US-Patent Nr.:  US6172941      (filing date 16/12/1999)

 EP Patent application   EP01146406A1  (filing date 03/12/1999)                                                                         

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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