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Cortical evoked potentials are of interest primarily as tests of changing neuronal excitabilities accompanying normal brain function. The first three steps in the anal ysis of these complex waveforms are proper placement of electrodes for recording, the proper choice of electrical or sensory stimulus parameters, and the establish ment of behavioral control. The fourth is development of techniques for reliable measurement. Measurement consists of comparison of an unknown entity with a set of standard scales or dimensions having numerical attributes in preassigned degree. A physical object can be described by the dimensions of size, mass, density, etc. In addition there are dimensions such as location, velocity, weight, hardness, etc. Some of these dimensions can be complex (e. g. size depends on three or more subsidiary coordi nates), and some can be interdependent or nonorthogonal (e. g. specification of size and mass may determine density). In each dimension the unit is defined with refer ence to a standard physical entity, e. g. a unit of mass or length, and the result of measurement is expressed as an equivalence between the unknown and the sum of a specified number of units of that entity. The dimensions of a complex waveform are elementary waveforms from which that waveform can be built by simple addition. Any finite single-valued function of time is admissible. They are called basis functions (lO, 15), and they can be expressed in numeric as well as geometric form.
I think, therefore I am. The legendary pronouncement of philosopher René Descartes lingers as accepted wisdom in the Western world nearly four centuries after its author's death. But does thought really come first? Who actually runs the show: we, our thoughts, or the neurons firing within our brains? Walter J. Freeman explores how we control our behavior and make sense of the world around us. Avoiding determinism both in sociobiology, which proposes that persons' genes control their brains' functioning, and in neuroscience, which posits that their brains' disposition is molded by chemistry and environmental forces, Freeman charts a new course--one that gives individuals due credit and responsibility for their actions. Drawing upon his five decades of research in neuroscience, Freeman utilizes the latest advances in his field as well as perspectives from disciplines as diverse as mathematics, psychology, and philosophy to explicate how different human brains act in their chosen diverse ways. He clarifies the implications of brain imaging, by which neural activity can be observed during the course of normal movements, and shows how nonlinear dynamics reveals order within the fecund chaos of brain function.
Experimental evidence in humans and other mammalians indicates that complex neurodynamics is crucial for the emergence of higher-level intelligence. Dynamical neural systems with encoding in limit cycle and non-convergent attractors have gained increasing popularity in the past decade. The role of synchronization, desynchronization, and intermittent synchronization on cognition has been studied extensively by various authors, in particular by authors contributing to the present volume. This book addresses dynamical aspects of brain functions and cognition.
Understanding how the brain works is probably the greatest scientific and intellectual challenge of our generation. The cerebral cortex is the instrument by which we carry the most complex mental functions. Fortunately, there exists an immense body of knowledge concerning both cortical structure and the properties of single neurons in the cortex. With the advent of the supercomputer, there has been increased interest in neural network modeling. What is needed is a new approach to an understanding of the mammalian cerebral cortex that will provide a link between the physiological description and the computer model. This book meets that need by combining anatomy, physiology, and modeling to achieve a quantitative description of cortical function. The material is presented didactically, starting with descriptive anatomy and comprehensively examining all aspects of modeling. The book gradually leads the reader from the macroscopic cortical anatomy and standard electrophysiological properties of single neurons to neural network models and synfire chains. The most modern trends in neural network modeling are explored.
With the rise of the knowledge economy, the knowledge content of goods and services is going up just as their material content is declining. Economic value is increasingly seen to reside in the former - that is, in intangible assets - rather than in the latter. Yet we keep wanting to turn knowledge back into something tangible, something with definite boundaries which can be measured, manipulated, appropriated, and traded. In short, we want to reify knowledge. Scholars have been debating the nature of knowledge since the time of Plato. Many new insights have been gained from these debates, but little theoretical consensus has been achieved. Through six thematically linked chapters, the book articulates the theoretical approach to the production and distribution of knowledge that underpins Max Boisot's conceptual framework, the Information Space or I-Space. In this way the book looks to provide theoretical and practical underpinnings to Boisot's book Knowledge Assets (OUP, 1998). Following an introductory chapter, how knowledge relates to data and information is first examined in chapter 1, and how different economic actors - entrepreneurs, managers, etc - use knowledge as a basis for action is explored in chapter 2. Chapter 3 looks at how the heterogeneity of economic actors arises naturally from their respective data processing strategies in spite of any similarities in the data that they might share. Chapter 4 argues, contra much transaction-based economics, that an organizational order must have preceded a market order, something that should be reflected in any knowledge-based theory of the firm. Chapter 5 discusses the cultural and institutional significance of different kinds of knowledge flows. Finally, chapter 6 presents an agent-based simulation model, SimISpace, that illustrates how the I-Space might be applied to concrete problems such those of intellectual property rights. A concluding chapter proposes a research agenda based on the theorizing developed in the book. The approach the book sets out is used by a whole range of organizations to issues of knowledge management, policy, economics, and organizational and cultural change.
Products of modern artificial intelligence (AI) have mostly been formed by the views, opinions and goals of the “insiders”, i.e. people usually with engineering background who are driven by the force that can be metaphorically described as the pursuit of the craft of Hephaestus. However, since the present-day technology allows for tighter and tighter mergence of the “natural” everyday human life with machines of immense complexity, the responsible reaction of the scientific community should be based on cautious reflection of what really lies beyond AI, i.e. on the frontiers where the tumultuous ever-growing and ever-changing cloud of AI touches the rest of the world. The chapters of this boo are based on the selected subset of the presentations that were delivered by their respective authors at the conference “Beyond AI: Interdisciplinary Aspects of Artificial Intelligence” held in Pilsen in December 2011. From its very definition, the reflection of the phenomena that lie beyond AI must be inherently interdisciplinary. And so is this book: all the authors took part in a mutual transdisciplinary dialogue after explaining their views on AI not only to a narrow selection of their usual close peers with the same specialisation, but to a much broader audience of various experts from AI engineering, natural sciences, humanities and philosophy. The chapters of this book thus reflect results of such a dialogue.
The subject of chaos has invaded practically every area of the natural sciences. Weather patterns are referred to as chaotic. There are chemical reactions and chaotic evolution of insect populations. Atomic and molecular physics have also seen the emergence of the study of chaos in these microscopic domains. This book examines the issue of chaos in nonlinear and dynamical systems, quantum mechanics, biology, and economics.
The two volume set LNCS 5506 and LNCS 5507 constitutes the thoroughly refereed post-conference proceedings of the 15th International Conference on Neural Information Processing, ICONIP 2008, held in Auckland, New Zealand, in November 2008. The 260 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. 116 papers are published in the first volume and 112 in the second volume. The contributions deal with topics in the areas of data mining methods for cybersecurity, computational models and their applications to machine learning and pattern recognition, lifelong incremental learning for intelligent systems, application of intelligent methods in ecological informatics, pattern recognition from real-world information by svm and other sophisticated techniques, dynamics of neural networks, recent advances in brain-inspired technologies for robotics, neural information processing in cooperative multi-robot systems.
In this fascinating book, William R. Uttal raises the possibility that, however much we learn about the anatomy and physiology of the brain and psychology, we may never be able to cross the final bridge explaining how the mind is produced by the brain. Three main classes of mind-brain theory are considered and rejected: field theories, because they are based on a superficial analogy; single cell theories, because they emerge from a massive uncontrolled experimental program; and neural net theories, because they are constrained by combinatorial complexity. To support his argument, Uttal explores the empirical and conceptual foundations of these theoretical approaches and identifies flaws in their fundamental logic. The author concludes that the problems preventing solution of the mind-brain problem are intractable, yet well within the confines of natural science.
Self-Organizing Complexity in Psychological Systems offers a contemporary perspective on the mind through a compilation of original chapters written by some of the leading researchers in the area of complexity theory. In each of the chapters, the authors attempt to use complexity theory to inform and in some cases reformulate existing theories of brain function (Freeman; Grigsby & Osuch), personality (Grigsby & Osuch), psychic organization and structure (Goldstein; Piers), human development (Demos), psychopathology (Palombo; Piers) and psychotherapeutic change (Palombo).