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Metabolic-repair models, or (M,R)-systems were introduced in Relational Biology by Robert Rosen. Subsequently, Rosen represented such (M,R)-systems (or simply MRs) in terms of categories of sets, deliberately selected without any structure other than the discrete topology of sets. Theoreticians of life’s origins postulated that Life on Earth has begun with the simplest possible organism, called the primordial. Mathematicians interested in biology attempted to answer this important question of the minimal living organism by defining the functional relations that would have made life possible in such a minimal system- a grandad and grandma of all living organisms on Earth.

A categorical and Łukasiewicz-Topos framework for Algebraic Logic models of nonlinear dynamics in complex functional systems such as Neural Networks, Cell Genome and Interactome Networks is introduced. Łukasiewicz Algebraic Logic models of both neural and genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable next-state/transfer functions is extended to a Łukasiewicz Topos with an N-valued Łukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis.

The Line flow Contingency Selection and Ranking (CS & R) is performed to rank the critical contingencies in order of their severity. An Artificial Neural Network based method for MW security assessment corresponding to line outage events have been reported by various authors in the literature. One way to provide an understanding of the behaviour of Neural Networks is to extract rules that can be provided to the user. The domain knowledge (fuzzy rules extracted from Multi-layer Perceptron model trained by Back Propagation algorithm) is integrated into a Neural Network for fast and accurate CS & R in an IEEE 14-bus system, for unknown load patterns and are found to be suitable for on-line applications at Energy Management Centers. The system user is provided with the capability to determine the set of conditions under which a line-outage is critical, and if critical, then how severe it is, thereby providing some degree of transparency of the ANN solution.

Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Cognitive functions, like learning to speak, or discovering syntactical structures in language, have been modeled and these models are the basis for many claims about human cognitive capacities. Artificial neural nets (ANNs) have had some successes in the field of Artificial Intelligence, but the results from experiments with simple ANNs may have little value in explaining cognitive functions. The problem seems to be in relating cognitive concepts that belong in the `top-down' approach to models grounded in the `bottom-up' connectionist methodology. Merging the two fundamentally different paradigms within a single model can obfuscate what is really modeled. When the tools (simple artificial neural networks) to solve the problems (explaining aspects of higher cognitive functions) are mismatched, models with little value in terms of explaining functions of the human mind are produced. The ability to learn functions from data-points makes ANNs very attractive analytical tools. These tools can be developed into valuable models, if the data is adequate and a meaningful interpretation of the data is possible. The problem is, that with appropriate data and labels that fit the desired level of description, almost any function can be modeled. It is my argument that small networks offer a universal framework for modeling any conceivable cognitive theory, so that neurological possibility can be demonstrated easily with relatively simple models. However, a model demonstrating the possibility of implementation of a cognitive function using a distributed methodology, does not necessarily add support to any claims or assumptions that the cognitive function in question, is neurologically plausible.

The task of understanding how the brain works has met with only limited success since important design concepts are not as yet incorporated in the analysis. Relevant concepts can be uncovered by studying the powerful methodologies that have evolved in the context of computer programming, raising the question of how the concepts involved there can be realised in neural hardware. Insights can be gained in regard to such issues through the study of the role played by models and representation. These insights lead on to an appreciation of the mechanisms underlying subtle capacities such as those concerned with the use of language. A precise, essentially mathematical account of such capacities is in prospect for the future.

The Line flow Contingency Selection and Ranking (CS & R) is performed to rank the critical contingencies in order of their severity. An Artificial Neural Network based method for MW security assessment corresponding to line outage events have been reported by various authors in the literature. One way to provide an understanding of the behaviour of Neural Networks is to extract rules that can be provided to the user. The domain knowledge (fuzzy rules extracted from Multi-layer Perceptron model trained by Back Propagation algorithm) is integrated into a Neural Network for fast and accurate CS & R in an IEEE 14-bus system, for unknown load patterns and are found to be suitable for on-line applications at Energy Management Centers. The system user is provided with the capability to determine the set of conditions under which a line-outage is critical, and if critical, then how severe it is, thereby providing some degree of transparency of the ANN solution.

Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Simulations of cognitive functions, like learning to speak, or discovering syntactical structures in language, are the basis for many claims about human capacities in language acquisition. This raises the question whether results obtained from experiments that are essentially performed on data structures are equivalent to results from "real" experiments. This paper examines some design methodologies for models of cognitive functions using artificial neural nets. The process of conducting the cognitive simulations is largely a projection of theories, or even unsubstantiated conjectures, onto simulated neural structures and an interpretation of the experimental results in terms of the human brain. The problem with this process is that results from virtual experiments are taken to refer unambiguously to the human brain; and the more the language of human cognitive function is deployed in both theory construction and (virtual) experimental interpretation, the more convincing the impression. Additionally, the complexity of the methodologies, principles, and visualization techniques, in the implementation of the computational model, masks the lack of actual similarities between model and real world phenomena. Some computational models involving artificial neural nets have had some success, even commercially, but there are indications that the results from virtual experiments have little value in explaining cognitive functions. The problem seems to be in relating computational, or mathematical, entities to real world objects, like neurons and brains. I argue that the role of Artificial Intelligence as a contributor to the knowledge base of Cognitive Science is diminished as a consequence.

Current methodologies in the neurosciences have difficulty in accounting for complex phenomena such as language, which can however be quite well characterised in phenomenological terms. This paper addresses the issue of unifying the two approaches. We typically understand complicated systems in terms of a collection of models, each characterisable in principle within a formal system, it being possible to explain higher-level properties in terms of lower level ones by means of a series of inferences based on these models. We consider the nervous system to be a mechanism for implementing the demands of an appropriate collection of models, each concerned with some aspect of brain and behaviour, the observer mechanism of Baas playing an important role in matching model and behaviour in this context. The discussion expounds these ideas in detail, showing their potential utility in connection with real problems of brain and behaviour, important areas where the ideas can be applied including the development of higher levels of abstraction, and linguistic behaviour, as described in the works of Karmiloff-Smith and Jackendoff respectively.

The theory of computational complexity is used to underpin a recent model of neocortical sensory processing. We argue that encoding into reconstruction networks is appealing for communicating agents using Hebbian learning and working on hard combinatorial problems, which are easy to verify. Computational definition of the concept of intelligence is provided. Simulations illustrate the idea.

The corticothalamic feedback and the thalamic reticular nucleus have gained much attention lately because of their integrative and modulatory functions. A previous study by the author suggested that this circuitry can process analogies (i.e., the {\em analogy hypothesis}). In this paper, the proposed model was implemented as a network of leaky integrate-and-fire neurons to test the {\em analogy hypothesis}. The previous proposal required specific delay and temporal dynamics, and the implemented network tuned accordingly functioned as predicted. Furthermore, these specific conditions turn out to be consistent with experimental data, suggesting that a further investigation of the thalamocortical circuit within the {\em analogical framework} may be worthwhile.

We apply Slow Feature Analysis (SFA) to image sequences generated from natural images using a range of spatial transformations. An analysis of the resulting receptive fields shows that they have a rich spectrum of invariances and share many properties with complex and hypercomplex cells of the primary visual cortex. Furthermore, the dependence of the solutions on the statistics of the transformations is investigated.

Shepard and Chipman's second order isomorphism describes how the brain may represent the relations in the world. However, a common interpretation of the theory can cause difficulties. The problem originates from the static nature of representations. In an alternative interpretation, I propose that we assign an active role to the internal representations and relations. It turns out that a collection of such active units can perform analogical tasks. The new interpretation is supported by the existence of neural circuits that may be implementing such a function. Within this framework, perception, cognition, and motor function can be understood under a unifying principle of analogy.

Judgments of style in art, music, and literature are commonplace, although the mechanisms providing for this structural sensitivity are not well understood. Watanabe, Sakamoto, and Wakita (1995) showed that pigeons trained to discriminate colour slides of paintings of Picasso from those of Monet could generalise this discrimination not only to new paintings of Picasso and Monet, but also to paintings of other cubist and impressionist painters. These results suggest that the bases for such judgments of artistic style may be simpler than normally thought. This tacit sensitivity to artistic style is explored in terms of a simple PCA network model applied to pixel-maps of the paintings. The eigenvectors obtained from the singular value decomposition of sets of these pixel-maps provide for descriptions of the stimuli in terms of visual “macro-features”. These macro-features provide a simple basis not only for recognising previously-experienced paintings, but for the successful discrimination of novel paintings into various style categories. A summary of simulations of the performance of Watanabe et al.’s pigeons using precisely the same stimuli and tasks is provided. The results suggest that the eigen-decomposition is a necessary first-step, and that the bases for judgments of style may indeed be quite simple.

This paper investigates potential learning rules in the cerebellum. We review evidence that input to the cerebellum is sparsely expanded by granule cells into a very wide basis vector, and that Purkinje cells learn to compute a linear separation using that basis. We review learning rules employed by existing cerebellar models, and show that recent results from Computational Learning Theory suggest that the standard delta rule would not be efficient. We suggest that alternative, attribute-efficient learning rules, such as Winnow or Incremental Delta-Bar-Delta, are more appropriate for cerebellar modeling, and support this position with results from a computational model.

Traditionally, cognition has been considered as a ``mental processes'' only domain. Recently, however, there is a growing conscensus that cognition should be ``embodied'', i.e. it emerges from physical interaction with the world through a body with given perceptual and motoric abilities. Terms like ``emergence'', ``enaction'', ``grounding'', and ``situatedness'' are often used, but little attention is being paid to actually understanding the neural dynamics correlates of an emergence of cognition. Nor is hardly being investigated how the structure of the body-environment coupling is perceived and manipulated by our brain. It is as if talking about neural dynamics would somehow throw us backwards to the old cognitivist approach. In this paper we present a balanced view, in which we try to keep things in their respective place.

We examined the influence of the pre-existing organiza-tion of the semantic memory on forming new episodic associations between words. Testing human subjects' performance we found that a semantic relationship be-tween words facilitates forming episodic associations be-tween them. Furthermore, the amount of facilitation in-creases linearly as a function of the number of co-occurrence of the words, up to a ceiling. Constrained by these empirical findings we developed a computational model, based on the theory of spreading activation over semantic networks. The model uses self-organizing maps to represent semantic relatedness, and lateral connections to represent the episodic associations. When two words are presented to the model, the interaction of the two ac-tivation waves is summed and added to the direct lateral connection between them. The main result is that the model is capable of replicating the empirical results. The model also makes several testable predictions: First, it should be easier to form an association from a word with few semantic neighbors to a word with many se-mantic neighbors than vice-versa. Second, after associat-ing an unrelated word pair it should be easier to associate another two words each related to one of the words in the first pair. Third, a less focused activation wave, which may be the cause of schizophrenic thought disor-der, should decrease the advantage in learning rate of re-lated over unrelated pairs.

It is argued that cognitive capacities can be understood as the outcome of the collective action of a set of agents created by tools that explore possible behaviours and train the agents to behave in such appropriate ways as may be discovered. The coherence of the whole system is assured by a combination of vetting the performance of new agents and dealing appropriately with any faults that the whole system may develop. This picture is shown to account for a range of cognitive capacities, including language.

THERAPEUTIC IMPLICATIONS OF COMPUTER MODELS OF BRAIN ACTIVITY FOR ALZHEIMER DISEASE.

Self-organizing models develop realistic cortical structures when given approximations of the visual environment as input, and are an effective way to model the development of face recognition abilities. However, environment-driven self-organization alone cannot account for the fact that newborn human infants will preferentially attend to face-like stimuli even immediately after birth. Recently it has been proposed that internally generated input patterns, such as those found in the developing retina and in PGO waves during REM sleep, may have the same effect on self-organization as does the external environment. Internal pattern generators constitute an efficient way to specify, develop, and maintain functionally appropriate perceptual organization. They may help express complex structures from minimal genetic information, and retain this genetic structure within a highly plastic system. Simulations with the RF-LISSOM model show that such preorganization can account for newborn face preferences, providing a computational framework for examining how genetic influences interact with experience to construct a complex system.

We have implemented RatCog, a Graphical User Interface (GUI) radial-maze simulation tool providing various computational models of rats. Rat models are loaded as runtime plugin files, and an Application Programming Interface (API) enables additional plugins to be created. One implemented plugin is a back-propagation trained connectionist model. GUI features include maze graphics and performance statistics. The GUI makes it easier to use these computional models, while the plugins make the models widely available.

This paper discusses the simulation results of a model of biological development for neural networks based on a regulatory genome. The model’s results are analyzed using the framework of Heterochrony theory (McKinney and McNamara, 1991). The network development is controlled by genes that produce elements regulating the activation, inhibition, and delay of neurogenetic events. The genome can also regulate the gene expression mechanisms. An ecological task of foraging behavior is used to test the model with an evolving population of artificial organisms. Organisms evolve an optimal foraging behavior and the ability to adapt to changing environments. The adaptive strategy consists in changes of network architecture that are determined by the regulatory rearrangment of neurogenetic events. Results show how heterochronic changes play an adaptive role in the evolution of neural networks.

A model is proposed to demonstrate how neurons in the primary visual cortex could self-organize to represent the direction of motion. The model is based on a temporal extension of the Self-Organizing Map where neurons act as leaky integrators. The map is trained with moving Gaussian inputs, and it develops a retinotopic map with orientation columns that divide into areas of opposite direction selectivity, as found in the visual cortex.

This paper shows how the relationship between two arrays of artificial neurons, representing different cortical regions, can be learned. The algorithm enables each neural network to self-organise into a topological map of the domain it represents at the same time as the relationship between these maps is found. Unlike previous methods learning is achieved without a separate training phase; the algorithm which learns the mapping is also that which performs the mapping.

Psychological spaces give natural framework for construction of mental representations. Neural model of psychological spaces provides a link between neuroscience and psychology. Categorization performed in high-dimensional spaces by dynamical associative memory models is approximated with low-dimensional feedforward neural models calculating probability density functions in psychological spaces. Applications to the human categorization experiments are discussed.

Self-organizing models develop realistic cortical structures when given approximations of the visual environment as input. Recently it has been proposed that internally generated input patterns, such as those found in the developing retina and in PGO waves during REM sleep, may have the same effect. Internal pattern generators would constitute an efficient way to specify, develop, and maintain functionally appropriate perceptual organization. They may help express complex structures from minimal genetic information, and retain this genetic structure within a highly plastic system. Simulations with the RF-LISSOM orientation map model indicate that such preorganization is possible, providing a computational framework for examining how genetic influences interact with visual experience.

Values are critical for intelligent behavior, since values determine interests, and interests determine relevance. Therefore we address relevance and its role in intelligent behavior in animals and machines. Animals avoid exhaustive enumeration of possibilities by focusing on relevant aspects of the environment, which emerge into the (cognitive) foreground, while suppressing irrelevant aspects, which submerge into the background. Nevertheless, the background is not invisible, and aspects of it can pop into the foreground if background processing deems them potentially relevant. Essential to these ideas are questions of how contexts are switched, which defines cognitive/behavioral episodes, and how new contexts are created, which allows the efficiency of foreground/background processing to be extended to new behaviors and cognitive domains. Next we consider mathematical characterizations of the foreground/background distinction, which we treat as a dynamic separation of the concrete space into (approximately) orthogonal subspaces, which are processed differently. Background processing is characterized by large receptive fields which project into a space of relatively low dimension to accomplish rough categorization of a novel stimulus and its approximate location. Such background processing is partly innate and partly learned, and we discuss possible correlational (Hebbian) learning mechanisms. Foreground processing is characterized by small receptive fields which project into a space of comparatively high dimension to accomplish precise categorization and localization of the stimuli relevant to the context. We also consider mathematical models of valences and affordances, which are an aspect of the foreground. Cells processing foregound information have no fixed meaning (i.e., their meaning is contextual), so it is necessary to explain how the processing accomplished by foreground neurons can be made relative to the context. Thus we consider the properties of several simple mathematical models of how the contextual representation controls foreground processing. We show how simple correlational processes accomplish the contextual separation of foreground from background on the basis of differential reinforcement. That is, these processes account for the contextual separation of the concrete space into disjoint subspaces corresponding to the foreground and background. Since an episode may comprise the activation of several contexts (at varying levels of activity) we consider models, suggested by quantum mechanics, of foreground processing in superposition. That is, the contextual state may be a weighted superposition of several pure contexts, with a corresponding superposition of the foreground representations and the processes operating on them. This leads us to a consideration of the nature and origin of contexts. Although some contexts are innate, many are learned. We discuss a mathematical model of contexts which allows a context to split into several contexts, agglutinate from several contexts, or to constellate out of relatively acontextual processing. Finally, we consider the acontextual processing which occurs when the current context is no longer relevant, and may trigger the switch to another context or the formation of a new context. We relate this to the situation known as "breakdown" in phenomenology.

We present a corner-detection algorithm based on a model for end-stopping cells in the visual cortex. Shortcomings of this model are overcome by a combination over several scales. The notion of an end-stopped cell and the resulting corner detector is generalized to color channels in a biologically plausible way. The resulting corner detection method yields good results in the presence of high frequency texture, noise, varying contrast, and rounded corners. This compares favorably with known corner detectors.