example, the magnitude follows a sinusoidal course. At each
phase transition to
the threshold zones P1, P2, P3, P4, P5, P6, and so on, the STQ(v) and STQ(d)
elapse times are vm-
adaptively recorded, which provides the current T( time data pattern that is stored in the TICM. The
additional quantization of STQ elapse times from magnetic field gradients helps to locate covariant
Tw'-Td' time patterns that are stored in the reference data memory. Consequently, the auto-adaptation
and recognition capability of the robot vehicle is improved. The more sensors involved in the auto-
adaptation process, the more "autonomous" is the described mechanism (see also Fig. 5). A self-
organizing, autonomous organism based on biological or chemical structures, as discussed in Figs.
4a -f , can be produced in this manner.
Fig. 8f shows the acquisition of circular position fields by means of
GPS sensors. These measurements
(in addition to those shown in Figs. 8b - e) are used to improve temporal and
motoric auto-adaptation
and make auto-covariance behaviour and motion emulation more precise. A prerequisite for successful
function is a GPS ("global positioning system") of high quality, which operates with extremely low
errors. Since a square wave signal is received in this case (therefore no subdivision into distinctive
sensitivity zones is possible) only STQ(v)- and STQ(i) elapse times, but no
STQ(d) elapse times can be
quantized - which, as we have seen, are measured between phase transitions from
lower to higher
potentials, and, respectively, vice versa. In Fig. 8f the training robot ("hare") 116 moves itself with
variable speed in front of the long distance skier 117 along the loipe 118, while circular GPS position
fields are produced along the track 134a,b,...,n, which are perceived by the GPS sensor 133 with high
precision in a reproducible manner. The radii of the position fields, as well
as the resolution between
adjacent fields, is adjustable. With each detection of a new position field,
a trigger signal is transmitted to
the STQ acquisition unit, which records the STQ(v) and STQ(i) elapse times, and which then stores
these currently vm-adaptive recorded time data sequences Tw(1,2,3....) into the TICM. The ability of the
robot to otimize auto-adaptation can be aided by counting and comparing the number of detected
position fields, or by assigning a specific data code to time data within each
crossed position field.
Fig. 9 is a schematic diagram showing how time data streams are produced.
Each transition of the
amplitude through sensitivity zones or threshold potentials in redundancy-poor autonomous self-
organized systems (such as mechanistic robot systems or organisms) leads to the quantization of elapse
times, if these systems are equipped with sensors (or receptors) that are adequate for the perception of
the external physical surroundings. It is asserted that the core technology shown in the diagram has
universal validity and applicability. The diagram shows a highly simplified
scheme for the technology,
which can be understood plainly by a non-expert.
The principles of this invention, as represented schematically in this diagram, are summarized below:
1) The "primary act" of every autonomous organism (including autonomous
self-organizing robots)
is
to "explore" their surroundings in order to ascertain whether temporal-spatial variation exists between
its own physical state and that of its surroundings. In order to do this, a multiplicity of sensors or
receptors 135a, b...,n are necessary.
2) Only when deviation exists, are the current STQ elapse times Tw(1,2...n) or Td(1,2...n) 137a,b,...,n
derived. The time counting frequency of their measurement depends on currently acquired STQ(v)-
quanta Tv(1,2,3....n) 136a,b,c,.....n, which represent parameters for the temporal-spatial variations
vm(1,2 ...n) between sensors 135a,b,....n and external signal sources. These deviations are identical to
the "relative speeds" vm(1,2,...n). Note: vm(1,2,...,n) are always acquired by means of an invariant time
counting frequency f, respectively, at an absolute time base.
3) The current STQ elapse times Tw(1,2..n) or Td(1,2..n) flow into so-called "information pots" 138 (or
time data memories) and form STQ time data patterns Tw'(1,2....n) or Td'(1,2...n), which serve as
reference patterns. If the organism finds sub-sequences of these Tw' or Td' patterns which in some
combination are covariant with a currently recorded Tw or Td pattern, then the organism interprets these
combinations of sub-sequences as an "isomorphous pattern" significant for defining the "actually
perceived event-pattern" (i.e. what actually is). In this way, the present event (represented by temporal
or spatial deviations between sensors and external signal sources) is "recognized".
4) An organism is equipped with "actuators" that influence a self-referential change - that is concurrently
being recognized - in an organism's temporal-spatial condition (e.g. its own motion) in such a manner,
that the change is highly covariant with a prior recorded pattern of change of a temporal-spatial
condition (it emulates the prior pattern). Because the shortest and most efficient time patterns have a
tendency to be of high priority while new Tw or Td sequences are being recorded in the memory,
organisms continuously try to optimize changes in temporal-spatial conditions. Both processes result
exclusively from comparison of quantized STQ elapse times and from recognition of isomorphous time
data patterns (see also Fig. 5), and are termed "auto-emulation" and "auto-optimization"; or,
equivalently, "autocovariance behaviour".
5) An essential consequence of these considerations is that a teleological tendency inheres in all
organisms of the described type, towards auto-adaptation and auto-optimization.This generates the
ability for self-organisation.
As seen from Fig. 10, both "time" and "velocity" unequivocally depend on the existence of sensors for
their perception. Actually, all time data and information flow from the "present" (the origin of the
recording) into the "past" (the verifiable existence). Indeed, time and velocity are not "sensed" as a
continuum, but in the form of quanta. In order to feel both physical quantities as a continuum, an
enormous capability for auto-adaptation and auto-emulation is required of an organism. It can be said
that the above fundamental principles are valid not only for robotics and biological units, but also for
molecular, atomic and subatomic structures. Also, these have to be "time sensing organisms"; otherwise
they can have no basis for existence. Consequently: time, space - every physical quantity - cannot
exist without subjective sensing of it. Viewed objectively, existing in the universe are only sensorial to-
gether with distinct sensitivity zones; and these form the basis for local subjective time sensing together
with a general universal tendency for auto-adaptation, auto-optimisation, and auto-emulation. This is a
fundamental teleological principle.
FINAL SUMMARY
1) The herein described invented method is universally applicable and describes the ultimate achievable
state of technology.
2) Discrete time quantization methods, according to which the received signal is scanned and digitized at
predetermined points in time, prove themselves to be inadequate in the generation of highly efficient
autonomous self-organisation processes.
3) In redundancy-free autonomous self-organizing systems, there are no "points in time" and there is no
determinism. In these systems, STQ elapse times are quantized which are derived from the temporal-
spatial changes in physical conditions between sensors and external sources.
4) Each such system has its own time counting pulses and produces its own time. The time counting
frequency for the quantization of elapse times is continuously adapted in an auto-adaptive manner
according to the relative velocity vm with which changes in condition occur. The time recording has in
each case a quantum nature; i.e. it has the properties of a "discrete counting", no matter whether the
recording is analogue or digital. Moreover, the time recording is subjective and passive; i.e. the time
quanta are "sensed" and not "objectively measured" as in the conventional physical understanding.
5) In order to be able to quantize elapse times in autonomous self-organising systems, the individual
receptors or sensors must have distinctive grades of perception zones (or threshold values).
6) In order to explain precisely the difference between "synchronism" (in the conventional understanding)
and "auto-adaptation", we define the following:
a) parallel synchronism (i.e. "synchronism"): this occurs when temporal changes of physical conditions
of different systems are covariant at the same time.
b) autonomous adaptation (i.e. "auto-adaptation"): this occurs when temporal changes of the physical
state of a particular system are covariant at different times.
7) In all redundancy-free autonomous systems the capability for self-organisation increases with the
quantity of elapse time parameters available for autonomous adaptation and for optimization process,
as well as with the number and variety of sensors or receptors.
8) With synchronism (definition 6a above), the number of quantized elapse time parameters vanishes;
in 3b this number is a maximum (and point 7 above is valid! ). Therefore one can conclude that there
is an inherent tendency in all autonomous systems of the type discussed herein, towards continuous
auto-adaptation, auto-optimization and auto-emulation. This is similar to the biological term "vitality"
9) In autonomous self-organizing systems, there is no "timing" (i.e. temporal motion coordination) with-
out the comparison of currently acquired elapse time patterns with previously recorded elapse time
patterns. Briefly stated, there is no "timing" without accompanying "time keeping".
10) Auto-adaptation theorem of Erich Bieramperl :
Every current non-chaotic change (A) in condition of an autonomous system (X) with the variable
dynamic trajectory vm(1,2,3....n) underlies a currently acquired sequence of elapse times TW(1,2,3 ...n)
as well as a covariant sequence of elapse times TW'(1,2,3 ...n) from a temporal displaced condition
change (A') or from a combination of distinct temporal displaced condition changes (A1 ') (A2 ')...( An'),
whereupon (A) with (A') or (A) with (A1') (A2') ....(An') are approximately isomorphous.
Hence: TW = vm adaptively acquired current STQ(i) or STQ(d) elapse times Tw or Td
TW' = vm adaptively acquired covariant STQ(i) or STQ(d) elapse times Tw' or Td'
Other consequences in the scientific domain are the following
11) Each preselection of a certain time for an intended action, a so-called "act of free will" by an
autonomous organism, results from continued autonomous adaptation of the described type, and is there-
fore not realizable in a deterministic manner.
12) From the ability of an autonomous system to find previously acquired elapse time patterns matching
with currently acquired elapse time patterns, and from trying to emulate these, not only is auto-
adaptation, auto-optimization, self-organisation and recognition of physical surroundings and self-
motion made possible, but ultimately also motion co-ordination (timing), intelligent behaviour and
conscious action are produced.
13) Auto-adaptive, auto-optimizing and self-organizing processes of the described type have universal
validity not only in autonomous mechanistic systems, robots, automatic machines and biological
organisms, but also in molecular and atomic structures. All autonomous self-organizing systems
contain information in form of time data.
The following results from the property that in such systems, "time" is "subjectively sensed" and not
"objectively measured ":
14) In the universe, all time dependent physical values are "subjectively sensed". If there is no adequate
sensorium for time and velocity, then "time" cannot exist objectively. Example: in "black holes", no
"time" exists because there is no sensorium for it. In this case, the atomic and subatomic sensorium is
quasi "dead". Each change of physical condition, which does not underly an auto-adaptive process,
continues increasingly chaotically; whereupon it follows that the described tendency for auto-adaptation
in the universe counteracts the tendency towards entropy and chaos.
15) If vm is too high and STQ(v) is too short to be measured (or "sensed"), then neither an auto-adaptation
nor any self-organization process results (because no elapse times are derivable). Therefore, for
example, the velocity c of propagation of light is an "ultimate value", because it implies the shortest
STQ(v) quantum that can be "perceived" by atomic structures.
16) If there is absolute physical invariance between the sensorium of autonomous systems and their
surroundings, then also no STQ quanta are derivable. This is the reason why, for example, absolute zero
( 273,15°C) is an ultimate physical quantity. In this case, the atomic and subatomic sensorium is not
capable of recognizing a lower temperature because of lack of STQ quanta, and no auto-adaptation
process can take place.
17) As mentioned before, atomic and subatomic structures also display sensory and time quantization
properties. Their description from the view of quantum theory is inadequate. If there is no measurement
or observation of an event, then exists also neither "time" nor "velocity" (S.13). Quantum phenomena
appearing in the known two slit experiment or in the SCULLY experiment (quantum indeterminism) are
explicable in this way.
18) The electromagnetic force, gravitation, the strong and weak interaction (nuclear force), so-called
"autocatalysis" (KAUFFMANN), "synergetic effects" (HAKEN), or other phenomena are produced by
the existence of time quantization sensorium, auto-adaptation and auto-emulation. These features can be
regarded as the inherent teleological principle of the universe (S. 8).
19) The ability to perceive time and velocity as a continuum, and not as an endless series of sensed
elapse times, is likewise produced from continued auto-adaptation and self-organization processes. The
higher the "intelligence" of an autonomous system as a result of such processes, the more distinctive its
subjective time perception and its ability to anticipate.
Consequences for metamathematics, propositional calculus, epistemology and philosophy are:
1) Because there are no deterministic point of times, the status of a system can neither be ascertained to
be at a certain "point in time", nor "points in time" can be determined for a future status. There is
nowhere any type of determinism. Since the classical physics as well as the quantum theory are based
on the postulate that a system is in a certain status at a certain "point in time" (in the first case as points
of phase space, and in the other case as probability distributions in phase space), neither theory can be
completely consistent (see also THOMAS BREUER / 1997).
2) Regarding WIGNER (1961), an absolutely universally valid theory would have to be capable of
describing the origin of human consciousness. The auto-adaptation theory described herein could be
capable of this; the quantum theory cannot. (Wigner postulated that complex quantum mechanics
delivers a usable description of the physical reality only when there is no "subjective sensing". The author
holds the view that subjective sensing also exists in atomic and subatomic structures.)
3) Sequences of elapse times like TW and TW' are definable as strings of an axiomatic formal system;
albeit this system is a "time domain system" and not an arithmetic systems in the usual sense of the
classic number theory. Indeed, said formal system shows at least one axiom and derives from it
continuous strings of numbers through the application of a certain algorithm. Regarding TURING, an
axiomatic number theoretical system can be produced also by a mechanical procedure, which produces
"formulas and algorithms".For this reason, the known logic theorems of GOEDEL, TARSKI or HENKIN
are absolutely applicable on such a model. GOEDEL's incompleteness theorem shows that each
extensive number theoretical model includes consistent formulations which cannot be proven with
the rules of the model, and which therefore are undecidable. This is valid also to metatheoretical models
and to meta-metatheoretical models etc.
For example, a self-referential metatheoretical sentence like the type of the Goedel formulation <I am
provable> is neither provable nor disprovable. A decision procedure for this proposition leads to an
infinite regress. TARSKI showed that a decision procedure for number theoretical "truth" is also
impossible, and leads to an infinite regress. Thus, a self-referential sentence of the type <I am provable>
is admittedly "true", but not "provable". It follows, that "provability" is a weaker notion than "truth"
. HENKIN showed that there are sentences, that assert their own provability and "producibility" in a
specific number theoretical model and which are invariable "true". A self-referential sentence based on
Henkins theorem would be: <It exists a number theoretical model in which I am provable>. Strings
of quantized elapse times like TW and TW' approach the domain of validity of HENKIN's theorem.
Applying Henkin's logic, these strings assert: <I will be produced to proved>. TW and TW's are
therefore strings or sentences that are produced in a specific formal model, which induces its own
decision procedure on truth, consistence, completeness and provability through continued self-generation
(see also description to Fig.10).
In contrast to self-referential strings or sentences of the Gödel or Henkin type, strings of elapse times
are never asserted to be "true", "consistent", "complete" or "provable" to a certain "point in time",
because within the "number theoretical model" in which they are produced, no "points of time" exist.
This model also prohibits superior semantics or metatheories or metametatheories. It is plainly obvious
that each formal system, each metatheory, each meta-metatheory and each semantics, in which axioms,
strings or sentences of any type are formulated, is the result of continued autonomous adaptation (which
is based on the quantization of elapse times) and therefore a derivation of the model described in this
work.
4) The cognition, that a specific formal system exists asserting absolute universal validity, from which
everything has been produced and to whom all other systems have to be subordinated, is not new.
Already in early antiquity, many years before PLATO and ARISTOTLE, the Hebrew Scriptures
(2. Moses 3: 14) let this <source of all logic> say from itself: "JHWH" (spoken: Jahwe or Jehovah), that
is about: "I shall be proved". This sentence asserts its own decision procedure on provability, truth,
completeness and consistence; through a specific formal system, that it "induces to be".
5) There is no "cognition" without "recognition".
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