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,