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Single cell interactomics in simpler organisms, as well as somatic cell interactomics in multicellular organisms, involve biomolecular interactions in complex signalling pathways that were recently represented in modular terms by quantum automata with ‘reversible behavior’ representing normal cell cycling and division. Other implications of such quantum automata, modular modeling of signaling pathways and cell differentiation during development are in the fields of neural plasticity and brain development leading to quantum-weave dynamic patterns and specific molecular processes underlying extensive memory, learning, anticipation mechanisms and the emergence of human consciousness during the early brain development in children. Cell interactomics is here represented for the first time as a mixture of ‘classical’ states that determine molecular dynamics subject to Boltzmann statistics and ‘steady-state’, metabolic (multi-stable) manifolds, together with ‘configuration’ spaces of metastable quantum states emerging from complex quantum dynamics of interacting networks of biomolecules, such as proteins and nucleic acids that are now collectively defined as quantum interactomics. On the other hand, the time dependent evolution over several generations of cancer cells --that are generally known to undergo frequent and extensive genetic mutations and, indeed, suffer genomic transformations at the chromosome level (such as extensive chromosomal aberrations found in many colon cancers)-- cannot be correctly represented in the ‘standard’ terms of quantum automaton modules, as the normal somatic cells can. This significant difference at the cancer cell genomic level is therefore reflected in major changes in cancer cell interactomics often from one cancer cell ‘cycle’ to the next, and thus it requires substantial changes in the modeling strategies, mathematical tools and experimental designs aimed at understanding cancer mechanisms. Novel solutions to this important problem in carcinogenesis are proposed and experimental validation procedures are suggested. From a medical research and clinical standpoint, this approach has important consequences for addressing and preventing the development of cancer resistance to medical therapy in ongoing clinical trials involving stage III cancer patients, as well as improving the designs of future clinical trials for cancer treatments. KEYWORDS: Emergence of Life and Human Consciousness; Proteomics; Artificial Intelligence; Complex Systems Dynamics; Quantum Automata models and Quantum Interactomics; quantum-weave dynamic patterns underlying human consciousness; specific molecular processes underlying extensive memory, learning, anticipation mechanisms and human consciousness; emergence of human consciousness during the early brain development in children; Cancer cell ‘cycling’; interacting networks of proteins and nucleic acids; genetic mutations and chromosomal aberrations in cancers, such as colon cancer; development of cancer resistance to therapy; ongoing clinical trials involving stage III cancer patients’ possible improvements of the designs for future clinical trials and cancer treatments.

A novel approach to self-organizing, highly-complex systems (HCS), such as living organisms and artificial intelligent systems (AIs), is presented which is relevant to Cognition, Medical Bioinformatics and Computational Neuroscience. Quantum Automata (QAs) were defined in our previous work as generalized, probabilistic automata with quantum state spaces (Baianu, 1971). Their next-state functions operate through transitions between quantum states defined by the quantum equations of motion in the Schroedinger representation, with both initial and boundary conditions in space-time. Such quantum automata operate with a quantum logic, or Q-logic, significantly different from either Boolean or Łukasiewicz many-valued logic. A new theorem is proposed which states that the category of quantum automata and automata--homomorphisms has both limits and colimits. Therefore, both categories of quantum automata and classical automata (sequential machines) are bicomplete. A second new theorem establishes that the standard automata category is a subcategory of the quantum automata category. The quantum automata category has a faithful representation in the category of Generalized (M,R)--Systems which are open, dynamic biosystem networks with defined biological relations that represent physiological functions of primordial organisms, single cells and higher organisms.

It is here proposed an analysis of symbolic and sub-symbolic models for studying cognitive processes, centered on emergence and logical openness notions. The Theory of logical openness connects the Physics of system/environment relationships to the system informational structure. In this theory, cognitive models can be ordered according to a hierarchy of complexity depending on their logical openness degree, and their descriptive limits are correlated to Gödel-Turing Theorems on formal systems. The symbolic models with low logical openness describe cognition by means of semantics which fix the system/environment relationship (cognition in vitro), while the sub-symbolic ones with high logical openness tends to seize its evolutive dynamics (cognition in vivo). An observer is defined as a system with high logical openness. In conclusion, the characteristic processes of intrinsic emergence typical of “bio-logic” - emerging of new codes-require an alternative model to Turing-computation, the natural or bio-morphic computation, whose essential features we are going here to outline.

In this paper we show how a simulated anthropomorphic robotic arm controlled by an artificial neural network can develop effective reaching and grasping behaviour through a trial and error process in which the free parameters encode the control rules which regulate the fine-grained interaction between the robot and the environment and variations of the free parameters are retained or discarded on the basis of their effects at the level of the global behaviour exhibited by the robot situated in the environment. The obtained results demonstrate how the proposed methodology allows the robot to produce effective behaviours thanks to its ability to exploit the morphological properties of the robot’s body (i.e. its anthropomorphic shape, the elastic properties of its muscle-like actuators, and the compliance of its actuated joints) and the properties which arise from the physical interaction between the robot and the environment mediated by appropriate control rules.

We investigate the behavioral patterns of a population of agents, each controlled by a simple biologically motivated neural network model, when they are set in competition against each other in the Minority Model of Challet and Zhang. We explore the effects of changing agent characteristics, demonstrating that crowding behavior takes place among agents of similar memory, and show how this allows unique `rogue' agents with higher memory values to take advantage of a majority population. We also show that agents' analytic capability is largely determined by the size of the intermediary layer of neurons. In the context of these results, we discuss the general nature of natural and artificial intelligence systems, and suggest intelligence only exists in the context of the surrounding environment (embodiment). Source code for the programs used can be found at http://neuro.webdrake.net/.

Why are attentional processes important in the driving of anticipations? Anticipatory processes are fundamental cognitive abilities of living systems, in order to rapidly and accurately perceive new events in the environment, and to trigger adapted behaviors to the newly perceived events. To process anticipations adapted to sequences of various events in complex environments, the cognitive system must be able to run specific anticipations on the basis of selected relevant events. Then more attention must be given to events potentially relevant for the living system, compared to less important events. What are useful attentional factors in anticipatory processes? The relevance of events in the environment depend on the effects they can have on the survival of the living system. The cognitive system must then be able to detect relevant events to drive anticipations and to trigger adapted behaviors. The attention given to an event depends on i) its external physical relevance in the environment, such as time duration and visual quality, and ii) on its internal semantic relevance in memory, such as knowledge about the event (semantic field in memory) and anticipatory power (associative strength to anticipated associates). How can we model interactions between attentional and semantic anticipations? Specific types of distributed recurrent neural networks are able to code temporal sequences of events as associated attractors in memory. Particular learning protocol and spike rate transmission through synaptic associations allow the model presented to vary attentionally the amount of activation of anticipations (by activation or inhibition processes) as a function of the external and internal relevance of the perceived events. This type of model offers a unique opportunity to account for both anticipations and attention in unified terms of neural dynamics in a recurrent network.

Why anticipatory processes correspond to cognitive abilities of living systems? To be adapted to an environment, behaviors need at least i) internal representations of events occurring in the external environment; and ii) internal anticipations of possible events to occur in the external environment. Interactions of these two opposite but complementary cognitive properties lead to various patterns of experimental data on semantic processing. How to investigate dynamic semantic processes? Experimental studies in cognitive psychology offer several interests such as: i) the control of the semantic environment such as words embedded in sentences; ii) the methodological tools allowing the observation of anticipations and adapted oculomotor behavior during reading; and iii) the analyze of different anticipatory processes within the theoretical framework of semantic processing. What are the different types of semantic anticipations? Experimental data show that semantic anticipatory processes involve i) the coding in memory of sequences of words occurring in textual environments; ii) the anticipation of possible future words from currently perceived words; and iii) the selection of anticipated words as a function of the sequences of perceived words, achieved by anticipatory activations and inhibitory selection processes. How to modelize anticipatory semantic processes? Localist or distributed neural networks models can account for some types of semantic processes, anticipatory or not. Attractor neural networks coding temporal sequences are presented as good candidate for modeling anticipatory semantic processes, according to specific properties of the human brain such as i) auto-associative memory; ii) learning and memorization of sequences of patterns; and iii) anticipation of memorized patterns from previously perceived patterns.

Fundamental approaches bypass the problem of getting consciousness from non-conscious components by positing that consciousness is a universal primitive. For example, the double aspect theory of information holds that information has a phenomenal aspect. How then do you get from phenomenal information to human consciousness? This paper proposes that an entity is conscious to the extent it amplifies information, first by trapping and integrating it through closure, and second by maintaining dynamics at the edge of chaos through simultaneous processes of divergence and convergence. The origin of life through autocatalytic closure, and the origin of an interconnected worldview through conceptual closure, induced phase transitions in the degree to which information, and thus consciousness, is locally amplified. Divergence and convergence of cognitive information may involve phenomena observed in light e.g. focusing, interference, and resonance. By making information flow inward-biased, closure shields us from external consciousness; thus the paucity of consciousness may be an illusion.

In attempts to formulate a computational understanding of brain function, one of the fundamental concerns is the data structure by which the brain represents information. For many decades, a conceptual framework has dominated the thinking of both brain modelers and neurobiologists. That framework is referred to here as "classical neural networks." It is well supported by experimental data, although it may be incomplete. A characterization of this framework will be offered in the next section. Difficulties in modeling important functional aspects of the brain on the basis of classical neural networks alone have led to the recognition that another, general mechanism must be invoked to explain brain function. That mechanism I call "binding." Binding by neural signal synchrony had been mentioned several times in the liter ature (Lege´ndy, 1970; Milner, 1974) before it was fully formulated as a general phenomenon (von der Malsburg, 1981). Although experimental evidence for neural syn chrony was soon found, the idea was largely ignored for many years. Only recently has it become a topic of animated discussion. In what follows, I will summarize the nature and the roots of the idea of binding, especially of temporal binding, and will discuss some of the objec tions raised against it.

The present article is an attempt to give - in the frame of the theory of the organism-environment system (Jarvilehto 1998a) - a new interpretation to the role of efferent influences on receptor activity and to the functions of senses in the formation of knowledge. It is argued, on the basis of experimental evidence and theoretical considerations, that the senses are not transmitters of environmental information, but they create a direct connection between the organism and the environment, which makes the development of a dynamic living system, the organism-environment system, possible. In this connection process the efferent influences on receptor activity are of particular significance, because with their help the receptors may be adjusted in relation to the parts of the environment which are most important in the achievement of behavioral results. Perception is the process of joining of new parts of the environment to the organism-environment system; thus, the formation of knowledge by perception is based on reorganization (widening and differentiation) of the organism-environment system, and not on transmission of information from the environment. With the help of the efferent influences on receptors each organism creates its own peculiar world which is simultaneously subjective and objective. The present considerations have far reaching influences as well on experimental work in neurophysiology and psychology of perception as on philosophical considerations of knowledge formation.

During embryonic morpho-genesis, a collection of individual neurons turns into a functioning network with unique capabilities. Only recently has this most staggering example of emergent process in the natural world, began to be studied. Here we propose a navigational strategy for neurites growth cones, based on sophisticated chemical signaling. We further propose that the embryonic environment (the neurons and the glia cells) acts as an excitable media in which concentric and spiral chemical waves are formed. Together with the navigation strategy, the chemical waves provide a mechanism for communication, regulation, and control required for the adaptive self-wiring of neurons.

The recently introduced Static and Dynamic State (SDS) Feedback control scheme together with its modified form, the Data Compression and Reconstruction (DCR) architecture that performs pseudoinverse computation, suggests a unified model of cortical processing including consciousness. The constraints of the model are outlined were and the features of the cortical architecture that are suggested and sometimes dictated by these constraints are listed. Constraints are imposed on cortical layers, e.g., (1) the model prescribes a connectivity substructure that is shown to fit the main properties of the `basic neural circuit' of the cerebral cortex (Shepherd and Koch, 1990, Douglas and Martin 1990). In: The synaptic organization of the brain, Oxford University Press, 1990), and (2) the stability requirements of the pseudoinverse method offer an explanation for the columnar organization of the cortex. Constraints are also imposed on the hierarchy of cortical areas, e.g., the proposed control architecture requires computations of the control variables belonging to both the `desired' and the experienced' moves as well as a `sign-proper' separation of feedback channels that fit known properties of the basal ganglia -- thalamocortical loops (Lorincz, 1997). An outline is given as to how the DCR scheme can be extended towards a model for consciousness that can deal with the `homunculus fallacy' by resolving the fallacy and saving he homunculus as an inherited and learnt partially ordered list of preferences.

The development of the issue of binding as fundamental to neural dynamics has made possible recent advances in the modeling of difficult problems of perception and brain function. Among them is perceptual segmentation, invariant pattern recognition and one-shot learning. Also, longer-term conceptual developments that have led to this success are reviewed.

Le modele d'ancrage propose ici est simple a recapituler. Les projections sensorielles analogiques sont les intrants des reseaux neuronaux qui doivent apprendre a connecter certaines des projections avec certains symboles (le nom de leur categorie) et certaines autres projections avec d'autres symboles (les noms d'autres categories pouvant se confondre les unes aux autres), en trouvant et en utilisant les invariants qui les representent de facon a favoriser l'accomplissement d'une categorisation juste. Les symboles ancres sont alors enfiles dans des combinaisons d'ordre superieur (descriptions symboliques ancrees) par un deuxieme processus combinatoire qui presente une difference critique a l'egard de la manipulation symbolique classique. Dans la manipulation symbolique standard (non ancree), la syntaxe est la seule contrainte a laquelle les combinaisons de symboles sont soumises et elle s'applique a la configuration (arbitraire) des symboles. Dans un systeme symbolique ancre, on doit tenir compte d'une deuxieme contrainte, celle de la forme non arbitraire des invariants sensoriels qui connectent le symbole a la projection sensorielle analogique de l'objet auquel il se rapporte. Je ne peux m'etendre sur la nature de ces systemes symboliques ancres a double contrainte , si ce n'est que pour indiquer que la perception categorielle humaine peut apporter quelques indices quant a la nature de cette interaction entre les contraintes analogiques et syntaxiques.

Harnad's main argument can be roughly summarised as follows: due to Searle's Chinese Room argument, symbol systems by themselves are insufficient to exhibit cognition, because the symbols are not grounded in the real world, hence without meaning. However, a symbol system that is connected to the real world through transducers receiving sensory data, with neural nets translating these data into sensory categories, would not be subject to the Chinese Room argument. Harnad's article is not only the starting point for the present debate, but is also a contribution to a longlasting discussion about such questions as: Can a computer think? If yes, would this be solely by virtue of its program? Is the Turing Test appropriate for deciding whether a computer thinks?

The representation of biological systems in terms of organismic supercategories, introduced in previous papers by Baianu et al. (Bull. Math. Biophysics,30, 625–636;31, 59–70) is further discussed. To state more clearly this representation some new definitions are introduced. Also, some necessary changes in axiomatics are made. The conclusion is reached that any organismic supercategory has at least one superpushout, and this expresses the fact that biological systems are multistable. This way a connection between some results of Rashevsky’s theory of organismic sets and our results becomes obvious.