GYVASIS PASAULIS - REKURSIJŲ BIOINŽINERIJA

Dobilas Kirvelis and Kastytis Beitas

Abstract.The aim of this work is to show that the essence of life and living systems is their organization as bioinformational technology on the base of informational anticipatory control. Principal paradigmatic and structural schemes of functional organization of life (organisms and their systems) are constructed on the basis of systemic analysis and synthesis of main phenomenological features of living world. Life is based on functional elements that implement engineering procedures of closed-loop coding-decoding control (CL-CDC). Phenomenon of natural bioinformational control appeared and developed on the Earth 3-4 blnyears ago, when the life originated as a result of chemical and later biological evolution. Informatics paradigm considers the physical and chemical transformations of energy and matter in organized systems as flows that are controlled and the signals as means for purposive informational control programs. The social and technical technological systems as informational control systems are a latter phenomenon engineered by man. The information emerges in organized systems as a necessary component of control technology. Generalized schemes of functional organization on levels of cell, organism and brain neocortex, as the highest biosystem with CL-CDC, are presented. CL-CDC concept expands the understanding of bioinformatics.

Keywords:Living systems, Information, Coding-Decoding, Informational control, Anticipatory control, Organized systems.

PACS:87.18.Vf; 87.18.Nq.

1. INTRODUCTION

The aim of this work is to show that the essence of life and living systems is their functional organization, as phenomenon of the natural technology based on the bioinformational or informational model. The model is based on anticipatory control. It is a trial to interpret the origin and the evolution of life as a natural origin and evolution of bioinformationally controlled technologies that ensure adaptability of life and living systems. The point of view of biocybernetics, the paradigm to understand the life, has been applied. Here, the living systems are interpreted as systems for collecting and processing information in order to control the subsystems of material and energy conversion, which technologically grounds the existence of elaborated anticipatory control. Efficiency of natural bioinformational control ensures stability and adaptability of living systems.

Biology has not yet found a general principle of the functional organization of living systems [1]. The life on the Earth is a complex system, consisting of various functionally closely interconnected components such as biomolecular clusters, organelles, cells and organs, which undergo continual changes in order to adapt to the varying environment. It is supposed, that the general principle of the functional organization of life does exist. But experimental research, which is the dominant method of biology, floods up the information sphere with the diverse data that often act as a noise in the search for the essential understanding of life. Therefore, other methods of scientific research which could enable us to look at the living systems from distance are required.

The method of choice might be the method of theoretical synthesis of the hypothetical functional system with the subsequent application of this system for interpretation of experimental facts. In search for the general principle of life organization, Н. Maturana and F. Varela raised the concept of autopoiesis (self-production) as an initial functional hypothesis [2]. From this point of view life, is self-producing technological system. Technology is a system of knowledge and practice that deals with tools and techniques as a whole for purposive mass serial transformations and production of matter (chemical or material technologies), energy (physical or energy technologies) and information (information technologies). Life is a natural technology, the system of various organized complex technologies.

It is accepted that 3.8-3.0 bln years ago, the life emerged as the qualitatively new form of matter organization. The complex living world that consists of hierarchically organized biosphere (and noosphere at later stages) has appeared as a result of the evolution of life. The emergence of life is based on the emergence of natural technology controlled by bioinformation, that is a new phenomenon of the world. It has extended the dyad of matter and energy to triad of matter, energy and information. The totality of stochastically organized conversions of matter and energy was enriched by a rise of information that caused the beginning of organized, purposefully controlled processes, i.e. technology. The ideas of life, information, control, technology andorganization must be inseparable in a scientific understanding of living systems. Evolution of living systems should be analyzed as evolution of informationally controlled technologies.

Biosphere and technosphere might be represented as systems of natural and artificial technologies. A biological species might be regarded as a natural technological system that lives, adapts and maintains itself using the existing environmental resources. In biosystems of all hierarchical levels starting from cells, it is possible to distinguish two essentially different, but functionally closely interconnected natural biotechnologies: (a) material-energy transformations subsystem (a controlled one) and (b) informational control subsystem (a controlling one). The first technology emerged when material-energy transformations fused with informational control to purposeful closed-loop coding-decoding control (CL-CDC). The initial technology was based on genetic informational control and enzymatic material-energy converting principles. This point of view allows interpreting of biological evolution as specific development of natural technologies of biosphere and their adaptations. Parallelism of natural (biological) and artificial (technical) technologies is seen in existence of bionics/biomimetics.

This approach to living systems emerges from the concept of modeling relation by R. Rosen and J. L. Casti [3-5], and perceptual control theory by W. Powers [6, 7]. The development of this interpretation leads to CL-CDC scheme that expresses more joint understanding of organized control systems. It is a way of reflections (coding) and re-reflections (decoding) [8-18]. It should be noted, that the living systems are a special sort of organized systems. The class of organized systems per se includes some pure technical systems (e.g., robots).

The aim of this study is to reveal and formulate the general principle of functional organization of the living systems and put the biological data in graphic schemes. The main attention has been paid to the concept of informational control, as the essence of natural and artificial technological control procedures. Here, a new concept of life as a natural technology organized on the principle of CL-CDC is presented. The CL-CDC concept is based on the following concepts: living systems theory [19-21], self-reproducing automata [22], self-referring automata and systems [2, 23, 24], eigenbehavior, semiotics and self-organization [24-26], perceptual control theory [6, 7], modeling relations [3, 4], and computing anticipatory systems with incursion and hyperincursion [27, 28].

The paradigmatic, functional and structural schemes of living systems, presented in this study, have been synthesized by holistic systemic [29], projective constructive [30], and systemic bio-engineering [31] methods on the constructive foundation [30, 32]. Methods of synthetic biology [33], analytic (or bioinformatics) biology [33], constructive biology [30], complex systems biology [35, 36], based on engineering thinking [31], are supposed to integrate different areas of research in order to create a more holistic understanding of life. Recently, the terms of synthetic systems biology have been used in a sense of science and engineering combination in order to design and build novel virtual or real biological functions and systems. In this study, the thinking in terms of synthetic systems biology has been used to design of graphic schemes representing the essential functional organization of living systems.

2. CLOSED-LOOP CODING-DECODING CONCEPT

Accordingto the CL-CD scheme, the natural system N of the real world sphere may be represented by encoding (coding) procedure on the formal (abstract, mathematical, virtual, computer) world sphere F as a model ofN. Model or formal system F operates with special rules(Fig.1).The coding procedure corresponds to observations, measurements, analysis, representations or reflections. Accordingly, the decoding procedure is de-reflection or synthesis of the natural system N under control of a model programme in the formal system F. The decoding accompanies procedures of interpretation, control, prediction, synthesis and anticipation. Here, it is necessary to mention that there are two classes of models. Thus, encoding of the system N can produce two kinds of models: (a) gnostic model F_G (intended for cognition of N) and (b) action model F_A (intended for reproduction of N or production of changes in existing N). In some systems, the models F_G can be transformed to F_A, and in some systems, the model F_A is produced directly [37].

FIGURE 1. Principle scheme of the organizationally closed, matter-energy-information open or informational closed-loop coding-decoding control (CL-CDC) procedures in the organized systems

Coding(encoding) should be understood as a reflection of a real system (nature or a technological process) in an abstract virtual form on memory structures (DNA, hormones, neural networks, programs, books, etc.) in a way that decoding from the abstract form to the real one (and purposive actions in real system) would by possible.

The coded reflection in the memory is a gnostic model or a technological project (action model) of the real system. This action model or a coded representationfor control is the essence of information. Decoding is the realization of such a project or the control of biotechnological procedures according to information. In the process of decoding, the activated coded states of the memory structures or the projects for synthesis of reality are reflected in the dynamic states of the real world, real structures of body, etc. The real world is changed according to the action model.

The full closed-loop coding-decoding system consists of complex, partially autonomic, organized systems. There are genetic, hormonal, neural, psychical, social, robotic organized systems in the world. Dualistic material↔informational mapping manifests itself in the following organized systems: signal↔information; phenotype↔genotype; body↔soul; brain↔thought; hardware↔software; biosphere↔noosphere; social group↔management; state↔government.

The systems that function according these principles are organizationally closed and informationally open. Organizational closeness causes the functional compatibility of coding-decoding and functional sense (semantics) of coded reflections. Informational openness means ability to join additional information about environment to the pool of the existing world models (the pool of gnostic and action models). This openness also means the feature of gnostic models to change according to changes of the real world. Later, these changes are transferred to the action models. These related changes of real world↔gnostic models↔action models means “informational metabolism”, in analogy with mater and energy conversion.

It is undoubtful that most elaborated control systems are a part of functional structure of biological organisms, because they are the result of natural evolution that developed these technologies during millions years of life history.

3. CLOSED-LOOP CODING-DECODING SYSTEMIC STRUCTURE

The CL-CD structure is presented in Fig. 2. It is a scheme of C.E. Shannon's informational (communication) channel with open encoding-decoding extended with real subsystem (subsystem of real material and energy processing). In this system, the informational channel is a feedback element that corresponds to a component of controlling subsystem of organized technological systems. This controlling subsystem decodes the inner action model and controls material and energetic transformations in the real (controlled) subsystem, that means the execution of purposive actions. This feature gives semantic meaning to the coding informational states of controlling subsystem.

FIGURE 2.  Closed-loop coding-decoding informational system. Classical Shannon’s coding-decoding system is open, without real (matter and energy transform) subsystem

Many organized systems, e. g. the neural system, behave anticipatory: on the basis of the present state, the controlling subsystem forms hypothetical state H_j+1 of the system model at the next time moment (extrapolates) and compares it with the necessary state of the system at the next time moment. Perceptual tools P_j are used for comparison of these states. According to the comparison of the resukts, the commands to the controlled subsystem are generated and sent. These commands initiate material changes in the controlled subsystem, that decrease or eliminate disagreements between present®hypothetical and necessary future states. States and disagreements are a part of the gnostic model that represents the real system.

All this recurrent behavior of the whole system is described by equation:

                                                                             (1)

H₀–an initial hypothesis, i. e. gnostic model of the expected state at future moment. Formulation of the gnostic model in active organized system is an anticipatory action. Model is a priori information based on the last percepted sensory data P₀  of the present system state and the earlier states;  

P₁– gnostic model of initial percepted state, that is compared with H₀;

C_j– procedure of informational comparison of states of two models [H_j-1 -P_j];

D_i – i-ties decision making, action model, where H_i-1 = P_i after one or several recurrent comparison cycles.

This equation means an algorithm of recurrent informational modeling of future states of the system and similar states of the same system after corrective actions. All organized systems that work on CL-CD principle use this algorithm. This scheme is a development of systemic ideas of H. von Forster‘s eigenbehavior[25], K. Pribram‘s Test-Operation-Test-Excite (T-O-T-E, or T-O-T-E-TO-BE) [38] and Analysis by Synthesis (A-by-S) [11, 15, 17, 18].

4. INTEGRAL TRANSFORMATIONS AS CODING-DECODING PROCEDURES

CL-CD may be represented as informational procedure of the integral transformations.

The coding is an informational procedure, where the physical space (subsystem X) with the states u(x) is transformed to another physical space ξ with states U(ξ) according to kernel function Φ(x, ξ). Then decoding is an inverse informational procedure (re-reflection or re-transformation) of the states of memory space U(ξ) on the physical space X according inverse kernel function Ψ(x, ξ) asthe state u(x’) (Fig.3.).

FIGURE 3. Integraltransformations as CODING-DECODING

Ideal coding-decoding must satisfy orthogonal set of the kernel functions

                                                                                                    (2)

Then coding-decoding as integral transformations may be expressed mathematically:

                                                                                                                      (3)

or

                                                                                                                     (4)

Eq. 3 represents non-homogeneous procedures, and Eq. 4 is convolution integrals for homogeneous ones.

This integral coding-decoding may be realized in discrete form, when kernel functions are expressed as matrix-operators [F] and [Y] that satisfy orthogonality condition

                                                                                                                                             (5)

For the particular situations (e. g., in neural nets) it is rational to use complex kernel functions:

                                      (6)

The simplest coding-decoding kernel functions are, as follows:

                                                (7)

Fourier-like functions (A) represent non-homogeneous transformations, and Frehnel-like represents the (B) homogeneous ones. These functions represent technology of coding-decoding. Fourier and Frehnel coding-decoding transformations are known for their quasi-holographic features.

The systems developed by man are much more primitive and simple ones in comparison with biological systems. So, it is interesting to sketch the structure of functional organization of biological organisms.

5. ORGANISM AS A CODING – DECODING SYSTEM

The living system as an organized system includes two functionally different subsystems: the controlling one (a controller) that processes the information, and the controlled one that carries out transformations of matter and energy for goal-orientedactions (Fig. 4). The controller in the cell is based on the system of genes, and the controller of multicellular organism is augmented by hormonal regulation. Animals have an additional controller, a nervous system. In animals, this three-level structure of control is linked to the environment by internal and external feedbacks and forms a hierarchically organized CL-CD system. Coding-decoding procedures implement the multiplication of discretically coded genetic project of the organism. Biotechnology of reproduction becomes a rather steady bioinformation technology.

Many phenomena of living nature could be explained by the terms of information technologies in connection with coding-decoding procedures:

• Spores and seeds are carriers of biotechnological projects (programs; action models) of future organisms loaded with initial supply of necessary decoding tools, substances and energy. The essence of the existence of plants, fungi and animals are replication, improvement and spreading of these projects.
• Sexual reproduction (recombination) is the diversification of these programs or projects and a part of the process of encoding in genetic controllers.
• Adaptive modifications are alternative ways of implementation of the programs.
• Gene engineering is the purposive insert of new individual components to the genetic programs.
• Apoptosis is the programmed cell demolition that is necessary for the effective dismantling of some parts of the organism.
• Morphogenesis is carried out under the informational control where action genetic models and hormonal signals are used.
• The influence of pheromones on behavior of insects is an example of the action of informational programs by special extra-organismal signal molecules.
• Activities of the nerve system that determine the behavior of animals as a response to external stimuli are the obvious products of information technologies.
• Repetition of phylogenesis in ontogenesis (biogenetic law or theory of recapitulation) is an example of persisting underlaying evolutionary programmes (an illustration of the evolution of information coding-decoding procedures).

FIGURE 4. Functional structure of the organized systems with combined feedforward and feedback informational control or external and internal closed-loop coding-decoding and animal or human functional diagramas hierarchical organized closed-looped coding-decoding (bioinformational) control system
- receptors (elementary coders), - effectors (elementary decoders)

Hormonal coordination of activitiesof multicellular organism can be explained in terms of agent theory as selective receiving of molecular signals, information processing, and decision-making for action. It is the activity of the coding-decoding systems. The more dynamic control of multicellular animal is carried out by the complexes of nerve cells, neural nets which receive, process, and send information. Obviously, the neural control of multicellular organism is a network of coding-decoding procedures.

The nerve system is a typical information coding-decoding system, which reflects and codes not only the environment of the animal, but its inner state, as well. Animals control their activities according to this information and select the optimal behavior.

6. ORGANIZED SYSTEM AS ANTICIPATORY CONTROL SYSTEM

Control systems cybernetic theory and, especially, W. Powers’ perceptual control theory explain the functional organization of living systems and correspond with CL-CD control scheme [6, 7, 19]. The general structure of organized system based on principles of CL-CD control is presented in Fig. 5.

Even the most simple organized systems based on CL-CD control principles have at least two CL-CD control circuits. The first circuit reflects information about the environment. It develops (recreates) the gnostic model of the environment that is compared with action models of the goal. On the basis of this comparison, the action models in a form of sequential commands are generated. Action models encode the necessary states of environment-body (organized system) interactions. Decoding of these models is the controlling of the environment through effectors. The second circuit collects information about inner environment and analogically develops the action models for control of inner environment by means of inner effectors. Usually, these two circuits work in tandem. This two circuits system corresponds to the cybernetic system of combined feedforward and feedback control.

FIGURE 5. Functional structure of an organized system with feedforward and feedback informational control or external and internal CL-CDC.

An organized system is a case of a complex system that has features of the cybernetic system (purposeful system), especially, if it has features of the second order cybernetic system, according to H. von Förster [25]. An organized system consists of two closely connected qualitatively different subsystems, i. e. controlling subsystem and controlled subsystem. Here, the controlling subsystem stores, collects, processes and sends information, and the controlled one handles the material and energy transformations.

M. A. Kalaidjieva and G. A. Swanson identify seven levels of complexity of intelligence in the context of living systems theory [39]. The levels of intelligence are defined on the decision making within perceptual-motor arch and the evolution of life [40]. Evolutionary cybernetic analysis of functional organization of animal nerve systems and of behavior, carried out by D. Kirvelis and K. Beitas, identified five levels of CD-CDC [15].

7. BIOINFORMATION AND BIOINFORMATICS, INFORMATION AND INFORMATICS AT LARGE

The present concept of CL-CDC allows to expand and generalize the understanding of both bioinformation and bioinformatics, as well as that of information and informatics [32]. From the technological viewpoint, each organism needs the ability to control and coordinate the purposive transformations of matter, energy, and information using various technological tools. These transformations have to be controlled by informational structures of controlling subsystems (controllers).

The first controlling information technologies, which emerged on the Earth by way of biological evolution and still keep operating in each cell, are the genetic ones, i. e. bioinformational control by genes and proteins (Fig. 4.). In metaphytes, intracellular genetic control is enhanced by hormonal means. On the level of metazoa, nervous networks are added. On the biosocial level, control has been extended by use of inter-individual communication agents (pheromons and acoustic signals). And on the level of a human society, the important and increasing role in control is played by information transmitted by oral and written languages, where graphical symbols (e. g. letters and numbers in printing, electronic medias) are used for different modeling representations. Thus, it is possible to regard information as a specific property of the organized matter arisen during biological evolution for management and coordination of technologies. In the beginning, it was natural biotechnologies. Along with the development of human society, human reason kept creating and introducing new technologies, starting with application of fire and arriving to informational technologies.

FIGURE 6. Different levels of closed-loop coding-decoding or bioinformational and informational knowledge processing in the living (biological, humanitarian and social) systems

Therefore, the substance of information theory and informatics as branches of science is the methods of quantitative estimation of information, its functional value and importance for control and management. Undoubtedly, information and bioinformation have the common roots. They differ only in a level of world organization where they work: bioinformation in biological systems and information in social and technological systems. So, informatics should be a general science both for bioinformational and informational control-managing procedures on any organized level of organized system.

Special interest from the technological point of view should be paid to understanding of generation of information. The copying (copy) of information is an informational procedure, but it creates no new information. Generation of information is important for creation of projects of new technologies. Only two bioinformational technologies in living nature produce new information (new gnostic and actional models): 1) genetic natural biotechnology, which implements stochastic testing in living populations with the subsequent natural selection (genetic algorithms), and 2) mental natural biotechnology, which implements a motivated search, creation of virtual imitatory projects aimed at the achievement of purposes with the subsequent checks, rejection or acceptance for action.

The mental natural biotechnology is carried out by special zones of brain (neocortex) of mammals and birds. The name of such mental manufacture-generation of information is the “creative work”.

8. SENSORY NEOCORTEX AS PERCEPTUAL CONTROLLER OR SUBSYSTEM ANALYSIS BY SYNTHESIS

In order to reveal the functional organization of neocortex, the visual analyzer has been explored as an example [10, 11, 15, 18]. Five levels of CL-CD control can be seen in mammal's visual analyzer [15]:

• Simple reflection;
• Multireflexic coordination and programmed control;
• Regulation and homeostasis;
• Simple perceptronic analysis;
• Analysis-by-Synthesis (A-by-S) without or with “sensory screens”.

The visual analyzers of the animals and various visual structures of human brain display the above mentioned levels of anticipatory control: on-off motoric reactions of the jelly-fish or of human eye lids to light; coordination of earthworm movement or control of human head turning to light; regulation of the eyes pupils and lenticuli in reptiles and human; simple perceptronic analysis and motoric response to the visual situation in frog or control of the human eyes look to the light spot; visual A-by-S in the sensory structures of neocortex Area Striata of the mammals and particularly man.

Receptoric structures code the environment images and their changes, and send coded information to primary visual zones of neocortex. Here, properties of visual images are analyzed in detail. Results of the analysis are used for primary perceptronic recognition, for comparison with reconstructed image from memory and for registering in memory structures. The essential specifics of visual neocortex is that it continually analyses images transferred from retina and compares them with anticipated images that are collected from memory on basis of environmental situation and motivations. This cyclic A-by-S or internal distinctly anticipatory CL-CD control procedure carries out the imitative cognitive modeling. The results are used in generation of pragmatic behavior models for current and future actions.

FIGURE 7. Hierarchical neural networks structures in the mammals

All this activities are used for the following tasks:

• to generate gnostic (knowledge cognitive) models that must correspond to reality as exactly as possible. The role of motivation here is to shorten search for most corresponding models in memory.
• to generate action (pragmatic) models that is used to change reality. The reality is changed for correspondence plans (action models of goal class) generated in mind. The feature of generation of strategic plans is especially characteristic for human mind.

Maybe, this imitative A-by-S with CL-CD feedbacks is an essence of thinking, because thinking is a mental creation of schemes (point of view of cognitive psychology) that possibly is implemented in structures of neocortex.

Supposedly, all these behavior forms are related to special kinds of neurostructures in animal and correlates with emergence of new principles in informational control during evolution (Fig. 7).

The general scheme of functional organization of information processing by visual analyzer has been constructed on the basis of interpretation of visual perception, anatomic and morphological structure of visual subsystems of animals, as neuro-physiological, psychological, and psycho-physiological data, in the light of theoretical solutions of image recognition, and simulation of visual perception processes (Fig. 8). The scheme reflects the active information processing. The activities in special areas of neocortex are as follows: focused attention, analysis of visual scenes with prediction, synthesis of predictive mental images, and comparison of visual scenes with predictive mental images.

FIGURE 8. Functional structure of sensory neocortex as Analysis by Synthesis (A-by-S) or imitative Closed-Loop Coding-Decoding (CL-CD) with Neural CHAOS Memory

The functional organization of neuron layers of Brodmann area 16 in primary visual zone V1 of mammals is especially interesting from a viewpoint of information processing. Morphological, neurophysiologic and computational research generated a great amount of experimental data and created many theoretical models, but principles of organization and functioning of the area 16 are still very hazy. Interpretations of functioning of Area Striate might enclear the CL-CDC principles of neocortex in visual thinking and general thinking procedure as information processing.

In the projection zone of visual cortex Area Striata or V1, a “sensory” screen (SS) and “reconstruction” or synthesis screen (RS) are supposed to exist. The visual analysis consists of the following components: analysis of visual scenes projected onto SS; “tracing” of images; preliminary recognition; reverse image reconstruction onto RS; comparison of images projected onto SS with images reconstructed onto RS; and “correction” of preliminary recognition. It is supposed, that periodical procedures of coding-decoding are carried out by the quasi-holographic image “tracing” and reverse image reconstruction. Receptoric structures code the environment images and their changes and after local filtering send coded information to primary visual zones of neocortex. Here, properties of visual images are analyzed in detail. The results of analysis are used for primary perceptronic recognition, comparison with reconstructed image from memory and registration in memory structures. The essential trait of visual neocortex is that it continually analyses images transferred from retina and compares them with anticipated images that are collected from memory on the basis of environmental situation and motivations. This cyclic A-by-S or internal distinctly anticipatory CL-CD control procedure carries out the imitative cognitive modeling. Gnostic models are created or modified in this process.

The results (gnostic models) are used in generation of action models (pragmatic behavior models) for current and future actions.

A full dynamical perceptual control system of coding-decoding in visual analyzer is a closed loop because an actual image is compared with reconstructed one from memory (Fig. 8.). This system consists of closed-loop space/time coding-decoding structures that correspond to reflective local Hermite-Laguerre-like coder and decoder structures. This property gives functional sense for information in visual perception system.

We propose that the neuronal structure implementing the quasi-holographic analysis-by-synthesis ought to possess at least ten functional layered complexes: the receptor layer (1), where the retinal image is projected; layer of local filtering (2); local Hermite-Laguerre like analyzer (3) and local Hermite-Laguerre like synthesizer (4) with comparator (5) between them. These structures are looped by quasi-holographic memory layered complexes Q^-1 and Q⁺¹ (6 and 7) with CHAOS memory neural structure (8) controlled by systemic perceptron-like classificator (9) in-between them. The memory tracesareextracted by means of the topological transformations structure (10) controlled by signals from the comparator. The comparison block collates actual signal of local analysis and mental image of local synthesis. The synthesis may be accomplished by dedicated predictive structures driven by arbitrary motivations or preliminary expectations of events in environment.

This model is based on both the visual psychophysical and neurobiological data, interpreted in the light of the theoretical solutions of image recognition and visual. The functioning of visual analyzer according this model consists of the following stages:

• projection of retinal image (image arriving from retina) to the neurons networks that realize local Hermite-Laguerre like analysis; the actual images are quasi-holographically transformed and recorded to chronological searchable CHAOS memory (with systemic search catalogue);
• projection of mental image from the CHAOS memory (searched for in catalogue, extracted, decoded by inverse quasi-holographic transformations and topologically transformed) to the local Hermit-Laguerre-like as synthesis structure, that operate inverse integral transform;
• comparison of real image and hypothetic mental images, and detection of mismatching features; additional rotation, shift and other topological transformations are used in comparison;
• decision or recognition is based on the preliminary recognition and involves an iterative formulation of image identity hypothesis, which leads to synthesis of an image of the new object; the iterative procedures last until the correspondence between the actual object and its retrieved hypothetical image is achieved.

It could be suggested, that this imitative A-by-S with CL-CD feedbacks is an essence of thinking, because thinking is mental creation of models (point of view of cognitive psychology) that is implemented in structures of neocortex [11, 17].

A similar functional structure is characteristic to biological and social systems. The possible prospects and problems of information, knowledge and creative society (IKCS) can be analyzed indirectly through analysis of functional evolution of biological and neural system. Such analysis can be valuable as an instrument of prognosis of development of IKCS and finding of most prospective directions in development of IKCS. We can expect that the biological evolution has found many valuable ways of information processing and functional organization that can be implemented in human technical and social creative technologies.

9. CONCLUSIONS

1. The Living Systems could be regarded as complex systems of the organized technologies (organized systems).
2. Biological information implements control on vital technological procedures in the living systems.
3. The Closed-Loop Coding-Decoding Control (CL-CDC) concept is a potential basis for understanding and explaining organization of the living systems.
4. The animals are entities with obvious anticipatory multilevel control systems.
5. Nervous system can be interpreted as controller with anticipatory control principles when all animal is interpreted as organized control system consisting of two subsystems (controller and controlled) that are closely coupled by informational closed-loop coding-decoding procedures.
6. Functional evolution of nervous system is an evolution of anticipatory control systems.
7. Organizational coding-decoding closeness causes the functional compatibility and functional sense (semantics) of coded technological reflections.
8. Five levels of anticipatory control can be seen in animal visual analyzer: simple reflection; multireflexic coordination and programmed control; regulation and homeostasis; simple perceptronic analysis; analysis by synthesis without or with “sensory screens”.
9. Analysis by synthesis or imitatory closed-loop coding-decoding of the sensory neocortex of the mammals and avifauna permits thinking, generates information, and creates projects for actions.
10. Only the fifth level of anticipatory control (analysis by synthesis) represents full anticipatory (or model-based) control system that can be simulated as hyperincursive computer program. Other levels of the control represent anticipatory features that generate predictions of lower or higher level, that can be simulated as incursive computer programs.

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