CHAPTER 5 AUTOPOIESIS AND FRENCH THEORY

Transcription

1 CHAPTER 5 AUTOPOIESIS AND FRENCH THEORY AUTOPOIESIS A systemic perspective, and borrowed from the discipline of biology, autopoiesis defines the way life self-produces, rather than getting caught up solely in the traditional sense to reproduce. Although, it is eclectic in its present day usage across disciplines, historically, it was confined to cognitive science and replaced the external observer's viewpoint with a deliberation on an internal relativistic understanding, or an observer who was supposedly embedded within the system. Autopoietic thought is marked by structural closures, in that the states the biological entities find themselves in are triggered by external/environmental perturbations, but not determined by these perturbations. These are determined by selforganizing principles. Self-organization within a system is a result of complex interactions that are guided by the rules and regulations of nonlinear mathematics. The perturbations on an organism from the environment trigger state trajectories, which decide the regularities of state cycles, if the triggers are themselves regular. But, this is not a one way process, since, in all likelihoods, the organisms could affect the environment in their own partial or full right, thus exhibiting a catalysis of evolution that travels in two ways, or in short, called co-evolution of not just the system, but also the An autopoietic system is to be contrasted with an allopoietic system, such as a car factory, which uses raw materials (components) to generate a car (an organized structure) which is something other than itself (the factory). 212

2 environment. Such a thesis deviates from its tracks, if we have to account for the components in addition to the interactions, since, in cases like these, adaptations make their presence felt. Obviously, what is clear is the varying intensity of co-evolution, since, affordance of ignoring the reactions of system and environment in their own way is a predicament. These components are autocatalytic in nature, meaning that they take in resources from lower-level components, and recreate themselves using dissipative processes, thus closing the system off from the surrounding environment on the one hand, and shunning the procedures involving reproduction on the other. Such selfsustaining or homeostatic systems are way better for life, as compared with the so-called illogicality of defining life through reproductive procedures, and is therefore aptly taken up by the likes of Maturana and Varela. And, this is precisely the reason, why the notion of co-evolution is often referred to as systems being structurally coupled. This structural coupling is determinant of the state of historical posterity handed down, and is thus considered as a combination of evolutionary procedures and development processes. Not only this, even learning is a form of structural coupling, and thus is a far cry from modeling based on a common/traditional mechanism. An opposite aspect here is the process of decoupling, where the systems are strongly self-consistent, and hence undergoes a break from the environment in that they do not impact the environment as should be suggested by the likes of an observer who understands the system by positioning herself externally. This notion is given such a pivotal importance that for some biologists like Maturana and Varela, it defines the essence of what life is all about. In the former's viewpoint, it expresses the center of constitutive dynamics of living systems. To attain such dynamism, the living systems draw on their resources from the environment they are embedded in. In short, they exhibit not only their autonomy, but also their dependency, thus giving rise to a paradoxical situation, which clearly misses out on the crucial point of their study, if done through the traditional means of linear thought. If the main purpose of linear thought is analytical, then by default, the point of dynamism 213

3 ruptures, and the only way to come out clean of this paradoxical scenario is to think of living organisms in terms of self-producing machines. What would such a description amount to? To begin with, at the very least, these self-producing machines are circularly productive, that they not only produce, but also are the product of their own productions. The productive circularity is a result of components that build up the system, and must fulfill the following criteria to be at the behest of autopoiesis, (i) through their interactions and transformations continuously they regenerate and realize the network of processes (relations) that produced them; and (ii) they constitute it (the system) as a concrete unity in the space in which they (the components) exist by specifying the topological domain of its realization as such in a network (Maturana & Varela 1980: 78). A biological cell is a canonical example of an autopoietic system. The eukaryotic cell, for example is composed of biochemical components like nucleic acids and proteins, and is organized into bounded structures such as the cell nucleus, various organelles, a cell membrane and cytoskeleton. These structures, based on an external flow of molecules and energy, produce the components which, in turn, continue to maintain the organized bounded structure that gives rise to these components. 214

4 (figure 9) In matters of cognition and language, structural coupling is instrumental in crafting a domain, that itself finds difficulty in decoupling from the historically learned instructions, and thereby, it becomes all the more wrong to call on autopoietic systems to consider brain as an information processor, and language as instructive interaction, since these considerations are based upon an external observer's viewpoint, and hence miss the tenacity of internal descriptive symbol manipulative systems. The tedious task of identifying an autopoietic system aggravates, if one misses the crux of identifying these systems as such. The identification or the checklist for a system to be autopoietic was provided by Maturana, Varela and Uribe (1974). This is a 6-point Figure courtesy: Brazma, Parkinson, Schlitt and Shojatalab. A quick introduction to elements of biology - cells, molecules, genes, functional genomics, microarrays. < First published Jul 2,

5 checklist, which involves a step-by-step procedure in evaluating autopoiesis, and though concisely laid out, a caveat of translation into clarity should not be left out. 1. Determine if the unity has identifiable boundaries. In other words, check, if it is discrete; if it exhibits extent; if it is capable of being circumscribed; or, is a specification regarding the stopping of unity and the beginning of environs possible? If the answer is in the affirmative, then step 2 should be taken up, otherwise, the first step is terminated, and this is what for Maturana, Varela and Uribe (1974: 192) is the indescribability of unity, as nothing can be said about it. 2. Determine, if there are constitutive elements of the unity, that is, components of the unity. In other words, check, if the unity can be seen as a set of parts, or if these parts comprise the whole? If the answer is in the affirmative, then step 3 should be taken up, else, the unity is an unanalyzable whole, and therefore not an autopoietic system (Maturana, Varela and Uribe 1974: 192). 3. Determine, if the unity is a mechanistic system, or that the components' properties are capable of satisfying certain relations that determine in the unity the interactions and transformations of these components. In other words, check, if the unity is as it is because of its components' interactions, and not simply due to its components' individual properties? If the answer is in the affirmative, the step 4 should be taken, else the unity does not satisfy the conditions for an autopoietic system. 4. Determine, if the components that constitute the boundaries of the unity constitute these boundaries through preferential neighborhood relations and interactions between themselves, as determined by their properties in the space of their The generic term denoting the organization characterizing autopoietic machines / systems. The term "... simply means processes interlaced in the specific form of a network of productions of components which realizing the network that produced them constitute it as a unity." (Maturana & Varela, 1980, p. 80), or "the relations that define a system as a unity, and determine the dynamics of interaction and transformations which it may undergo as such a unity..." (Maturana & Varela, 1980, p. 137). A mechanistic system is characterized by the observer implicitly or explicitly accepting that the properties of the system to be explained are generated by relations of the components and are not to be looked for among the properties of those components, thus making the relations between the components to be highly necessary. 216

6 interactions (Maturana, Varela and Uribe 1974: 193). In other words, check, if the components in the apparent boundary, participating in that boundary the result of their interrelations and interactions; or are these components in the apparent boundary discernible as such because of their participation in the processes of the composite unity? If the answer is in the affirmative, then step 5 should be taken, else, one does not possess an autopoietic unity, because one is determining its boundaries and not its unity. 5. Determine if the components of the boundaries of the unity are produced by the interactions of the components of the unity, either transformation, or previously produced components, or by transformations and/or coupling of non-component elements that enter the unity through its boundaries (Maturana, Varela and Uribe 1974: 193). In other words, check, if the components of the apparent boundary are produced by the processes comprising the unity itself?, or is the unity generating the components of this apparent boundary, either from existing components or accreting elements it appropriates from its environment? If the answer is in the affirmative, then step 6 should be taken, else, one does not have an autopietic system. 6. Determine if all the other components in the unity are also produced by the interactions of its components as in step 5, and if those which are not produced by the interactions of other components participate as necessary permanent constitutive components in the production of other components (Maturana, Varela and Uribe 1974: 193). In other words, check if all the components of the unity produced by its components realize processes within the unity itself?, or if all the unity's components participate in the production of its components? If the answer is in the affirmative, then one is in possession of an autopoietic unity in the space in which its components exist, otherwise, in the words of the authors, [If] "... there are components in the unity not produced by components of the unity as in 5, or if there are components of the unity which do not participate in the production of other components, you do not have an autopoietic unity" (Maturana, Varela and 217

7 Uribe 1974: 193). Once these steps are checked for their affirmations, it becomes quite clear that the notion of autopoiesis is not just confined to biological entities, but cognition, and even social institutions form a part within the same. The words of Humberto Mariotti, a Brazilian psychiatrist and psychotherapist echo precisely this, The concept of autopoiesis has long surpassed the realm of biology. It has been used in areas so diverse as sociology, psychotherapy, management, anthropology, organizational culture, and many others. This circumstance transformed it in a very important and useful instrument for the investigation of reality. Years ago, Chilean scientists Humberto Maturana and Francisco Varela proposed the following question: to what extent human social phenomenology could be seen as a biological phenomenology? Autopoietic systems are self-sustaining wholes, but where they are loosely defined in terms of vague open boundaries, like the human systems, autopoietic systems are then referred to as sympoietic systems. Both these systems stand in contrast to heteropoietic systems that are externally sustained or planned systems and allopoietic systems which are characterized with unsustainability and are throughput-based systems. The crux of autopoiesis lies in the fact that life could be understood at all levels, since the notion stresses on an action within an environment, thus inviting the self-organization mode of complexity thought that emphasizes upon the co-evolution of system and environment. Or, as Lucas would have it, Whilst autopoiesis usually does not incorporate the complexity concept of dynamical attractors it uses the same idea of limited flexibility due to structural Mariotti, H. Autopoiesis, Culture and Society. < First published May 26, Lucas, C. Autopoiesis and Coevolution. < First published Mar Current page version Jun

8 connectivity, along with the need to change structure if we are to develop new modes of behaviour. No mechanism is generally suggested however to drive these structural changes and in this respect complexity thought goes beyond this field, allowing for internal mutation or recombination to generate emergent metastable options for subsequent testing against environmental response. Autopoiesis remains however a valuable perspective with which to understand the essential nature of the interplay between any system and its current (and ever changing) environment. The notion of autopoiesis is quite bold in its claims in general, and hence a plethora of criticisms are not surprising. The major one is the difficulty in comprehending the definition of the term in cases pertaining to life and even social constructions. As there is a lack of clear definition and due to the extreme usage of language of self-referentiality, without any external reference, it becomes nothing more than an attempt to give substantiality to Maturana's radical constructivist or solipsistic epistemology (Swenson 1992a). An autopoietic model in Swenson's (1992b) words is, miraculously decoupled from the physical world by its progenitors... (and thus) grounded on a solipsistic foundation that flies in the face of both common sense and scientific knowledge. A closely associated term with autopoiesis is self-organization, although Maturana disagrees on any kind of synonymy, for the sake of brevity, in the thesis, they shall be used synonymously. Self-organization deliberates on the appearance of a pattern within a system This is what Danilo Zolo (quoted in Wolfe 1998) refers to as desolate theology, which is reflected in the assertion by Maturana and Varela, when they say, what we do not see does not exist (Maturana and Varela 1988: 242). Maturana (1987: 71) himself stated he would never use the notion of self-organization, because it cannot be the case... it is impossible. That is, if the organization of a thing changes, the thing changes. 219

9 without the influence of a central authority, or an external force trying to impose the said pattern. Therefore, how the pattern emerges, is purely the result of internal interactions of the components that go on to make the system, and the organization that builds up is based not only on parallel distribution, since the components act at the same time, but also along distributed lines, since there is no component that acts in a privileged position. This might appear counter-intuitive to begin with. Counter-intuitive because, left to themselves, the systems would be taken over by stochasticity, and if on the contrary there appears order, then the only way to attribute this order is to invoke an agency responsible for bringing it about. The complexity of a system has got to be measured by the amount of information required to predict its future behavior. How is this determined? This is dependent on the organization of the processes, and on its causal architecture. The higher the complexity, equally proportional is the effective numerical parts in the architecture. If this premiss gets satisfied, then an autonomous system that gets an input from the outside, self-organizes itself in case of rising complexity. Self-organization has also been conflated with the idea of emergence, and indeed one can occur without the other, thus nullifying the thesis of strong reliance between the two. Moreover, western philosophical traditions have been quite vocal in their skepticism about emergence and order within a structure, if there isn't a presence of an external agency, either in the form of God, or in some a priori principle. But these traditions are indeed in for a rude shock, since there is nothing mystical about emergence and even self-organization (in cases where they are thought to be in conflated usage). Not just an absence of mysticism characterizing self-organization, but, even stochasticity seems to be a missing link in the said principle. Although, examples supporting the case vary according to the diverse environmental factors and complexity inherent in the system, the ease of working through becomes apparent, if self-organization or autopoiesis is viewed as a the capacity exhibited by the complex systems in enabling them to change or develop the internal structure spontaneously, while adapting and manipulating with their environment in the ongoing process. This could very well be the starting point in line 220

10 with a working definition of autopoiesis. A clear example of this kind would be the human brain (although, brains of animals could suffice this equally well), which shows a great proclivity to learn, to remember in the midst of its development. Language is another instance, since in its development, a recognition of its structure is mandated, and this very structure in its attempt to survive and develop further under circumstances that are variegated, must show allegiance to adaptability. Even if, language is guided by social interactions between humans, the cultural space conducive for its development would have strong affinity to a generalized aspect of linguistic system. Now, let us build up on the criteria of determining what makes the system autopoietic, and thereby see what are the features that are generally held to be in common with autopoietic systems. Self-organization abhors predetermined design, thus enabling the system to dynamically adapt to the regular/irregular changes in the environment in a nonlinear adherence. Even if emergence can occur without the underlying principle of selforganization, and vice versa, self-organization is in itself an emergent property of the system as a whole, with the individual components acting on local information and general principles. This is crucial, since the macroscopic behavior emerges out of microscopic interactions that in themselves are carriers of scant information, and in turn have a direct component of complexity associated with them when viewed microscopically. This complexity also gets reflected in their learning procedures, since for systems that are self-organizing, it is only the experiential aspects from previous encounters compared with the recent ones that help. And what would this increase in complexity entail? As a matter of fact, complexity is a reversal of entropy at the local level, thus putting the system at the mercy of a point of saturation. Moreover, since the systems are experiential, they are historical and hence based on memory. If such is the case, then it is safe to point out that these systems are diachronic in nature, and hence memory forms a vital component of emergence. Memory as anamnesis is unthinkable without selective amnesia, for piling up information does trade off with relevance of information simultaneously. For information that goes under the name of irrelevant, is 221

11 simply jettisoned, and the space created in this manner is utilized for cases pertaining to representation. Not only representation sort of makes a back door entry here, it is also convenient for this space to undergo a systematic patterning that is the hallmark of these systems. Despite the patterns being the hallmark of self-organization, it should in no way be taken to mean that these systems are stringently teleological, because, the introduction of nonlinear functions that guide these systems introduce at the same time the shunning off of a central authority, or anthropomorphic centrality, or any external designer. Linear functions could partake in localized situations, but at the macroscopic level, they lose their vitality, and if complex systems stick on to their loyalty towards linear functions, they fade away in the process of trying hard to avoid negotiating this allegiance. Just as allegiance to nonlinearity is important for self-organization, so is an allegiance to antireductionism. That is due to the fact of micro-level units having no knowledge about the macro-level effects, while at the same time, these macro-level effects manifest themselves in clusters of micro-level units, thus ruling out any sort of independent levelbased descriptions. The levels are stacked, intertwined, and most importantly, any resistance to reductionist discourse in trying to explicate the emergence within the system has no connotation for resistance to materialist properties themselves emerging. Clusters of information flow into the system from the external world that have an influencing impact on the internal makeup of the system and in turn triggers off interactions in tune with Hebb's law (other laws of learning as outlined in the first chapter could be potentially useful, but for sake of brevity involved in case studies, this law finds most takers) to alter weights. With the process in full swing, two possibilities could take shape, viz, formation of a stable system of weights based on the regularity of a stable cluster, and association between sets of these stable clusters as and when they are identified. This self-organizing principle is not only based on learning, but at the same time also cautious with sidelining those that are potentially futile for the system. Now, when such information flows into the system, sensors and/or transducers are set up, that obligate varying levels of intensity of activity to some neurons and nodes over 222

12 others. This is of course to be expected, and the way to come to terms with a regulated pattern of activity is the onus of adjustments of weights associated with neurons/nodes. A very important factor lies in the fact of the event denoting the flow of information from the external world into the system to occur regularly, or at least occasionally, lest the self-organizing or autopoietic system should fail to record in memory such occurrences and eventually fade out. Strangely, the patterns are arrived at without any reliance upon differentiated micro-level units to begin with. In parallel with neural networks, the nodes and neurons possess random values for their weights. The levels housing these microlevel nodes or neurons are intertwined to increase their strength, and if there is any absence of self-persisting positive feedback, the autopoietic system can in no way move away from the dictates of undifferentiated states it began with. As the nodes are associated with random values of weights, there is a race to show superiority, thus arresting the contingent limitless growth under the influence of limitless resources, thereby giving the emerging structure some meaningful essence and existence. Intertwining of levels also results in consensus building, and therefore effectuates meaning as accorded to these emergent structures of autpoietic systems. But this consensus building could lead astray the system from complexity, and hence to maintain the status quo, it is imperative for these autopoietic systems to have a correctional apparatus. The correctional apparatus spontaneously breaks the symmetry that leads the system away from complexity by either introducing haphazard fault lines in connections, or chaotic behaviors resulting from sensitivity to minor fluctuations as a result of banking on nonlinearity. Does this correctional apparatus in any way impact memory gained through the process of historicality? Apparently not. This is because of the distributed nature of memory storage, which is largely due to weights that are noncorrespondingly symbolic. The weights that show their activity at the local scale are associated with memory storage through traces, and it is only due to this fact that information gets distributed over the system generating robustness. With these characteristic features, autopoietic systems only tend towards organizing their structures to the optimum, with safely securing the complexity expected within the system. 223

13 A system that undergoes unexpected and/or violent upheaval is always attributed to as being facing up to a rupture, the kind of which is comprehensible by analyzing the subsystems that go on to make the systemic whole. Although, it could prove to be quite an important tool analysis, it seldom faces aporetic situations, because of unthought behavior exhibited. This behavior emanates from localized zones, and therefore send in lines of rupture that digresses the whole system from being comprehended successfully. To overcome this predicament, one must legitimize the existence of what Bak, Chen et. al. refer to as autopoietic or self-organizing criticality. According to Bak and Chen (1991: 26),...composite systems naturally evolve to a critical state in which a minor event starts a chain reaction that can affect any number of elements in the system. Although composite systems produce more minor events than catastrophes, chain reaction of all sizes are an integral part of the dynamics. According to the theory, the mechanism that leads to minor events is the same one that leads to major events. Furthermore, composite systems never reach equilibrium but evolve from one meta-stable state to the next...self-organized criticality is a holistic theory: the global features such as the relative number of large and small events, do not depend on the microscopic mechanisms. Consequently global features of the system cannot be understood by analyzing the parts separately. To our knowledge, self-organized criticality is the only model or mathematical description that has led to a holistic theory for dynamic systems. The acceptance of this criticality as existing has an affirmative impact on the autopoietic system in moving towards the point aptly called the critical point, which is loaded with a plethora of effects for a single event. This multitude is achieved through state-space descriptions or diagrams, with their uncanny characteristics of showing up different dimensions for different and independent variables. Diagrammatically, a state-space or Wuensche Diagram is as below: 224

14 (figure 10) In such a scenario, every state of the system would be represented by a unique point in the state-space and the dynamics of the system would be mapped by trajectories through the state-space. These trajectories when converge at a point, are said to converge in on a basin of attraction, or simply an attractor, and it is at this point that any system reaches stability. In all complex systems simulations at each moment the state of the system is described by a set of variables. As the system is updated over time these variables undergo changes that are influenced by the previous state of the entire system. System dynamics can be viewed as tabular data depicting the changes in variables over time. However, it is hard to analyze system dynamics just looking at the changes in these variables, as causal relationships between variables are not readily apparent. By removing all the details about the actual state and the actual temporal information, we can view the dynamics as a graph with nodes describing states and links describing transitions. For instance software applications can have a large number of states. Problems occur when software applications reach uncommon or unanticipated states. Being able to visualize the entire state space, and quickly comprehend the paths leading to any particular state, allows more targeted analysis. Common states can be thoroughly tested, uncommon states can be identified and artificially induced. State space diagrams allow for numerous insights into system behaviour, in particular some states of the system can be shown to be unreachable, while others are unavoidable. Its applicability lies in any situation in which you have a model or system which changes state over time and you want to examine the abstract dynamical qualities of these changes. For example, social network theory, gene regulatory networks, urban and agricultural water usage, and concept maps in cognition and language modeling. Drakos, N. and Moore, R. Discrete State-Space Trajectory Diagram. < First published Sep 9, primary source: Watkins, Hawkins, Bradley et. al. (2005). The point of convergence, if called an attractor, would have an opposite, when the trajectories recede from this point, and in that case, is called a repellor. A point with both an attractor and a repellor is called meta-stable. 225

15 (figure 11: Basin of attraction) But, would this attractor phenomenon work for neural networks, where there are myriad nodes, each with their own corresponding state-spaces? The answer is in the affirmative, and that is because in stable systems, only a few attractor points are present, thus pulling in the system to stability. On the contrary, if the system is not stable, utter chaos would reign supreme, and this is where autopoiesis as a phenomenon comes to rescue by balancing perfectly between chaos and ordered states. This balancing act is extremely crucial and sensitive, for on the one hand, a chaotic system is too disordered to be beneficial, and on the other, a system that is highly stable suffers a handicap in dedicating a lot of resources towards reaching and maintaining attractor point/s. Not only that, even a transition from one stable state to another would rope in sluggish responses in adaptability to environment, and that too at a heavy cost of perturbations. But, selforganizing criticality would take care of such high costs by optimally utilizing available Bourke, P. Random Attractors Found using Lyapunov Exponents. < First published Oct

16 resources. And as the nodes are in possession of unequal weights to begin with, the fight for superiority takes precedence, that gets reflected in the state-space as well. If inputs are marked by variations, optimization through autopoietic criticality takes over, else, the system settles down to a strong attractor/s. The nodes that interact at local zones are responsible for the macro-level effects, and according to Kauffman (1991), this is possible in simple networks, by the switching values of on or off at input. In such cases, order is ensured with the formation of cores of stability that thereafter permeate the network and further see to it that the system reaches stability by drawing in other nodes into stability. In complex networks, nonlinearity is incorporated to adjust signals approaching critical point. The adjustment sees to it that if the signals are getting higher values, the system as such would slide into stability, or otherwise into chaos. Therefore adjustment as a mechanism is an important factor in complex systems to self-organize by hovering around criticality, and this is what is referred to as on the edge of chaos by Lewin (1993). CASE UNDER CONSIDERATION Distinctions and binaries have their problematics, and neural networks are no different when one such attempt is made regarding the information that flows from the outside into the inside, where interactions occur. The inside of the system has to cope up with the outside of the system through mechanisms that are either predefined for the system under consideration, or having no independent internal structure at all to begin with. The former mechanism results in loss of adaptability, since all possible eventualities would have to be catered for in the fixed, internal structure of the system. The latter is guided by conditions prevailing in the environment. In either cases, learning to cope with the environmental conditions is the key for system's reaching any kind of stability. But, how would a system respond to its environment? According to the ideas propounded by Changeaux et. al. (1984), this is possible in two ways, viz, 227

17 1) An instructive mechanism is directly imposed by the environment on the system's structure and, 2) a selective mechanism, that is Darwinian in its import, helps maintain order as a result of interactions between the system and environment. The environment facilitates reinforcement, stabilization and development of the structure, without in any way determining it. These two distinct ways when exported to neural networks take on connotations as supervised and unsupervised learning. The position of Changeaux et. al. is rooted in rulebased, formal and representational formats, and is thus criticized by the likes of Edelman (1987). According to him, in a nervous system (his analysis is based upon nervous systems) neural signals in an information processing models are taken in from the periphery, and thereafter encoded in various ways to be subsequently transformed and retransformed during processing and generating an output. This not only puts extreme emphasis on formal rules, but also makes the claim on the nature of memory that is considered to occur through the representation of events through recording or replication of their informational details (Edelman 1987: 38). Although, Edelman's analysis takes nervous system as its centrality, the informational modeling approach that he undertakes is blanketed over the ontological basis that forms the fabric of the universe. Connectionists have no truck with this approach, as can be easily discerned from a long quote Edelman provides. He (Edelman 1987: 38) says (I quote in full), The notion of information processing tends to put a strong emphasis on the ability of the central nervous system to calculate the relevant invariance of a physical world. This view culminates in discussions of algorithms and computations, on the assumption that brain computes in an algorithmic manner...categories of natural objects in the physical world are implicitly assumed to fall into defined classes or typologies that are accessible to a 228

18 program. Pushing the notion even further, proponents of certain versions of this model are disposed to consider that the rules and representation (Chomsky 1980) that appear to emerge in the realization of syntactical structures and higher semantic functions of language arise from corresponding structures at the neural level. Edelman is aware of the shortcomings in informational processing models, and therefore takes a leap into connectionist fold with his proposal of brain consisting of a large number of undifferentiated, but connected neurons. He, at the same time gives a lot of credence to organization occurring at development phases of the brain. He lays out the following principles of this population thinking in his Neural Darwinism: The Theory of Neuronal Group Selection: 1) These homogeneous, undifferentiated population of neurons is epigenetically diversified into structurally variant groups through a number of selective processes called primary repertoire. 2) Connections among the groups are modified due to signals received during the interactions between the system and environment housing the system. Such modifications that occur during the post-natal period become functionally active to used in future, and form secondary repertoire. 3) With the setting up of primary and secondary repertoires, groups engage in interactions by means of feedback loops as a result of various sensory/motor responses, enabling the brain to interpret conditions in its environment and thus act upon them. Degenerate is what Edelman (1987: 6) calls are the neural groups in the primary repertoire to begin with. This entails the possibility of a significant number of nonidentical variant groups. This has another dimension to it as well, in that, non-identical variant groups are distributed uniformly across the system. Within Edelman's nervous 229

19 system case study, degeneracy and distributedness are crucial features to deny the localization of cortical functions on the one hand, and existence of hierarchical processing structures in a narrow sense on the other (Edelman 1987: 162). Edelman's cortical map formation incorporate the generic principles of autopoiesis. Cortical maps are collections (areas) of minicolumns in the brain cortex that have been identified as performing a specific information processing function. Schematically, it is like, (figure 12: cortical map) In Edelman's theory, neural groups have an optimum size that is not known a priori, but Figure source: Leuzinger-Bohleber, M. and Pfeifer, R. Remembering a Depressive Primary Object: Memory in the Dialogue Between Psychoanalysis and Cognitive Science. < id=ijp a>

20 develops spontaneously and dynamically. Within the cortex, this is achieved by means of inhibitory connections spread over a horizontal plane, while excitatory ones are vertically laid out, thus enabling the neuronal activity to be concentrated on the vertical plane rather than the horizontal one. Hebb's rule facilitates the utility function of this group. Impulses are carried on to neural groups thereby activating the same, and subsequently altering synaptic strengths. During the ensuing process, a correlation gets formed between neural groups with possible overlapping of messages as a result of synaptic activity generated within each neural groups. This correlational activity could be selected for frequent exposure to such overlaps, and once selected, the group might start to exhibit its activity even in the absence of inputs or impulses. The selection is nothing but memory, and is always used in learning procedures. A lot depends upon the frequency of exposure, as if this is on the lower scale, memory, or selection could simply fade away, and be made available for a different procedure. No wonder, why forgetting is always referred to as a precondition for memory. Fading away might be an useful criteria for using the freed allotted memory storage space during developmental process, but at the stage when groups of the right size are in place and ready for selection, weakly interacting groups would meet the fate of elimination. Elimination and retention of groups depends upon what Edelman refers to as the vitality principle, wherein, sensitivity to historical process finds more legitimacy, and that of extant groups find takers in influencing the formation of new groups. The reason for including Edelman's case was specifically to highlight the permeability of self-organizing principles during the cognitive development of the brain, and also pitting the superiority of neural networks/connectionist models in comprehending brain development over the traditional rule-based expert and formal systems of modeling techniques. In order to understand the nexus between brain development and environment, it would be secure to carry further Edelman's analysis. It is a commonsense belief in linking the structural changes in the brain with environmental effects. Even if one takes recourse to Darwinian evolution, these changes are either delayed due to systemic 231

21 resistance to let these effects take over, or in not so Darwinian a fashion, the effects are a compounded resultant of embedded groups within the network. On the other hand, Edelman's cortical map formation is not just confined to the processes occurring within brain's structure alone, but is also realized by how the brain explores its environment. This aspect is nothing but motor behavior in its nexus between the brain and environment and is strongly voiced by Cilliers (1998: 195), when he calls to attention, The role of active motor behavior forms the first half of the argument against abstract, solipsistic intelligence. The second half concerns the role of communication. The importance of communication, especially the use of symbol systems (language), does not return us to the paradigm of objective informationprocessing. Structures for communication remain embedded in a neural structure, and therefore will always be subjected to the complexities of network interaction. Our existence is both embodied and contingent. Edelman is criticized for showing no respect to replication in his theory, which is a strong pillar for natural selection and learning. Recently, attempts to incorporate replication in the brain have been undertaken, and strong indicators for neuronal replicators with the use of Hebb's learning mechanism as showing more promise when compared with natural selection are in the limelight (Fernando, Goldstein and Szathmáry 2010). These autopoietic systems when given a mathematical description and treatment could be used to model onto a computer or a digital system, thus help giving insights into the world pregnant with complexity. Autopiesis goes directly to the heart of anti-foundationalism. This is because the epistemological basis of basic beliefs is not paid any due respect or justificatory support in autopietic system's insistence on internal interactions and external contingent factors obligating the system to undergo continuous transformations. If autopoiesis could survive wonderfully well without the any transcendental intervention, or a priori 232

22 definition, it has parallels running within French theory. If anti-foundationalism is the hallmark of autopoiesis, so is anti-reductionism, since it is well nigh impossible to analyze to have meaning explicated in terms of atomistic units, and especially so, when the systems are already anti-foundationalist. Even in biologically contextual terms, a mereology according to Garfinkel is emergent as a result of complex interactions that go on within the autopoietic system. Garfinkel (1987: ) says (and I quote in full), We have seen that modeling aggregation requires us to transcend the level of the individual cells to describe the system by holistic variables. But in classical reductionism, the behavior of holistic entities must ultimately be explained by reference to the nature of their constituents, because those entities 'are just' collections of the lower-level objects with their interactions. Although, it may be true in some sense that systems are just collections of their elements, it does not follow that we can explain the system's behavior by reference to its parts, together with a theory of their connections. In particular, in dealing with systems of large numbers of similar components, we must make recourse to holistic concepts that refer to the behavior of the system as a whole. We have seen here, for example, concepts such as entrainment, global attractors, waves of aggregation, and so on. Although these system properties must ultimately be definable in terms of the states of individuals, this fact does not make them 'fictions'; they are causally efficacious (hence, real) and have definite causal relationships with other system variables and even to the states of the individuals. (Authorial emphasis). Autopoiesis gains vitality, when systems thinking opens up the avenues of accepting Not just French theory, even structural linguistics could derive benefits from autopoiesis as a concept. According to Kravchenko, within the new framework of autopoiesis, a greater explanatory power is contained that assumes the connotational nature of language. The essence of language gets a deeper insight with notions like representation, sign and signification, intentionality, interpretation, communication and reciprocal causality when approached from the autopoietic angle. Kravchenko, A. V. Essential properties of language from the point of view of Autopoiesis (unpublished). In Cogprints. < Deposited on Dec 28, Entrainment is a physical principle and is defined as a tendency of two oscillating bodies to lock into phase so that they vibrate harmonically. It is also defined as a synchronization of two or more rhythmic cycles. The principle of entrainment is universal, appearing in chemistry, pharmacology, biology, medicine, psychology, sociology, astronomy, and architecture. 233

23 contradictions and opposites rather than merely trying to get rid of them. Vitality is centered around a conflict, and ideally comes into a balanced existence, when such a conflict, or strife helps facilitate consensus building, or cooperation. If such goals are achieved, analyzing complexity theory gets a boost, and moreover by being sensitive to autopoiesis, an appreciation of the sort of the real lebenswelt gets underlined. Memory and history are essentials for complex autopoietic system, whether they be biological and/or social, and this can be fully comprehended in some quite routine situations where systems that are quite identical in most respects, if differing in their histories would have different trajectories in responding to situations they face. Memory does not determine the final description of the system, since it is itself susceptible to transformations, and what really gets passed on are the traces. The same susceptibility to transformations would apply to traces as well. But memory is not stored in the brain as discrete units, but rather as in a distributed pattern, and this is the pivotal characteristic of self-organizing complex systems over any other form of iconic representation. This property of transformation as associated with autopoietic systems is enough to suspend the process in between activity and passivity, in that the former is determining by the environment and the latter is impact on the environment. This is really important in autopoiesis, since the distinction between the inside and outside and active and passive is difficult to discern, and moreover this disappearance of distinction is a sufficient enough case to vouch against any authoritative control as residing within the system, and/or emanating from any single source. Autopoiesis scores over other representational modeling techniques by its ability to self-reflect, or by the system's ability to act upon itself. For Lawson (1985: 20-21), reflexivity disallows any static description of the system, since it Rosenfeld (1988), proposes a non-symbolic, non-representational theory of memory, when he argues for brain as a creative generator of memory, rather than a repository of memory often called the localization theory. In the book, Rosenfield presents evidence that memories are not fixed images but instead past experiences altered to fit the present circumstances. Rosenfield first reinterprets clinical studies in brain research previously used to support localization theory; he then summarizes David Marr's work in artificial intelligence and Gerald Edelman's theory of Neural Darwinism to support his alternative theory. Hilary Lawson builds on his theory 'closure' in epistemology. Closure is the principle that if a subject S knows that p, and S knows that p entails q, then S can thereby come to know that q. Most epistemological theories involve a closure principle and many sceptical arguments assume a closure principle, arguing for instance that if you cannot know you are a not a brain in a vat, then you cannot know that you have hands. 234

24 is not possible to intercept the reflexive moment, and it also disallows a complete description of the system at a meta-level. Even though a meta-level description can be construed, it is only the frozen frames or snapshots of the systems at any given particular instance, and hence ignores the temporal dimensions the systems undergo. For that to be taken into account, and measure the complexity within the system, the role of activity and passivity cannot be ignored at any cost, despite showing up great difficulties while modeling. But, is it not really a blessing in disguise, for the model of a complex system should be retentive of complexity in the real world? Well, the answer is yes, it is. Somehow, the discussion till now still smells of anarchy within autopoiesis, and if there is no satisfactory account of predictability and stability within the self-organizing system, the fears only get aggravated. A system which undergoes huge effects when small changes or alteration are made in the causes is definitely not a candidate for stability. And autopietic systems are precisely such. Does this mean that these are unstable?, or does it call for a reworking of the notion of stability? This is philosophically contentious and there is no doubt regarding this. Unstability could be a result of probabilities, but complex systems have to fall outside the realm of such probabilities. What happens in complex systems is a result of complex interactions due to a large number of factors, that need not be logically compatible. At the same time, stochasticity has no room, for it serves as an escape route from the annals of classical determinism, and hence a theory based on such escape routes could never be a theory of self-organization (Patteee 1987: 330). Stability is closely related to the ability to predict, and if stability is something very different from what classical determinism tells it is, the case for predictability should be no different. The problems in predictions are gross, as are echoed in the words of Krohn and Küppers (1989: ), In the case of these 'complex systems' (Nicolis and Prigogine [1989]), or 'nontrivial' machines, a functional analysis of input-output correlations must be supplemented by the study of 'mechanisms', i.e. by causal analysis. Due to the 235