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Optimized interaction of the brain with environment requires the four-dimensional representation of space-time in the neuronal circuits. Information processing is an important part of this interaction, which is critically dependent on time-dimension. Information processing has played an important role in the evolution of mammals, and has reached a level of critical importance in the lives of primates, particularly the humans. The entanglement of time-dimension with information processing in the brain is not clearly understood at present. Time-dimension in physical world – the environment of an organism – can be represented by the interval of a pendulum swing (the cover page depicts temporal unit with the help of a swinging pendulum). Temporal units in neural processes are represented by regular activities of pacemaker neurons, tonic regular activities of proprioceptors and periodic fluctuations in the excitability of neurons underlying brain oscillations. Moreover, temporal units may be representationally associated with time-bins containing bits of information (see the Editorial), which may be studied to understand the entanglement of time-dimension with neural information processing. The optimized interaction of the brain with environment requires the calibration of neural temporal units. Neural temporal units are calibrated as a result of feedback processes occurring during the interaction of an organism with environment. Understanding the role of time-dimension in the brain information processing requires a multidisciplinary approach, which would include psychophysics, single cell studies and brain recordings. Although this Special Issue has helped us move forward on some fronts, including theoretical understanding of calibration of time-information in neural circuits, and the role of brain oscillations in timing functions and integration of asynchronous sensory information, further advancements are needed by developing correct computational tools to resolve the relationship between dynamic, hierarchical neural oscillatory structures that form during the brain’s interaction with environment.
The three volume set LNCS 7062, LNCS 7063, and LNCS 7064 constitutes the proceedings of the 18th International Conference on Neural Information Processing, ICONIP 2011, held in Shanghai, China, in November 2011. The 262 regular session papers presented were carefully reviewed and selected from numerous submissions. The papers of part I are organized in topical sections on perception, emotion and development, bioinformatics, biologically inspired vision and recognition, bio-medical data analysis, brain signal processing, brain-computer interfaces, brain-like systems, brain-realistic models for learning, memory and embodied cognition, Clifford algebraic neural networks, combining multiple learners, computational advances in bioinformatics, and computational-intelligent human computer interaction. The second volume is structured in topical sections on cybersecurity and data mining workshop, data mining and knowledge doscovery, evolutionary design and optimisation, graphical models, human-originated data analysis and implementation, information retrieval, integrating multiple nature-inspired approaches, kernel methods and support vector machines, and learning and memory. The third volume contains all the contributions connected with multi-agent systems, natural language processing and intelligent Web information processing, neural encoding and decoding, neural network models, neuromorphic hardware and implementations, object recognition, visual perception modelling, and advances in computational intelligence methods based pattern recognition.
Nonhuman primates (referred to here as primates) provide an invaluable source of information for a multitude of scientific fields including ecology, evolution, biology, psychology, and biomedicine. This volume addresses various topics related to primate research that includes phylogeny, natural observations, primate ecosystem, sociocognitive abilities, disease pathophysiology, and neuroscience. Topics discussed here provide a platform for which to address human evolution, habitat preservation, human psyche, and pathophysiology of disease.
The human brain is arguably the most complex system we know of. Over the past few decades, scientists have developed several methods and theories for studying the functional organization of the brain, and how cognitive/perceptual/emotional processes might arise from the brain's electro-chemical-computational dynamics. These methods facilitated and inspired large literatures on brain-behavior links, and yet there remains a seemingly endless chasm between our simple impoverished models and the unfathomable complexity of the human brain. The purpose of this Research Topic is to ask the question: Are we thinking about thinking about the brain in the right way? In most scientific publications, researchers describe a broad and established theoretical framework and briefly describe new experimental results consistent with that framework. Here, we encourage authors to express ideas that might be radical, controversial, or different from established theories or methodological approaches. Supportive data are highly encouraged. The aim is to spark discussions about the validity and usefulness of current methodological/theoretical approaches in human cognitive neuroscience, with the goal of inspiring new approaches and ways of thinking. Neuroscience is a massive field with myriad methodological and theoretical approaches; we focus this Research Topic on approaches most commonly used in human neuroscience.
Mind computation is a hot topic of intelligence science. It is explored by computing to explain the theoretical basis of human intelligence. Through long-term research, a mind model CAM (Consciousness and Memory) is proposed, which provides a general framework for brain-like intelligence and brain-like intelligent systems.This novel book centers on mind model CAM, systematically discusses the theoretical basis of mind computation in nine chapters. Because of its advanced progresses on brain-like intelligence, it is useful as a primary reference volume for professionals and graduate students in intelligence science, cognitive science and artificial intelligence.
A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.
Neuropsychological research on the neural basis of behavior generally asserts that brain mechanisms ultimately suffice to explain all psychologically described phenomena. This assumption stems from the idea that the brain consists entirely of material particles and fields, and that all causal mechanisms relevant to neuroscience can be formulated solely in terms of properties of these elements. Contemporary basic physical theory differs from classic physics on the important matter of how consciousness of human agents enters into the structure of empirical phenomena. The new principles contradict the older idea that local mechanical processes alone account for the structure of all empirical data. Contemporary physical theory brings directly into the overall causal structure certain psychologically described choices made by human agents about how they will act. This key development in basic physical theory is applicable to neuroscience. This book explores this new framework.
The first goal of this book is to extend Two Minds originating from behavioral economics to the domain of interaction, where the time dimension has to be dealt with rigorously; in human–machine interaction, it is of crucial importance how synchronization between conscious processes and unconscious processes is established for a sense of smoothness, and how memory processes and action selection processes are coordinated. The first half this book describes the theory in detail. The book begins by outlining the whole view of the theory consisting of action selection processes and memorization processes, and their interactions. Then, a detailed description for action selection processes theorized as a nonlinear dynamic human behavior model with real-time constraints is provided, followed by a description for memorization processes. Also, implications of the theory to human–machine interactions are discussed. The second goal of this book is to provide a methodology to study how Two Minds works in practice when people use interactive systems. The latter half of this book describes theory practices in detail. A new methodology called Cognitive Chrono-Ethnography (CCE) is introduced, which adds the time dimension to Hutchins’ Cognitive Ethnography, in order to practice "know the users" systematically by designing user studies based on a simulation of users’ mental operations controlled by Two Minds. The author then shows how CCE has been applied to understanding the ways in which people navigate in real physical environments by walking and by car, respectively, and explores the possibility of applying CCE to predict people’s future needs. This is not for understanding how people use interfaces at present but to predict how people want to use the interfaces in the future given they are currently using them in a certain way Finally, the book concludes by describing implications of human–machine interactions that are carried out while using modern artefacts for people's cognitive development from birth, on the basis of the theories of action selection and memorization.