ISBN
978-3-00-037458-6
ISBN 978-3-00-042153-2
Monograph of Dr. rer. nat. Andreas Heinrich Malczan
This chapter was not originally planned, and actually, from an epistemological point of view, it is a little too early to deal with the chosen topic here. For the understanding of the analogous and binary functioning of the subsystems of the nervous system is an essential prerequisite for this part. Likewise, the imprinting of signal sequences and the system theory of binding would have to be explained. Exactly this is planned in part 3 and part 4 of the monograph, but due to time constraints it has not yet been completed.
Nevertheless, with the theoretical elements already described, we have the most essential foundations for understanding the emergence of consciousness in terms of systems theory.
We summarize the most essential theoretical elements in order to derive from them a process that can be described as a kind of "independence". There is the saying of the "spirits I called". Some processes have the ability to develop unexpectedly. Physicists will recall with horror the enormous destructive power that lies in the unforeseen resonances to which bridge structures are exposed. Seemingly out of nowhere, vibrations generated by pedestrians, wind or rain, for example, can take on threatening proportions by means of natural resonances.
Unpredictability of such occurrences is based on an elementary principle: output of system is fed back to system as input. A low input can therefore be added up over a long period of time and produce a qualitative change, a kind of quantum leap.
Which of these aspects apply to the development of consciousness? In the opinion of the author of this monograph, these are the following prerequisites:
- The inverse system of the cerebellum is able to transform a complex signal input to the higher-level inverse associative matrix back into the original elementary signals and feeds these signals to exactly those thalamic neurons that would have received the original input of this complex signal.
- The system consisting of primary thalamus, primary cortex, primary cerebellum, secondary thalamus, secondary cortex and secondary cerebellum has a multilevel recursive structure. The output of the lower direct systems is the input of the higher direct systems. The output of the higher inverse systems is the input for the lower inverse systems.
- Due to the potentiation theorem, the number of complex signal neurons in the higher cortex areas (the association areas) increases exponentially.
- However, this also causes the number of elementary signal neurons in the thalamus regions of higher levels to increase exponentially, since each complex signal in the system of level k also has corresponding elementary signals of level (k-1).
- The receptive neighborhood inhibition in the thalamus becomes more and more ineffective as the number of stages in the subsystems increases. This is due to the fact that the receptive neighbourhood inhibition exerted by a particular thalamus neuron is spatially limited and becomes weaker with increasing distance. From a certain level in the recursively structured systems, this receptive neighbourhood inhibition effectively disappears because the distance between the active signal neurons to be inhibited becomes too great. The more elementary signals there are, the greater their statistical distance from each other, so that the receptive neighbour inhibition must statistically approach zero.
- Thus, the output of the inverse systems gains the upper hand from a certain step height onwards, because it can now move uninhibitedly in the system.
- The output signals of the inverse system move in two directions.
- The diverging direction feeds this output as input to the higher direct systems. Thus the higher direct systems receive new input contributions, which can be classified as "thoughts" of the individual from a certain level on.
- The converging direction feeds the output of the inverse system back to the subordinate inverse systems. Thereby the subordinate inverse systems receive new input contributions, which as "ideas" (thus also "thoughts") change the inner perception of one's own reflections.
- In this way - usually kept in motion by external input - a time-changing individual image of a partly real, partly imagined world is created, in which in a seemingly inexplicable way - but legally realized by the principle of feedback of the system with itself via the series connection of associative matrices and inverse associative matrices - thoughts, ideas, but also moods and feelings form the current consciousness.
The author decides to call this (as yet untested) knowledge the Avignon theorem. With reference to the poem "Der Zikade letzte Lieder" from the author's pen, which precedes this monograph, the author has the real fear of no longer experiencing a review and possible promotion of his scientific research. Then these would be the last findings on this subject put down on paper.
With the increasing number of signals in the recursively constructed system of direct and indirect associative matrices of the brain, the strength of inhibition in the receptive neighboring inhibition of the thalamus decreases with increasing number of stages of recursions, depending on the distance.
Thus, the output of the inverse systems can become independent and create an inner, multimodal and time-varying image of the world, which can be called consciousness, in superposition and feedback with the current as well as with the earlier, buffered input.
This is the last point in this monograph to address the question of whether or not the brain works like a computer. So far, we have recognized that a system clock is generated in the striosome system of the basal ganglia, which is the basis for the magnocellular climbing fiber signal. The latter proved to be a neural write command. It ensured that the digital signature of a complex signal was stored in a Purkinje group. Thus the embossing signal became the intrinsic signal of the Purkinje group, and the embossing signature of the embossing signal became the intrinsic signature of the Purkinje group. System clock and write command also exist on computers.
We recognized that the parvocellular climbing fiber signal serves to re-imprint, i.e. to constantly refresh the stored signatures in the Purkinj groups. A re-stamping, or more precisely a refreshing, is also used in computer electronics: the refresh cycle in modern RAM memories ensures that the temporarily stored binary values of a dual number in the memory cells are not lost. The binary values are finally represented by electric charges. These are subject to a continuous discharge due to undesired flowing off and are constantly refreshed with the help of the refresh command. If the refresh cycle fails permanently, the stored data is lost forever within a few milliseconds.
So today (in 2012) both the brain and the computer have a refresh cycle, even if it allows the brain to possibly relearn.
Given the importance of this process, we are defining a new term.
We call the process of re-marking a Purkinje group by the parvocellular climbing fiber signal of the matrix system derived from its output a neuronal refresh cycle.
The neural refresh cycle is used to refresh the digital signature of the Purkinje group and to adapt the own and external signal components of this Purkinje group to possible changes. At the same time - in the author's unchecked opinion - it is absolutely necessary for the preservation of the stored information.
If a Purkinje group permanently fails to recognize its own signal, either because it is completely absent for a very long period of time or because it is too strongly covered by foreign signals, the corresponding Purkinje cells are strongly excited, mainly by the foreign signal components. The parallel fibres belonging to the intrinsic signal are initially reduced in their synaptic coupling to the Purkinje cell by LTD, but when they are (partially) activated, the coupling becomes stronger again and finally approaches the original value of 1. For the permanent maintenance of the LTD, the tetanic excitation by the now missing re-imprinting climbing fibre signal would be necessary - even if at large intervals. The permanent absence of the re-impressing climbing fibre signal - caused by the absence of the complex signal - leads to the unlearning of the previously imprinted intrinsic signal - i.e. to forgetting. However, even in the case of the intrinsic signal detectors (basket, star and golgi cells), a permanent and slow change in the synaptic coupling takes place if the intrinsic signal is absent for a long time. This unconfirmed hypothesis is summarized in a new theorem.
If the imprinting signal of a Purkinje group capable of re-imprinting no longer occurs over a long period of several days, the re-imprinting of the associated Purkinje group by the parvocellular climbing fibre signal of the matrix system caused by it is omitted. Thus, the Purkinje group forgets the stored imprinting signal and returns to the unimprinted state in a slow process of forgetting. At about the same time, a Purkinje group which cannot be re-imprinted forgets its stored content if the stored intrinsic signal does not occur during this period, is too weak or is superimposed by external signals in such a way that the Purkinje group does not recognize it.
With computers, the period of time between the power being switched on and the complete loss of the memory content is relatively short. With the Cerebellum this time span is significantly longer.
At this point it should be remembered that not all Purkinj groups have a re-embossed climbing fibre of the matrix system. Especially the evolutionary older of the three Cerebellum subtypes may not be integrated into the matrix system at all. And in the Pontocerebellum, the re-imprinted climbing fibre of a Purkinj group is only formed in a longer process, when the corresponding Purkinj group has been imprinted with an own signal. Before that, this group is striosome-coupled. If it forgets its stored information, the inhibition of the magnocellular (striosomal) climbing fibre signal, which is typical for signal recognition, does not take place, so that this synaptic connection comes into play again. Thus, a matrix-coupled Purkinje group automatically becomes a striosome-coupled Purkinje group again after forgetting its memory content due to the lack of re-embossing. It can therefore be re-marked again with a - this time perhaps completely different - eigensignal. However, this is only an unconfirmed theory of the author of this monograph.
Therefore we distinguish striosome-coupled and matrix-coupled Purkinj groups. The former are addressed by the magnocellular climbing fibers, which are derived from the signal average of the cortex cluster. The matrix-coupled Purkinj groups already have the replication axon of the parvocellular matrix climbing fibers.
Since these two Purkinj groups are not very different in their structure, the striosome-coupled Purkinj groups will also be subject to a process of forgetting if they have not perceived their imprinting signal for a long time.
Nevertheless, the question remains why we do not forget stored information, although it is not repeated daily. This is where the process of dreaming comes into play. In the example process of dreaming (dog film!) shown here, it becomes clear that it is inseparably connected with the re-imprinting signal. Dreamed things are thus strengthened in terms of memory by re-imprinting. On the other hand, the "oscillation theorem" presented in the following explanations ensures a continuous neuronal refresh cycle. And finally, there are other places and algorithms in the brain that serve to store signals. The cerebellar Purkinjegroup memories are only one element of several used in the brain. The hippocampus, the cinguli gyrus, but also the cortex cortex as possible storage locations.
It seems that the similarities between the computer and the brain that have been recognized so far have already been listed in full. Nevertheless, we should not simply dismiss this topic. Because there is another common ground between computer and brain: the representation of numbers.
In the brain, complex signals are represented by their digital signature. This digital signature represents a dual number. Now all dual numbers are just ordinary numbers. So the brain also uses number theory. From here on, mathematicians should read on critically, because now we are taking the number-theoretical path to understanding consciousness and thinking.
Each neural complex signal corresponds to a complex neuron in the secondary cortex. To the complex signal and the complex neuron we have assigned a natural number, the digital signature of the signal. Here, the digital signature corresponds to a dual number whose digits correspond to the activity state of the corresponding signal neurons. A dual digit zero represents an inactive signal neuron, the dual digit one represents an active one.
Thus a complex signal can be represented in the brain in two ways:
- by an active complex neuron in the secondary cortex or
- by a set of active signal neurons in the primary cortex.
The signal requires the presence of a non-material and a material component:
- the ideal component consists in the state of excitation of either the complex neuron in the secondary cortex or the associated signal neurons in the primary cortex
- the material component consists in the physical existence of both the complex neuron in the secondary cortex and the associated signal neurons in the primary cortex.
Both components are necessary, both the ideal and the material. Because the mere physical presence of the complex neuron or signal neurons is not enough. This is due to the duality of the signal value: The complex signal - but also all elementary signals involved - can be active or passive.
It is therefore a difficult task to draw conclusions about the signal values, or more precisely, the digital signatures of the neurons involved, from the purely physical existence of the neurons and the presumed knowledge of their interconnection with each other.
To simplify the use of language, we define the term elementary neurons.
The active signal neurons that are assigned to an active complex signal are called the elementary neurons of this signal.
Elementary neurons therefore only include those signal neurons that are active. Those whose inactivity is marked by a zero in the digital signature of the complex signal are not counted as elementary neurons of the signal. Each elementary neuron corresponds to a non-trivial elementary signal at the level of the primary cortex. The complex neuron also does not belong to the elementary neurons.
Thus the following theorem applies:
Every active neural complex signal has two dual forms of representation, one ideal and one material. The ideational representation consists of the neuronal activity. The material representation consists in the existence of the associated neurons.
Each representation form of a complex signal also has two dual signal forms: the complex form and the elementary form. The complex form consists of the presence and excitation of the complex neuron. The elementary form of the complex signal consists in the existence and the excitability of the corresponding elementary neurons.
The above theorem does not claim that all signals of the brain are basically complex signals. The author would like to expressly contradict such a dogmatic interpretation. It is quite possible that there are other forms of existence of neuronal signals that have not been described so far. In particular, a widespread thesis that the information is widely (possibly even diffusely) distributed in the brain cannot be contradicted in any way. Because in view of the fact that a Purkinje cell in a coined Purkinje group can tap a maximum of about three hundred thousand parallel fibres, of which in extreme cases each one could represent its own elementary signal, a digital signature of such an own signal then possibly has three hundred thousand elementary signals. Each one then corresponds to its own elementary neuron in the primary cortex. And these are certainly not all concentrated in the same place. On the contrary, we must assume that the Purkinje cells with their huge dendrite trees cross the cluster boundaries of the associated cortex clusters and are synaptically coupled with neighbouring clusters. In this case, the "neuron cloud" of elementary neurons belonging to a complex signal with sufficient bit width would be distributed quasi diffusely in the brain.
We have already given preference to overlapping cluster boundaries on page 1 of this monograph. There we explained immediately after the definition of the cortex clusters:
(Own citation from page 1 of this monograph):
"The cortex clusters may overlap each other so that neighbouring clusters have a common subset of neurons. This overlap makes sense from a systems theory perspective, as will be shown later."
(end quote)
In addition to the overlapping of the cortex clusters, the spatial growth of the dendrite trees of the Purkinje cells (which started during evolution) caused the cluster boundaries to be exceeded, so that a Purkinje dendrite tree could also use the input of the neighbouring clusters. The crossing of cluster boundaries by the Purkinje cells of a Cerebellum cluster makes sense. In this way, even elementary signals that belong to different, perhaps even non-adjacent clusters, can be combined into a complex signal.
In the course of evolution, the radii of the cortex clusters increased, so that their overlapping increased. Parallel to this, the dendrite trees of the Purkinje cells could gradually become larger and eventually reach gigantic dimensions. This led to the additional crossing of cluster boundaries. This led to an extreme increase in the bit width of the digital signatures of the learnable complex signals.
The crossing of cluster boundaries is a prerequisite for the emergence of multimodality, in which signals of different modalities (sight, smell, taste, ...) can be combined.
After this brief objection, we recall the initial situation: Every active signal could exist on the one hand in the activity of its elementary neurons, and on the other hand in the activity of its complex neuron. The primary cortex was responsible for the first signal form, the secondary cortex for the second form.
Thus, on the one hand we would have the representation of the signal in a single complex neuron in its complex form. Alternatively, the same signal would exist in the active elementary neurons in its elementary form.
Thus, a class formation is realized within the neurons as data memory. On the one hand, there are complex neurons that function as multi-bit memory. They correspond to a dual number, which represents the stored signal as a digital signature. This class of neurons is present in the cerebellum and is in reality not only present in the form of Purkinje cells, but also in the basket, star, golgi cells and nuclear neurons that interact with them (own and foreign signal detectors).
The other class of neurons stores the elementary signals in one-bit neurons located in the primary and secondary cortex.
A previously unmentioned class of neurons is located in the primary cortex and has the task of digitizing the analog input of the receptors of the living being so that it can be further processed by the one-bit neurons of the secondary cortex and the multi-bit neurons of the cerebellum. We will find these in the planned third part of the monograph.
But in view of such representational states in the brain, how does thinking emerge as an information-theoretical process?
Here we remember the cooperation of the direct and inverse systems in the brain. Certain subsystems in the brain are capable of transforming a signal present in the elementary form into its complex form.
We remember the direct signal evaluation principle:
- If an unknown imprintable complex signal in its elementary form influences the primary cortex via the primary thalamus, the moss fibre input, which is conducted to the cerebellum via the bridge nuclei, leads to the imprinting of the signal in a Purkinje group. This requires the magnocellular climbing fiber signal formed by the striosome system, which we assign to short-term memory. This is later replaced by the parvocellular climbing fiber signal of the matrix system, which we assign to long-term memory.
- The output of the positive nuclear neurons of the Purkinje group marked with this complex signal reaches the secondary cortex via the secondary thalamus and excites exactly one complex neuron there.
- If this complex signal is later present in its elementary form again after its imprinting and excites the primary thalamus, the double system of primary cortex and primary cerebellum transforms this elementary form into its complex form. This excites the associated complex neuron.
But there is also an inverse signal evaluation principle:
- An excited complex neuron in the secondary cortex corresponds to an imprintable signal in its complex form. The excitation of the complex neuron is naturally generated by the input to the primary thalamus and from there to the primary cortex. From the signals of the elementary neurons involved, the matrix system of the basal ganglia gains a group of climbing fibre signals, which occupies as many Purkinj groups in the inverse secondary cerebellum as the complex signal in its elementary form has elementary neurons. By imprinting, each of the elementary signal-Purkinj groups will later be active whenever the complex neuron is active in the secondary cortex. This is because its activity feeds the excitation into these Purkinj groups via a bridging nuclear neuron and an associated moss fibre. The positive nuclear neurons of these Purkinj groups activate exactly those thalamus neurons in the primary thalamus that belong to the elementary form of the complex signal.
- Thus, the double system of secondary cortex and inverse secondary cerebellum transforms every imprinted signal from its complex form into the elementary form.
Therefore the following oscillation theorem, which the author names after himself, applies, so that at least one theorem of this theory bears his name. From here on, everyone may decide for himself whether the brain and the (present) computers are rather similar or rather dissimilar. The principle of oscillation is completely unknown for computers of the present (in 2012). Data are stored statically in computers and do not have two complementary and oscillating manifestations. The author is convinced that the question whether the brain works like a computer is not a really useful question. If we will know how the brain thinks, we will one day build thinking objects that will probably also be called computers. Whether these will have anything to do with the present computers, the future will show.
In the brain, a neuronally active complex signal leads to a constant oscillation of its signal form between the two possible forms of representation, the complex form and the elementary form, in the absence of the receptive neighbor inhibition in the primary thalamus. The transformation of the elementary form into the complex form realizes the double system of primary cortex and primary direct cerebellum. The transformation of the complex form into the elementary form realizes the double system of secondary cortex and secondary inverse cerebellum. The oscillation time depends on the transit time of the signals in the involved subsystems. The continuous oscillation between the two signal forms also serves to buffer the signal. If there is at least partial receptive neighbor inhibition of the neurons involved, this permanent oscillation can come to a standstill due to damping, so that the associated signal is then neither permanently stable in its complex form nor in its elementary form. If only individual elementary signal components are receptively inhibited, while new elementary signal input or new complex signal input is added, new complex signals can arise due to the selective signal recognition in the cerebellum, whose activity also oscillates between the elementary form and the complex form.
Since the axons involved as long projection lines are for the most part strongly myelinated, the author estimates the time required for a complete loop run at about 25 to 30 milliseconds. Then the oscillation would have a frequency of about 30 to 40 Hertz. However, this is only an unconfirmed assumption.
At this point it should be noted that the oscillation of the signals can be seen in the sketch 2.8 on page 149.
If this oscillation theorem applies, we must imagine the presence of a certain signal as an oscillation in a neural oscillating circuit consisting of two subsystems. One subsystem consists of the primary thalamus, the primary cortex cluster and the primary cerebellum. In this subsystem the elementary form of the signal is located and is represented by the presence and activity of the elementary neurons that belong exactly to this signal.
The complementary system consists of the secondary thalamus, the secondary cortex and the inverse secondary cerebellum. Here the signal is present in its complex form and is represented by the presence and neuronal activity of the complex neuron. Due to the relatively unchangeable, but greater length of the axons involved, an action potential that comes from the complex neuron needs a certain time to excite the elementary neurons so that these now generate action potentials and send them in the direction of the complex neuron for a complete loop run. This loop time is relatively constant, since the axon length does not (almost) change and the speed of propagation of the action potentials does not change either. Therefore, the neurons involved alternately oscillate with a relatively constant frequency, sometimes the complex neuron, then again the elementary neurons, then again the complex neuron, then again the elementary neurons, and so on and so on. (The so-called clock generators in the brain function in a similar way).
It is possible that the two subsystems could also have an inhibiting effect on each other by means of additional recurrent lines. Then, similar to the elementary oscillating circuit in the striosome system, there would be a clocked oscillation in which a continuous signal would be completely suppressed for a relatively short time in each case, only to be fully active again for a short time (just as long) afterwards.
Thus, it is quite possible that the output of the negative nuclear neurons in the nucleus ruber has an inhibitory effect on the descending bridging nuclear signals of the cortex, which in turn supply the cerebellum with its moss fiber input. This variant would fit perfectly to explain the functioning of systems in which the recognition grid (cortex) and search grid (cerebellum) interact. However, this topic will not be dealt with in this monograph, but is planned for parts 3 and 4. There we will (probably) learn why and especially how the nucleus ruber switches from the cortex to the cerebellum or from the cerebellum to the cortex in the selection of its signals. This switch-over hypothesis has been around in neuronal science for quite some time, but could not be specified so far.
The oscillation theorem gives us an answer to the question of how forgetting is prevented in the brain. On the one hand, forgetting is extremely important. Therefore it is simply included in the brain, especially in the cerebellum (theorem of forgetting).
On the other hand, practice teaches us that we also have long-term storage.
The oscillation theorem lets the signals rotate in closed signal loops. With each loop rotation, the re-stamping is activated in the cerebellum. This causes the signal to be re-imprinted, thus delaying any possible forgetting.
On the other hand, signal oscillation has a fascinating side effect for us humans. It is used to start dreams. With increasing signal poverty - e.g. at the beginning of dawn - the receptive neighbor inhibition in the primary thalamus decreases, because the signals that cause this inhibition are increasingly missing. As a result, the oscillating signals still present in the system - primarily the strongest of them - can excite the associated Purkinj groups more strongly via the post-impression axons. Stronger excitation, however, results in stronger repercussion signals (natural resonance). If, as described in the section on inverse video memory, these now also excite the neighboring Purkinj groups, a film runs in which all signals of the sequential memory chain are activated one after the other. These signals provide the illusion via the inverse system that the corresponding original signals are active. This is exactly the dream experience. It owes its start to the oscillation of significant signals from the higher subsystems. It is comparable to the waves on a water surface, which form a local maximum by superposition, which causes the start of a dream experience.
We summarize this knowledge in our own (unconfirmed) theorem.
As a result of the increasing exhaustion at the end of the day, combined with the reduction in the general stimulus intensity, among other things due to the decreasing brightness and the onset of evening rest, and promoted by an activity rhythm controlled by the nucleus suprachiasmaticus (SCC), the activity of the receptor input in the primary thalamus decreases towards the end of the day. As a result, the stronger complex and elementary signals still oscillating in the system can prevail over the weaker signals and against the weak receptor signals. This is additionally promoted by the reduction of the strength of the indirect signals (ARAS) in the cerebellum, which shifts the operating point in such a way that the response diversity decreases. Due to the feedback in the closed signal loops, these signals can increase in strength, since the receptive neighbor inhibition is greatly reduced. The strongest of these complex signals can activate the signal sequences stored in the cerebellar memory chains (video memories) because they are partial signals of them. The memory content of the memory chain is sequentially activated by the post-marking axons, which is supported by the secondary excitation of the intrinsic signal detectors of similarly chained Purkinje cells. Thus, an activity migration within the memory chain takes place, in which each Purkinje group is activated by the reimprinting axon of its predecessor group and in turn activates the successor group with its reimprinting axon. The inverse cerebellum delivers the original signals to the primary thalamus for each activated Purkinje group. There the illusion is created that the stored events are actually taking place. This process corresponds to the experience of a dream. The strong signal oscillation that takes place during this process is noticeable in the EEG (REM phase).
However, it does not have to be only visual dreams, which are caused by the described algorithm or by other circuit variants which, according to the author, are also functional. Also, the start of a signal sequence stored in a memory chain does not necessarily have to be involuntary in the subconscious.
For example, we are quite capable of consciously recalling a poem we have already learned - e.g. the bell by Schiller. Here the activation of the first Purkinje group of the chain happens consciously and mostly intentionally, even if an unconscious start is also possible. The latter becomes clear with the so-called "earwigs". These are melodies which (with or without singing) start and are played in the head, quasi by themselves. This then even gives pleasure, although it is a kind of "foreign interference" and undermines our own will. Good catchy tunes make themselves independent in the head and rotate there (as signal sequences) constantly back and forth - but not only in one affected person, but in hundreds of thousands. From this point of view, even potential "opponents" of an oscillation theory could make friends with exactly the oscillation theory presented.
The activation of traumatic experiences can also happen independently - almost without external cause. In this case, one could certainly draw conclusions about the neuronal circuitry (especially of the unconscious and conscious activation) from the treatment methods, if one had the time to deal with it more closely. The knowledge of psychologists and psychoanalysts represents a treasure that has been little used so far, especially with regard to the decoding of real neural circuits.
Of course, it must be stated that there is a far better algorithm for storing signal sequences than that of cerebellar memory chains involving the secondary excitation of similarity-coupled signals and the activation of neighbouring Purkinj groups by additional post-imprinting signals of the predecessor group. However, these are deliberately not presented in this monograph, since they require a separate, self-contained theory of the hippocampus and the limbic system. This theory has only just been developed.
Here there is a real interface to the already quoted book "Zur Konstruktion künstlicher Gehirne" (On the Construction of Artificial Brains) by Ulrich Ramacher and Christopf von der Malsburg. Both assume in their work - like many others, e.g. Prof. Wolf Singer - that the neuronal representation of a signal is based on the temporally synchronous activity of neuron ensembles. An object with many different properties would thus be neurally grasped in the brain in the process of binding, because a set of neurons belonging exactly to it would realise the presence (activity) of this signal through its synchronous activity.
In retrospect, it must be said that temporal synchronicity is indeed present.
In the case of complex signals, the synchronous activity of the corresponding elementary neurons is exactly the prerequisite for the imprinting of the Purkinje cell and later for the recognition of the complex signal. And due to the oscillation theorem, once a complex signal has been recognized, it remains in the brain for a relatively long time if it is at a sufficiently high level of the recursive system - where the receptive neighbor inhibition no longer applies. It changes constantly (in the millisecond range) from its elementary form to its complex form and vice versa and thus keeps the once achieved synchronicity of oscillation alive until an external input of elementary or complex input leads to the recognition of the changed signal configuration. This monograph therefore confirms the assumption that temporal synchronicity of neuronal activity and signal recognition are mutually dependent.
At this point at the latest, the question arises whether the periodic neuronal excitations, which can be detected by means of EEG, could not be a confirmation of the oscillation theorem. We also recall the theorem 1.14 about the loose synchronization of dopaminergic neurons in the substantia nigra pars compacta. We also recall that the cortex signals on their way through the matrix system must pass exactly through this substantia nigra pars compacta in order to finally become parvocellular replication axons. Here the (loose) nigral synchronization actively intervenes in the oscillatory process.
Future research should clarify at what frequency the signals oscillate between the complex form and the elementary form, i.e. between the primary cortex, primary cerebellum, secondary cortex and inverse secondary cerebellum, and whether there really is phase-shifted inhibition that occurs with a certain time interval after excitation. The duration of an oscillation, i.e. the change of the signal form from one state to another and back, is decisive for the speed of analytical thinking. At least this time needs a latent (unborn) thought to emerge from the subconscious and become conscious. This will be explained in more detail below.
Now we can ask ourselves - quasi as a crowning conclusion - the question of what thinking in itself could be. There can be many kinds and methods of thinking. The author wants to show here only one form of thinking, which he calls analytical thinking. It serves to recognize the connections between a complex signal and its elementary signals.
The question "Is that a dog? " can only be answered if you know the most important characteristics of a dog. Furthermore one must observe the object, which is meant by the specification "that", analytically. With each characteristic, which one recognizes by this object, one can better answer the question whether "that" is a dog. With each recognized property, the Purkinje group "dog" in the primary cerebellum receives further, additional input via its parallel fibers. Their input comes from the signal neurons of the primary cortex, where the individual signals - quasi the elementary characteristics of a dog - are recognized step by step. If the moss fiber input exceeds the required minimum strength, the own signals in the "dog cell" outweigh the also still existing foreign signals. The "dog cell" reports the excitation to the secondary thalamus and the secondary cortex: "Dog recognized!
Here the process becomes independent. The complex neuron "dog" in the secondary cortex activates those Purkinj groups of the inverse cerebellum via the bridge nuclei, which are assigned to the elementary signals of the dog. Their positive nuclear neurons activate the elementary neurons in the primary thalamus that belong to the complex signal "Dog". This is where the illusion of having perceived a dog is created. But: The complex neuron "Dog" activates all elementary cells belonging to the object "Dog" in the primary thalamus. Thus, all elementary signals to the dog cell are activated, especially those that were not recognized at all by the previous observation of the real "das" object. Since the elementary neurons have a relatively large spatial distance to the "dog properties", the receptive neighbor inhibition of each of them is not able to inhibit the other elementary signals of the dog signal.
The inverse cerebellum thus activates all elementary neurons in the primary thalamus that belong to the complex signal dog. Including those that we did not recognize in our analysis. And all modalities are activated: optical features such as colour or shape, but also acoustic ones such as "barking", additional olfactory ones such as "smells like dog", even pain signals such as "dog bites the hand". These additional signals represent an input that the system itself has generated.
The property, which the author calls Palm's theorem, that a Purkinje group recognizes its own signal even if it is covered by foreign signals to a maximum of half or is incompletely present, now proves to be the system-theoretical driving force of analytical thinking.
The cerebellum (as an associative matrix) thus recognizes a complex signal, although it is either incomplete or is superimposed by an external signal as long as the intrinsic signal component predominates. Its output activates a cortexneuron, whose output in the inverse system activates all learned elementary signals to the recognized signal. Among them are also those that were previously inactive. In this way, the inverse system itself generates additional input, which can be superimposed on the other signals in the neural system and combined to form new complex signals. This self-created input represents the thoughts that are generated in us during analytical thinking. They arise from our subconscious, because we have previously learned that the object "dog" has more characteristics than we have recognized during our own analysis. Our complex cell in the cortex complements, via the inverse system, the missing properties that are formed in our brain as new thoughts such as "The dog is trustful, however", "The dog is cuddly", "The dog does not yap at all" or even "That's our dog Djego!
The author of this monograph would like to call this kind of signal processing analytical thinking.
The reason for analytical thinking is the completion of the previously only partially existing elementary signals to a complex signal during the constant oscillation of the signal form between the complex and the elementary form, which is realized by the inverse system. The complex signal is recognized although it is incomplete and may be overlaid by foreign signals because its own signal component exceeds the foreign signal component. The signal completion by the inverse system after recognition despite incomplete input creates new elementary signal input, which we interpret as new thoughts. These change our inner image and become conscious as (analytical) thinking. Analytical thinking can usually only take place in the higher levels of the recursively constructed system, because there the receptive neighbor inhibition is distance-related ineffective.
Now we can specify what it means when "thoughts emerge from the subconscious". We can spatially locate this subconscious in the brain and even indicate its neural circuitry (if the author's theory is confirmed).
The subconscious is formed by the inverse cerebellum. There, our information already belonging to the long-term memory is available in its elementary form, but is generally inactive. Only the activation of complex neurons in the secondary cortex provides the input for the signal response via the bridge nuclei and moss fibres, which then "emerges" from the inverse cerebellum and works its way through to the primary thalamus - against a possible receptive neighbouring inhibition. These signals become our thoughts the moment we reach this thalamus. Therefore the thalamus is rightly called "the gateway to consciousness".
We summarize our knowledge about the subconscious in our own theorem. It will probably be the last theorem in this monograph.
The subconscious is formed by the indirect cerebellum clusters. They transform the complex signal input from the superior (secondary) cortex, which reaches them in the complex forms via the bridge nuclei, back into the completed elementary forms of the elementary signals and send them to the subordinate (i.e. primary) thalamus, where they become conscious. As the thalamus again transmits this data to the superordinate cortex, which transfers it back into the complex form, this data oscillates back and forth between the complex form and the elementary form for a longer period of time and is available to us during this time as thoughts, ideas and perceptions in consciousness. The change of the input situation through new input or the receptive neighbor inhibition leads to the constant active influence of the subconsciousness on the consciousness.
The inverse cerebellum not only provides the subconscious, but also enables us to think analytically.
However, analytical thinking requires knowledge, which should be as comprehensive as possible. Subsequently, a subset of properties is sufficient for object recognition, which completes our inverse system. The completion releases new, self-generated input, which is combined with the previous current input and with already stored input and again leads to new "completion thoughts".
There are many application examples. For example, the Pythagorean theorem is neurally derived in our brain in such a way that initial information is gradually supplemented by the inverse system. From the information thus obtained, the validity of the theorem is ultimately proven in a chain of evidence by combining it with existing knowledge.
Depending on the extent and exactness of the knowledge already available, analytical thinking can also be completely misleading. Those who have learned that the earth is a disk will be obsessed by the fear of falling off at its edge. Therefore, the eternal striving for new and confirmed knowledge is not only a prerequisite for making analytical thinking more error-free, but it also protects us from the negative consequences of wrong decisions. This applies both to the individual and to society as a whole. On the other hand, there are also efforts by certain interest groups to limit precisely this transfer of knowledge. Here, it is above all dictatorial systems and ideology-based claims to power that oppose the dissemination of knowledge and want to replace free thinking with dogmatism.[1]
But also the position of man in relation to man and to the other living beings depends on whether we become aware of the similarities and differences, or whether we (unjustifiably) empower ourselves to rise above all life and claim a special position. With this monograph at the latest, man has forfeited the claim to be the only thinking and intelligent being on this planet Earth. Likewise, it must also be examined whether consciousness as such did not already exist in the first mammals, or could even exist earlier. The neuronal circuits for this are from evolutionary point of view certainly already ancient!
It now seems necessary to point out that the secondary cerebellum - like any cerebellum - excitably projects to the superior thalamus. It would be futile to call this thalamus a tertiary thalamus, which sends its signals back to a tertiary cortex. Likewise, the output signals of the neurons of the tertiary cortex are transformed back to climbing fibre signals in the matrix system, and so on. For each cortex of level k < n there is a superior cortex of level k+1, whereby the number of levels n is of course finite. Likewise, there is again a subordinate and a superordinate direct cerebellum as well as an inverse cerebellum in the superordinate cerebellum, which supplies the subordinate thalamus with inverse input. In addition, we observe a transgression of the cluster boundaries in the cerebelli of the different stages. Therefore, the higher cortices are basically to be regarded as associative regions, which integrate and evaluate complex signals on a higher level.
It should not be forgotten that in addition to the parvocellular system there is also the magnocellular system. Remember here the theorem of the dissolution pyramid according to Ramacher. The signals of this system are also evaluated in the cerebellum and constitute the part of the integration areas of the cortex. This system is evolutionarily older and works closely with the limbic system. At least the author is convinced of this.
When the final version of this monograph had already been printed out for final checking, the author noticed an imperfection in the problem of the cooperation of the direct and inverse systems. This seems so significant that there is now a kind of unplanned addition here. It is another result of the validity of Palm's theorem.
As is well known, a Purkinj group recognizes its own signal even if it is incomplete, as long as the external signal portion does not exceed the own signal portion. The recognition activates a complex neuron in the secondary cortex. The output of this neuron also goes through the bridge cores to the inverse secondary cerebellum. The positive nuclear neurons of the associated Purkinj groups now activate all elementary signals belonging to the complex signal in the primary thalamus. These active thalamus neurons activate all elementary neurons in the primary cortex that belong to the complex signal.
However, as we have discovered, every active signal neuron of the primary cortex also projects into the primary cerebellum via the bridge nuclei. There, exactly the moss fibres belonging to this complex signal are active - all of them - including the previously inactive ones.
Up to now, we assumed that the activity of the moss fibres mentioned above caused the complex form of the signal to become active by activating the Purkinje group to exactly the complex signal under consideration. This resulted in the constant oscillation between the elementary form and the complex form of the signal under consideration.
All this is correct, but incomplete. Because when a pair of moss fibres is active in the primary cerebellum, normally not only one signal - the one considered above - is activated, but several, sometimes even many. Why?
Because each Purkinj group, whose own signal is fed at least half by the activated moss fiber pupolation, responds with an output if the disturbing foreign signal component is not too large. An active pair of moss fibers usually excites a whole class of Purkinj groups in the cerebellum, at least half of whose intrinsic signal is formed by these active moss fibers, if the interfering signals are not too strong.
So the inverse cerebellum not only generates the elementary form of an active complex signal K, but the primary cerebellum also responds (slightly time-delayed) with the output of all possible complex signals K1, K2, K3, ... Km, which have at least half of all moss fibres in common with the signal K and whose external signal component is not too large. The output of the positive nuclear neurons of these activated Purkinj groups now reaches the associated complex neurons in the secondary cortex as input created by the system itself.
While our subconscious completes the incompletely recognized elementary signals to a complex signal and these become conscious to us, all complex signals sufficiently similar to this complex signal are additionally created in our secondary cortex. This is where the characteristic begins that one would call multitasking in computers.
If, for example, the question is asked: "Is this a dog", the inverse cerebellum first of all provides all dog characteristics. One would be, for example, "Dogs eat meat and bones". But if in our cerebellar data memories the sentences "lions eat meat and bones", "tigers eat meat and bones", "jackals eat meat and bones" are already stored, there are also the three Purkinje cells with exactly this content. Each sentence consists of four essential words (if the auxiliary word "and" is not counted). And therefore each sentence is 75 percent related to the sentence "Dogs eat meat and bones". All four Purkinje cells use the moss fibres of the three signal components: "eat", "meat", "bones". (The auxiliary word "and" serves only for grammar purposes).
So if the moss fibres are activated to the sentence "Dogs eat meat and bones", the Purkinj groups "Lion", "Tiger" as well as "Jackals" answer with an output.
Therefore, we should not be surprised if someone, when looking at our dog, exclaims with delight: "He looks like a little tiger", "He's hungry like a little lion", or, for example: "He'll pounce on the meat and bones like a starving jackal! Such sentences in the presence of a small dog are proof that the dog was recognized as such, but that the elementary signals of the dog in our inverse cerebellum not only activated the complex cell "dog" in the secondary cortex, but also the similar complex signals "tiger", "lion" and "jackal" via the path of the direct, primary cerebellum.
Here, however, we must mathematically determine the similarity of signals.
Given its n elementary signals. If each elementary signal can be either active or passive, i.e. the associated signal values are 1 or 0, a total of 2n different complex signals can be formed from these elementary signals. Two arbitrary complex signals from this complex signal set may now both use exactly k identical active elementary signals, while they are different in the remaining elementary signals. Then the quotient of the number k and the number n is the similarity of these signals.
The similarity is a number between 0 and 1. We analyze an example:
- S1 = (1; 1; 0; 0; 1; 0; 0; 1; 1; 0)
- S2 = (1; 0; 0; 1; 1; 0; 1; 1; 1; 0)
- In the first, the fifth, the eighth and the ninth position the corresponding elementary signal is active in both signals, i.e. it has the signal value 1. That is exactly four positions. Both signals consist of 10 positions each.
- The similarity value is V = 4/10, thus V = 0.4.
- The two signals are forty percent similar.
Each position that is active in both signals simultaneously, i.e. has the value 1, corresponds to a common moss fibre in the primary cerebellum. A signal of the form
S = (1; 0; 0; 0; 1; 0; 0; 1; 1; 0)
if it were active, activate not only the Purkinje group to signal S, but also the Purkinje groups to signals S1 and S2. Provided that no active extraneous signal would be excessively strong and would "drown out" everything.
Therefore, the interaction of the direct system and the indirect system in connection with signal oscillation can lead not only to incomplete signals being completed, but also to similar signals being generated in the system itself and entering our consciousness as thoughts. We want to add this insight as the very last theorem of this monograph, as it were as an addition. For this insight was not originally present and even in the way just described it emerged from the author's subconscious.
During the oscillation of signals between their complex form and their elementary form, the currently active moss fibers in the primary cerebellum additionally activate all Purkinj groups whose own signal is at least half-fed by these active moss fibers and whose current external signal strength does not exceed this own signal strength. This results in additional complex signals in the secondary cortex during signal oscillation, which are similar to the previous ones and which we become aware of as new, similar thoughts
As a last thesis, the assumption should be discussed that especially in the lower stages of the recursive system, and there especially in the thalamus, similar complex signals are spatially closer together than dissimilar ones. However, this makes possible receptive neighborhood inhibition more likely, so that the (signal-strong) main thoughts will receptively inhibit the (signal-weak) similar secondary thoughts. However, this inhibition is not absolute but relative and therefore roughly proportional to the signal strength, so that the secondary thoughts are weakened but still exist. New input can then possibly inhibit the main thoughts and strengthen the secondary thoughts. Then an active, conscious thought work would be present, in whose result old and possibly erroneous thought processes are replaced by new, corrected ones. We are fully aware of this form of "insight" while it is taking place, because the signals coming from the primary cerebellum reach the secondary thalamus and the secondary cortex.
This is to be the last insight that the author gives to the inclined readers in this monograph. May the future decide whether a brain theory is close enough to reality, which - even if it temporarily omits the analogue and binary system of the brain - explains the formation of thoughts and thought processes in the brain at least partially in a system-theoretical way, and thereby designs a neuronal circuit of the brain, which will still have to be confirmed. May neurologists, mathematicians, physicists, psychologists and psychoanalysts get to work and check whether the "Manifesto of the eleven leading neuroscientists" is actually beginning to bear fruit. Without this call, the author would never have felt compelled to go into such detail in order to find a halfway plausible explanation for the neuronal circuitry of the brain and its possible functioning. For this reason, the authors of this manifesto are expressly included in the circle of those to whom the author is deeply indebted.
The reader now has the last word.
[1] See "Eastern turning fever" - last page
ISBN
978-3-00-037458-6
ISBN 978-3-00-042153-2
Monografie von Dr. rer. nat. Andreas Heinrich Malczan