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Here we exhibit multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or RN; N ∞ N, by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We study also the case of approximation by iterated operators of the last four types. These approximations are achieved by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order Fréchet derivatives. Our multivariate operators are defined by using a multidimensional density function induced by the algebraic sigmoid function. The approximations are pointwise and uniform. The related feed-forward neural network is with one hidden layer. [ABSTRACT FROM AUTHOR]

It has been proposed that meditative states show different brain dynamics than other more engaged states. It is known that when people sit with closed eyes instead of open eyes, they have different brain dynamics, which may be associated with a combination of deprived sensory input and more relaxed inner psychophysiological and cognitive states. Here, we study such states based on a previously established experimental methodology, with the aid of an electro-encephalography (EEG) array with 128 electrodes. We derived the Shannon Entropy (H) and Pearson's 1st Skewness Coefficient (PSk) from the power spectrum for the modalities of meditation and video watching, including 20 participants, 11 meditators and 9 non-meditators. The discriminating performance of the indices H and PSk was evaluated using Student's t-test. The results demonstrate a statistically significant difference between the mean H and PSk values during meditation and video watch modes. We show that the H index is useful to discriminate between Meditator and Non-Meditator participants during meditation over both the prefrontal and occipital areas, while the PSk index is useful to discriminate Meditators from Non-Meditators based on the prefrontal areas for both meditation and video modes. Moreover, we observe episodes of anti-correlation between the prefrontal and occipital areas during meditation, while there is no evidence for such anticorrelation periods during video watching. We outline directions of future studies incorporating further statistical indices for the characterization of brain states. [ABSTRACT FROM AUTHOR]

Here we exhibit multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or ℝN, N ∈ ℕ, by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We study also the case of approximation by iterated operators of the last four types. These approximations are achieved by establishing multidimensional Jackson type in- equalities involving the multivariate modulus of continuity of the engaged function or its high order Fréchet derivatives. Our multivariate operators are defined by using a multidimensional density function induced by the algebraic sigmoid function. The approximations are pointwise and uniform. The related feed-forward neural network is with one hidden layer. [ABSTRACT FROM AUTHOR]

The mammalian olfactory bulb displays a prominent respiratory rhythm, which is linked to the sniff cycle and is driven by sensory input from olfactory receptors in the nasal sensory epithelium. In rats and mice, respiratory frequencies occupy the same band as the hippocampal θ-rhythm, which has been shown to be a key player in memory processes. Hippocampal and olfactory bulb rhythms were previously found to be uncorrelated except in specific odor-contingency learning circumstances. However, many recent electrophysiological studies in both rodents and humans reveal a surprising cycle-by-cycle influence of nasal respiration on neuronal activity throughout much of the cerebral cortex beyond the olfactory system, including the prefrontal cortex, hippocampus, and subcortical structures. In addition, respiratory phase has been shown to influence higher-frequency oscillations associated with cognitive functions, including attention and memory, such as the power of γ-rhythms and the timing of hippocampal sharp wave ripples. These new findings support respiration's role in cognitive function, which is supported by studies in human subjects, in which nasal respiration has been linked to memory processes. Here, we review recent reports from human and rodent experiments that link respiration to the modulation of memory function and the neurophysiological processes involved in memory in rodents and humans. We argue that respiratory influence on the neuronal activity of two key memory structures, the hippocampus and prefrontal cortex, provides a potential neuronal mechanism behind respiratory modulation of memory. [ABSTRACT FROM AUTHOR]

It is of great interest to develop advanced sensory technologies allowing non-invasive monitoring of neural correlates of cognitive processing in people performing everyday tasks. A lot of progress has been reported in recent years in this research area using scalp EEG arrays, but the high level of noise in the electrode signals poses a lot of challenges. This study presents results of detailed statistical analysis of experimental data on the cycle of creation of knowledge and meaning in human brains under multiple cognitive modalities. We measure brain dynamics using a HydroCel Geodesic Sensor Net, 128-electrode dense-array electroencephalography (EEG). We compute a pragmatic information (PI) index derived from analytic amplitude and phase, by Hilbert transforming the EEG signals of 20 participants in six modalities, which combine various audiovisual stimuli, leading to different mental states, including relaxed and cognitively engaged conditions. We derive several relevant measures to classify different brain states based on the PI indices. We demonstrate significant differences between engaged brain states that require sensory information processing to create meaning and knowledge for intentional action, and relaxed-meditative brain states with less demand on psychophysiological resources. We also point out that different kinds of meanings may lead to different brain dynamics and behavioral responses. [ABSTRACT FROM AUTHOR]

Abstract In this paper a random graph model G Z N 2 , p d is introduced, which is a combination of fixed torus grid edges in (Z ∕ N Z) 2 and some additional random ones. The random edges are called long, and the probability of having a long edge between vertices u , v ∈ (Z ∕ N Z) 2 with graph distance d on the torus grid is p d = c ∕ N d , where c is some constant. We show that, whp , the diameter D (G Z N 2 , p d ) = Θ (log N). Moreover, we consider a modified non-monotonous bootstrap percolation on G Z N 2 , p d . We prove the presence of phase transitions in mean-field approximation and provide fairly sharp bounds on the error of the critical parameters. [ABSTRACT FROM AUTHOR]

A 50th birthday is an important milestone in the life of any individual and certainly in the development of collectives and organizations. January 2021 is such a milestone in the life of the IEEE Transactions of Systems, Man, and Cybernetics, which had its very first issue published in January 1971. Our 50th Anniversary Issue celebrates this remarkable achievement by introducing 20 survey articles from early pioneers of our field, as well as works by leading scientists conducting research at the cutting edge of systems science, with a focus on human aspects, cybernetics, with crucial societal impact. Our goal is to provide a vista of the remarkable developments in the past, giving a snapshot of the present state of our field, and indicating possible avenues for future progress in our rapidly changing research discipline. [ABSTRACT FROM AUTHOR]

Subjective experience suggests that we continuously observe, perceive, and evaluate the environment as we make decisions and intentional actions. The percept of continuity of our cognition, however, is an illusion. In the past decades, ample experimental evidence has been accumulated indicating that cognition evolves through a sequence of discontinuities and transients, and there are discernable neural processes correlating with the cognitive sequences. These discontinuities are crucial in the intentional action-perception cycle, as they mark the cognitive 'aha ' moment of deep understanding and conscious insight. Walter Freeman developed a hierarchical model to describe these physiological findings, culminating in the cinematic theory of cognition. Following the traditions of metastability, transient neurodynamics, and brain chaos, discontinuities are described as phase transitions in the cortical neuropil. This approach not only leads to deep theoretical insights of neural correlates of cognition and consciousness, but it also helps to form a conceptual framework to create artificially intelligent devices that are in harmony with humans and serve peaceful development of humanity. [ABSTRACT FROM AUTHOR]

Average evoked potential data recorded as impulse responses of brains to electric shocks show Bessel-like functional distributions which we analyze in terms of couples of damped/amplified oscillators. This reproduces results obtained in terms of ordinary differential equations (Freeman K-sets) and offers the possibility of a direct connection with the dissipative model of brain in the quantum gauge field theory paradigm. We study the control mechanism by fine tuning the model parameters and the brain property of discriminating between two similar behaviors or perceptions.We suggest that a similar control mechanism may be useful in security communication protocols. Finally, brain activity and mental activity is considered in the light of our results. [ABSTRACT FROM AUTHOR]

In this commemoration of Walter Freeman's life and legacy, we review some his key contributions to the field of cognitive neuroscience. We elaborate on his groundbreaking contribution to neural aspects of intentionality, what he called intentional neurodynamics. We describe the conceptual framework of intentional neurodynamics and its neurophysiological manifestations. We conclude with the outline of Freeman K (Katchalsky) sets, a hierarchical model of brain structure, dynamics, and functions, which provide a suitable mathematical and computational framework to grasp essential aspects of intentionality. [ABSTRACT FROM AUTHOR]

In this work, we describe operational principles of a pattern-based computing paradigm based on the neuropercolation model, which can be used as associative memory supporting sensory processing and pattern recognition. Neuropercolation extends the concept of phase transitions to interactive populations exhibiting frequent transients in their spatio-temporal dynamics, which can be viewed as manifestations of an asynchronous computer working with a sequence of meta-stable spatial patterns, in a bid to unravel the limitations of Turing computing principles. The model is motivated by the structural and dynamical properties of large-scale neural populations in the cerebral cortex and it implements basic building blocks of neurodynamics following the hierarchy of Freeman K-sets. The introduced mean-field approximation allows rigorous mathematical analysis of the emergent dynamics, which is the major novel contribution of this work. Specifically, we derive exact conditions for the onset of non-zero background activity, for the transition from steady state to narrow-band (limit cycle) oscillations, and for the transition from narrow-band to broad-band (chaotic) dynamics. We describe an array of connected oscillators, which exhibits transient synchronization episodes manifesting meta-stable collective states. The corresponding meta-stable spatial amplitude patterns are destabilized by inputs or spontaneously and jump to another pattern, yielding a sequence of transient patterns. These patterns are shaped by the connections between the nodes modifiable through learning. The sequence of patterns manifest the steps of the computation, which embody the meaning of the input data in the context of the system past experiences. [ABSTRACT FROM AUTHOR]

This paper provides new stability results for Action-Dependent Heuristic Dynamic Programming (ADHDP), using a control algorithm that iteratively improves an internal model of the external world in the autonomous system based on its continuous interaction with the environment. We extend previous results for ADHDP control to the case of general multi-layer neural networks with deep learning across all layers. In particular, we show that the introduced control approach is uniformly ultimately bounded (UUB) under specific conditions on the learning rates, without explicit constraints on the temporal discount factor. We demonstrate the benefit of our results to the control of linear and nonlinear systems, including the cart–pole balancing problem. Our results show significantly improved learning and control performance as compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

Mathematical approaches are reviewed to interpret intermittent singular space–time dynamics observed in brain imaging experiments. The following aspects of brain dynamics are considered: nonlinear dynamics (chaos), phase transitions, and criticality. Probabilistic cellular automata and random graph models are described, which develop equations for the probability distributions of macroscopic state variables as an alternative to differential equations. The introduced modular neuropercolation model is motivated by the multilayer structure and dynamical properties of the cortex, and it describes critical brain oscillations, including background activity, narrow-band oscillations in excitatory–inhibitory populations, and broadband oscillations in the cortex. Input-induced and spontaneous transitions between states with large-scale synchrony and without synchrony exhibit brief episodes with long-range spatial correlations as observed in experiments. [ABSTRACT FROM AUTHOR]

In this paper we consider ball packings in $$4$$ -dimensional hyperbolic space. We show that it is possible to exceed the conjectured $$4$$ -dimensional realizable packing density upper bound due to L. Fejes-Tóth (Regular Figures, Macmillian, New York, ). We give seven examples of horoball packing configurations that yield higher densities of $$0.71644896\dots $$ , where horoballs are centered at ideal vertices of certain Coxeter simplices, and are invariant under the actions of their respective Coxeter groups. [ABSTRACT FROM AUTHOR]

Deep Spiking Neural Networks (SNNs) with event-driven dynamics become increasingly popular in many challenging Machine Learning applications, based on their cheap and efficient computations. The discontinuity of the SNN dynamics, however, leads to problems in the learning process, resulting in performance loss, as the dominant gradient-based training approaches are not easily adaptable to the discontinuous SNN activation domain. One promising approach develops SNNs by converting trained Deep Neural Networks to SNNs, which has been very successful in classification applications. Recently, the scope of the conversion studies has been extended to Deep Q-Networks (DQNs), and highly competitive performance has been achieved on many challenging Atari games. The present work provides a comprehensive description of the DQN to SNN conversion algorithm and evaluates the causes of the potential performance loss during the conversion process. We analyze three key factors which allow practical implementations without loss of generality for a large class of highly demanding Q-learning problems, including robust conversion rate, threshold percentile, and simulation time. Our results are not only competitive to DQN in terms of performance but also highly efficient, which is extremely beneficial upon implementations on neuromorphic platforms. [ABSTRACT FROM AUTHOR]

In the light of contemporary forms of migration and in the context of globalisation and rapid development of technology and transportation, diaspora is increasingly viewed through the transnational prism. Instead of forcedly scattered countrymen, who dream of returning to their homeland and have difficulties with the integration in the reception country, diaspora is seen as a link that connects worlds, the country of origin and the receiving country, thus enabling migration to be used for development of the community and society on the whole. Instead of return, the emphasis is on the circular exchange and transnational mobility. In this way countries of Southeast Europe are trying to transform the "brain drain", which was particularly prominent in the 1990s, into "brain circulation" and "brain gain". In Serbia, 1990s migration was triggered by saving lives, primarily from the painful and bloody civil wars waged in the former Yugoslavia, international isolation and NATO bombing, as well as the difficult economic situation. In that period, the particularly massive departure was that of young and educated people with the generally accepted slogan "to graduate, and then to emigrate". However, migration of educated people and professionals, often motivated by career development and professional advancement abroad, has continued after 2000. Many young people are enabled to go abroad with the help of foreign scholarship foundations aiming at assisting development in Serbia, such as the Alexander von Humboldt Foundation AvH, German Academic Research Service DAAD, Mummert Foundation, etc. Therefore, the main objective of this paper is to determine the possibility for connection between professionals from diaspora and young students and engineers in Serbia, in order to facilitate the transfer of knowledge and their mobility and thus prevent the irreversible loss for the country. In addition to theoretical considerations of the possibility to improve cooperation with the diaspora, the paper analyses an example of successful cooperation between professionals in the metallurgical industry from Germany and professionals from Serbia, initiated by a representative of the professional diaspora. Finally, recommendations are made as to how to apply the lessons learned from the literature and practice for the development of sustainable cooperation among the professional diaspora and youth and professionals in Serbia. [ABSTRACT FROM AUTHOR]

Abstract: Sensory information processing and cognition in brains are modeled using dynamic systems theory. The brain’s dynamic state is described by a trajectory evolving in a high-dimensional state space. We introduce a hierarchy of random cellular automata as the mathematical tools to describe the spatio-temporal dynamics of the cortex. The corresponding brain model is called neuropercolation which has distinct advantages compared to traditional models using differential equations, especially in describing spatio-temporal discontinuities in the form of phase transitions. Phase transitions demarcate singularities in brain operations at critical conditions, which are viewed as hallmarks of higher cognition and awareness experience. The introduced Monte-Carlo simulations obtained by parallel computing point to the importance of computer implementations using very large-scale integration (VLSI) and analog platforms. [Copyright &y& Elsevier]

Abstract: Large-scale networks with hundreds of thousands of variables and constraints are becoming more and more common in logistics, communications, and distribution domains. Traditionally, the utility functions defined on such networks are optimized using some variation of Linear Programming, such as Mixed Integer Programming (MIP). Despite enormous progress both in hardware (multiprocessor systems and specialized processors) and software (Gurobi) we are reaching the limits of what these tools can handle in real time. Modern logistic problems, for example, call for expanding the problem both vertically (from one day up to several days) and horizontally (combining separate solution stages into an integrated model). The complexity of such integrated models calls for alternative methods of solution, such as Approximate Dynamic Programming (ADP), which provide a further increase in the performance necessary for the daily operation. In this paper, we present the theoretical basis and related experiments for solving the multistage decision problems based on the results obtained for shorter periods, as building blocks for the models and the solution, via Critic-Model-Action cycles, where various types of neural networks are combined with traditional MIP models in a unified optimization system. In this system architecture, fast and simple feed-forward networks are trained to reasonably initialize more complicated recurrent networks, which serve as approximators of the value function (Critic). The combination of interrelated neural networks and optimization modules allows for multiple queries for the same system, providing flexibility and optimizing performance for large-scale real-life problems. A MATLAB implementation of our solution procedure for a realistic set of data and constraints shows promising results, compared to the iterative MIP approach. [Copyright &y& Elsevier]

The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (<$50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios. [ABSTRACT FROM AUTHOR]

Electroencephalographic and magnetoencephalographic potential generated by active brain reveals widespread coherent oscillations. The oscillations of multiple rhythms overlap in broad spectrum noise. Spectrum analysis of short segments reveals peaks in various frequency ranges. As the duration of segments chosen for analysis increases, the basic form to which spectra converge is the one of Brown noise. Any frequency can be modeled with probabilistic cellular automata. Coupled inhibitory and excitatory probabilistic cellular automata with appropriate topology and probabilistic rule governing the local interactions simulate an oscillator. Coupled oscillators create a model with the activation whose spectrum of long segments converges to a Brown noise. [ABSTRACT FROM AUTHOR]

Human cognition performs granulation of the seemingly homogeneous temporal sequences of perceptual experiences into meaningful and comprehensible chunks of fuzzy concepts and behaviors. These knowledge granules are stored and consequently accessed during action selection and decisions. A dynamical approach is presented here to interpret experimental findings using K (Katchalsky) models. In the K model, meaningful knowledge is repetitiously created and processed in the form of sequences of oscillatory patterns of neural activity distributed across space and time. These patterns are not rigid but flexible and intermittent; soon after they arise through phase transitions, they dissipate. Computational implementations demonstrate the operation of the model based on the principles of intentional brain dynamics. [Copyright &y& Elsevier]

Cognitive experiments indicate the presence of discontinuities in brain dynamics during high-level cognitive processing. Non-linear dynamic theory of brains pioneered by Freeman explains the experimental findings through the theory of metastability and edge-of-criticality in cognitive systems, which are key properties associated with robust operation and fast and reliable decision making. Recently, neuropercolation has been proposed to model such critical behavior. Neuropercolation is a family of probabilistic models based on the mathematical theory of bootstrap percolations on lattices and random graphs and motivated by structural and dynamical properties of neural populations in the cortex. Neuropercolation exhibits phase transitions and it provides a novel mathematical tool for studying spatio-temporal dynamics of multi-stable systems. The present work reviews the theory of cognitive phase transitions based on neuropercolation models and outlines the implications to decision making in brains and in artificial designs. [ABSTRACT FROM AUTHOR]

In spite of the explosive growth of experimental research in basic neurobiology and neurophysiology of brain components in the past decade, understanding the integrated functioning of the brain remains a significant scientific challenge. Essential for understanding human brain function is the detailed knowledge concerning the spatio-temporal dynamics of neuronal populations and their intricate interactions during cognitive functions. The aim of the present issue is to examine brain dynamics and cognitive functions from a multidisciplinary perspective and to introduce the most recent results in this research frontier. Topics relevant to the special issue include: (i) Modeling brain dynamics at the mesoscopic and macroscopic scales, including dynamical systems with distributed parameters; (ii) Applying tools of discrete mathematics, statistical and quantum physics, network science to describe the dynamics of brains; (iii) Experimental research on brain dynamics from various aspects, including fundamental neurobiology, evoked potentials, functional brain imaging, and cognitive functions; (iv) Clinical neuroscience issues for improved diagnosis of dynamic brain diseases and their potential therapies. This special issue is dedicated to Professor Walter J. Freeman on the occasion of his 80th birthday. Dr. Freeman produced breakthrough contributions to research on brain dynamics over the past five decades. The present issue covers all aspects of neurodynamics, starting from neural populations of high-level cognition and consciousness, as well as philosophical aspects and practical implementations on digital computers and hardware designs. [ABSTRACT FROM AUTHOR]

Intentional behavior is a basic property of intelligence, and it incorporates the cyclic operation of prediction, testing by action, sensing, perceiving and assimilating the experienced features. Intentional neurodynamic principles are applied for on-line processing of multisensory inputs and for the generation of dynamic behavior using the SRR (Sample Return Rover) platform at the indoor facility of the Planetary Robotics Laboratory, Jet Propulsion Laboratory. The studied sensory modalities include CMOS camera vision, orientation based on an internal motion unit and accelerometer signals. The control architecture employs a biologically inspired dynamic neural network operating on the principle of chaotic neural dynamics manifesting intentionality in the style of mammalian brains. Learning is based on Hebbian rules coupled with reinforcement. The central issue of this work is to study how the developed control system builds associations between the sensory modalities to achieve robust autonomous action selection. The proposed system builds such associations in a self-organized way and it is called Self-Organized Development of Autonomous Adaptive Systems (SODAS). This system operates autonomously, without the need for human intervention, which is a potentially very beneficial feature in challenging environments, such as encountered in space explorations at remote planetary environments. The experiments illustrate obstacle avoidance combined with goal-oriented navigation by the SRR robot using SODAS control principles. [ABSTRACT FROM AUTHOR]

Electrocorticographic (ECoG) signals from the brain surface typically exhibit high synchrony across large cortical areas, interrupted by brief periods of desynchronization exhibiting propagating phase discontinuities, across which spatial patterns of phase emerge in selected frequency bands. Experiments with rabbits trained using classical conditioning paradigms indicated that such desynchronization periods demarcate cognitive processing in the subjects; the ECoG in the frames between such periods revealed spatial patterns of amplitude modulation that were classified with respect to sensory stimuli that the rabbits had been trained to recognize. The present work describes intermittent synchrony and desynchronization of ECoG signals measured over the visual cortex. We analyze the analytic amplitude (AA) and analytic phase (AP) of the signals bandpassed over the beta band (12.5–25 Hz) and theta band (3–7 Hz) using the Hilbert transform. The AP of analytic signals evaluated using a Shannon-based synchronization index in theta band exhibits phase synchronization for varying time periods averaging about 1 s, interrupted by desynchronization periods of duration about 0.1 s. Synchronization periods in the beta-band last <100 ms, with interruptions by desynchronization lasting one-tenth that, in which the analytic amplitude drops drastically. During these “null spikes,” the analytic phase is undefined, and the spatial and temporal phase differences show high dispersion. Detailed examination of the bandpass filtered ECoG confirms the presence of a shared mean frequency in a frame of synchronized oscillation, at which frequency the spatial pattern of the AP has the form of a cone. Between frames the AA approaches zero. The form of the null spike resembles a tornado (a vortex), as shown in sequential frames by a rotating spatial pattern of amplitude in the filtered ECoG. [ABSTRACT FROM AUTHOR]

Cellular simultaneous recurrent neural network (SRN) has been shown to be a function approximator more powerful than the multilayer perceptron (MLP). This means that the complexity of MLP would be prohibitively large for some problems while SRN could realize the desired mapping with acceptable computational constraints. The speed of training of complex recurrent networks is crucial to their successful application. This work improves the previous results by training the network with extended Kalman filter (EKF). We implemented a generic cellular SRN (CSRN) and applied it for solving two challenging problems: 2-D maze navigation and a subset of the connectedness problem. The speed of convergence has been improved by several orders of magnitude in comparison with the earlier results in the case of maze navigation, and superior generalization has been demonstrated in the case of connectedness. The implications of this improvements are discussed. [ABSTRACT FROM AUTHOR]

KIII is a strongly biologically inspired neural network model. It has a multi-layer architecture with excitatory and inhibitory neurons, which have massive lateral, feedforward, and delayed feedback connections between layers. KIII has been shown to be an efficient tool of classification and pattern recognition. In this work, we develop a methodology to use KIII for multi-step time series prediction. This method is applied to the IJCNN CATS benchmark data. Taking into account the results of CATS competition, we improve upon our method by tuning the K-network parameters, therefore providing a better set of parameters for prediction task. [Copyright &y& Elsevier]

Abstract: The hierarchy of models of interacting neural populations with excitatory and inhibitory connections is described using second-order nonlinear ordinary differential equations. Systematic analytical and numerical studies are aimed at determining stability conditions of the coupled system. Generalized stability conditions are derived for a class of excitatory–inhibitory neural population models. Equilibrium continuation analysis is applied to interpret the obtained stability conditions. The results are discussed in the context of chaotic brain dynamics theory and chaotic itinerancy. Attractor switching in biologically relevant multi-stable systems is demonstrated. [Copyright &y& Elsevier]

In the central nervous system (CNS), damaged axons are inhibited from regeneration by glial scars, where secreted chondroitin sulfate proteoglycan (CSPG) and tenascin repulse outgrowth of neurites, the forerunners of axons and dendrites. During differentiation, these molecules are thought to form boundaries for guiding neurons to their correct targets. In neuroblastoma NIE-115 cells, outgrowth of neurites on laminin could be induced by serum starvation or inhibition of RhoA by Clostridium botulinum C3 toxin. The outgrowing neurites avoided crossing onto the repulsive substrate CSPG or tenasein. This avoidance response was partially overcome on expression of membrane-targeted and kinase-inactive forms of PAK. In these cells, the endogenous PAK isoforms colocalized with actin in distinctive sites, αPAK in the cell center as small clusters and along the neurite shaft and βPAK and γPAK in areas with membrane ruffles and filopodia, respectively. When isoform-specific N-terminal PAK sequences were introduced to interfere with PAK function, substantially more neurites crossed onto CSPG when cells contained a γPAK-derived peptide but not the corresponding αPAK- or βPAK-derived peptide. Thus, while neurite outgrowth can be promoted by RhoA inhibition, overcoming the accompanying repulsive guidance response will require modulation of PAK activity. These results have therapeutic implications for CNS repair processes. [ABSTRACT FROM AUTHOR]

We model the dynamical behavior of the neuropil, the densely interconnected neural tissue in the cortex, using neuropercolation approach. Neuropercolation generalizes phase transitions modeled by percolation theory of random graphs, motivated by properties of neurons and neural populations. The generalization includes (1) a noisy component in the percolation rule, (2) a novel depression function in addition to the usual arousal function, (3) non-local interactions among nodes arranged on a multi-dimensional lattice. This paper investigates the role of non-local (axonal) connections in generating and modulating phase transitions of collective activity in the neuropil. We derived a relationship between critical values of the noise level and non-locality parameter to control the onset of phase transitions. Finally, we propose a potential interpretation of ontogenetic development of the neuropil maintaining a dynamical state at the edge of criticality. [ABSTRACT FROM AUTHOR]

Abstract: The interaction between entorhinal cortex (EC), amygdala, and hippocampal and cortical areas in vertebrate brains is studied using the dynamical K model approach. Special emphasis is given to the role of EC in decision making under the influence of sensory, orientation, and motivational clues. We introduce a simplified KIV model with positive and negative reinforcement learning in the hippocampus and the cortex. The developed model is implemented in a 2D computational environment for multi-sensory control of the movement of a simulated animal. Our results support the interpretation of recent EEG measurements with instantaneous macroscopic phase transitions. [Copyright &y& Elsevier]

Mesoscopic level neurodynamics study the collective dynamical behavior of neural populations. Such models are be- coming increasingly important in understanding large-scale brain processes. Brains exhibit aperiodic oscillations with a much more rich dynamical behavior than fixed-point and limit-cycle approximation allow. Here we present a discretized model inspired by Freeman's K-set mesoscopic level population model. We show that this version is capable of replicating the important principles of aperiodic/chaotic neurodynamics while being fast enough for use in real-time autonomous agent applications. This simplification of the K model provides many advantages not only in terms of efficiency but in simplicity and its ability to be analyzed in terms of its dynamical properties. We study the discrete version using a multilayer, highly recurrent model of the neural architecture of perceptual brain areas. We use this architecture to develop, example action selection mechanisms in an autonomous agent. [ABSTRACT FROM AUTHOR]

In response to this digital divide between developed and developing countries in their use of computers to prepare students for the global economy, the World Bank and, subsequently, the World Links organization provided schools in developing countries with networked computers and training that supports integration of ICT into teaching. This article synthesizes findings from 3 years of evaluative research on the program. The findings are based on surveys of teachers, students, administrators, and technology coordinators, as well as a field test assessment of student learning. The study examines the services provided, documents their impact, and draws implications for policy in developing countries. [Copyright &y& Elsevier]

Previous studies on the KIV model outlined a general architecture of modeling sensory–perceptual–intentional action cycle in the primordial vertebrate forebrain using nonlinear dynamical principles. KIV consists of three KIII units representing aperiodic/chaotic dynamics in sensory cortex, hippocampal formation, and midline forebrain, respectively. The sensory cortex has demonstrated excellent performance as a pattern recognition and classification device. In this work, the behavior of the hippocampal formation is studied as part of the KIV system. We elaborate a reinforcement algorithm to learn goal-oriented behavior based on global orientation beacons, biased by local sensory information provided by visual or infra-red sensors. We illustrate the operation of the KIV model using the multiple T-maze navigation problem. [Copyright &y& Elsevier]

We present a model of spatial navigation based on the non-convergent dynamics of brain activity. The system includes a hippocampal module that processes global spatial information and a cortical module that deals with local sensory information. We test the model using several spatial navigation paradigms: goal finding, shortcutting and detouring. Computer simulations show that the performance of the agent qualitatively matches that of animals and related models. This new approach provides a novel interpretation of how the brain accomplishes spatial navigation. [ABSTRACT FROM AUTHOR]

In this article, we describe an approach designed to assess student skills as they use information and communications technologies (ICT) to solve complex, significant, recurring academic and applied problems. We describe a Coordinated ICT Assessment Framework and its underpinnings in theory and research. We describe studies of ICT use in schools and highlight the recent Second Information Technology in Education Study: Module 2 (SITES M2) international case studies. We then offer an innovative design approach for assessing students' performances as they employ a range of reasoning strategies to use technologies to solve academic and applied problems. [ABSTRACT FROM AUTHOR]

This study examines the findings from 174 case studies of innovative pedagogical practices using technology from 28 participating countries. The study looks at how classrooms worldwide are using technology to change the practices of teachers and students. Within many of these classrooms, the use of technological tools and resources supports students as they search for information, design products, and publish results. Teachers create structure, provide advice, and monitor progress. Beyond these commonly exhibited practices, the study identifies specific patterns of classroom practice that are more likely to be associated with reports of certain desirable student outcomes. Examples are provided. (Keywords: computers; educational technology; classroom practice; educational innovation). [ABSTRACT FROM AUTHOR]

Studies dynamical models of neural networks which exhibits phase transitions between states of various complexities. Usage of biologically motivated KIII model; Features of the KIII model; Role of additive noise in the development if itinerant trajectories.

EEG measurements indicate the presence of common-mode, coherent oscillations shared by multiple cortical areas. In previous studies the KIII model has been introduced, which interprets the experimental observations as nonlinear, spatially distributed dynamical oscillations of locally coupled neural populations. KIII can account for the fast and robust classification and pattern recognition in sensory cortices. In order to describe selection of action, planning, and spatial orientation functions, in this paper we expand KIII into the KIV model. KIV approximates the operation of the corticostriatal-hippocampal system. KIV consists of three KI, eight KII and three KIII components, including sensory and cortical systems, as well as the hippocampus, amygdala, and the septum. KIV implements various types of dynamic neural activities. The neural activity patterns determine the emergence of global spatial encoding to implement the orientation function of a simulated animal. Our results indicate the mechanisms, which we believe support the generation of cognitive maps in the hippocampus based on the sensory input-based destabilization of cortical spatio-temporal patterns. In this paper, we describe the conceptual design of the KIV model. We outline the biological background and motivation of the basic principles that are applied to design the KIV computational model. We use the KIV model to explain how the hippocampal neural circuitry functions are constructed and controlled by the corticostriatal-hippocampal loops, supplemented with specific subcortical units. In the second part, we implement these principles using the example of the hippocampal formation as a KIII unit. We demonstrate the learning and navigation principles using the Evolving Multi-module Mobile Agent (EMMA) in 2D software environment. [ABSTRACT FROM AUTHOR]

EEG measurements indicate the presence of common-mode, coherent oscillations in various cortical areas. In previous studies the KIII model has been introduced, which interprets the experimental observation as nonlinear, spatially distributed dynamical oscillations of coupled neural populations. In this paper we combine multiple KIII sets into the KIV model, which approximates the operation of the basic vertebrate forebrain together with the basal ganglia and motor systems. This paper outlines a summary description of the essential components of the KIV model, as the basis for future modeling of their cooperative dynamics guided by analysis of multichannel EEG in animals and humans. [Copyright &y& Elsevier]

A dynamical classification method is introduced based on the principle of encoding sensory information in oscillating spatio-temporal patterns. The method is used for the evaluation of EEG signals measured by spatially distributed electrodes over sensory cortices of rabbits. Our analysis reveals two types of patterns in neocortex. One type occurs with short latency after stimulus arrival. This early pattern represents the direct impact of a discriminated stimulus on the receiving cortex. The other type is endogenous and occurs with variable latency in the subsequent ∼1200 ms. It represents the destabilization of the spatio-temporal dynamics of the cortices and consequent phase transitions. [Copyright &y& Elsevier]

A fundamental tenet of the theory of deterministic chaos holds that infinitesimal variation in the initial conditions of a network that is operating in the basin of a low-dimensional chaotic attractor causes the various trajectories to diverge from each other quickly. This "sensitivity to initial conditions" might seem to hold promise for signal detection, owing to an implied capacity for distinguishing small differences in patterns. However, this sensitivity is incompatible with pattern classification, because it amplifies irrelevant differences in incomplete patterns belonging to the same class, and it renders the network easily corrupted by noise. Here a theory of stochastic chaos is developed, in which aperiodic outputs with 1/&fnof² spectra are formed by the interaction of globally connected nodes that are individually governed by point attractors under perturbation by continuous white noise. The interaction leads to a high-dimensional global chaotic attractor that governs the entire array of nodes. An example is our spatially distributed KIII network that is derived from studies of the olfactory system, and that is stabilized by additive noise modeled on biological noise sources. Systematic parameterization of the interaction strengths corresponding to synaptic gains among nodes representing excitatory and inhibitory neuron populations enables the formation of a robust high-dimensional global chaotic attractor. Reinforcement learning from examples of patterns to be classified using habituation and association creates lower dimensional local basins, which form a global attractor landscape with one basin for each class. Thereafter, presentation of incomplete examples of a test pattern leads to confinement of the KIII network in the basin corresponding to that pattern, which constitutes many-to-one generalization. The capture after learning is expressed by a stereotypical spatial pattern of amplitude modulation of a chaotic carrier wave. Sensitivity to... [ABSTRACT FROM AUTHOR]

Identifies the 58-kD substrate of the insulin receptor tyrosine kinase (IRS-58) as a target for Cdc42Hs, which is involved in cytoskeletal reorganization and required for neurite outgrowth in N1E-115 cells. Molecular mechanism by which Cdc42Hs regulates these processes; Characteristics of Cdc42Hs interaction with IRS-58.

Examines the use of representations and tools in the chemistry laboratory. Implication of representations and tools for chemistry learning; Aspects of mediational process; Purpose of scientists in using scientific representations and tools; Role of representations in student discourse about scientific concepts and principles.

Demonstrates the use of the computer program, Multimedia and Mental Models in Chemistry (4M:CHEM) for the study and teaching of chemistry. Enhancement of the teaching and learning of chemical concepts; Design rationale of the project; Focus of the prototype modules on chemical equilibrium; Examination of the effectiveness of the gaseous equilibrium module of 4M:CHEM.

Looks at the utilization of educational technology within the school system in the United States, highlighting the impact on at-risk students. How the media facilitates learning; Definition of the psychological aspects of learning; Recommendations on the effective use of computers.

Examines the impact of computer-based tools and embedded topical and rhetorical prompts on college writers. Correlation between the amount of planning and the quality compositions; Interaction between treatments and level of writer; Analysis of the verbal protocols.