Each brain enlivens a body in interaction with the social and physical environment. Peter Zumthor's Therme at Vals exemplifies the interplay of interior with surroundings, and ways the actions of users fuse with their multi-modal experience. The action-perception cycle includes both practical and contemplative actions. We analyze what Louis Sullivan meant by "form ever follows function" but will more often talk of aesthetics and utility. Not only are action, perception and emotion intertwined, but so are remembering and imagination. Architectural design leads to the physical construction of buildings - but much of what our brains achieve can be seen as a form of mental construction. A first look at neuroscience offers schema theory as a bridge from cognitive processes to neural circuitry. Some architects fear that neuroscience will strip the architect of any creativity. In counterpoint, two-way reduction explores how neuroscience can "dissect" phenomenology by showing how first-person experiences arise from melding diverse subconscious processes. This raises the possibility that neuroscience can extend the effectiveness of architectural design by showing how different aspects of a building may affect human experience in ways that are not apparent to self-reflection"--
In Neural Organization, Arbib, Erdi, and Szentagothai integrate structural, functional, and dynamical approaches to the interaction of brain models and neurobiologcal experiments. Both structure-based "bottom-up" and function- based "top-down" models offer coherent concepts by which to evaluate the experimental data. The goal of this book is to point out the advantages of a multidisciplinary, multistrategied approach to the brain.Part I of Neural Organization provides a detailed introduction to each of the three areas of structure, function, and dynamics. Structure refers to the anatomical aspects of the brain and the relations between different brain regions. Function refers to skills and behaviors, which are explained by means of functional schemas and biologically based neural networks. Dynamics refers to the use of a mathematical framework to analyze the temporal change of neural activities and synaptic connectivities that underlie brain development and plasticity--in terms of both detailed single-cell models and large-scale network models.In part II, the authors show how their systematic approach can be used to analyze specific parts of the nervous system--the olfactory system, hippocampus, thalamus, cerebral cortex, cerebellum, and basal ganglia--as well as to integrate data from the study of brain regions, functional models, and the dynamics of neural networks. In conclusion, they offer a plan for the use of their methods in the development of cognitive neuroscience.
Various brain areas of mammals can phyletically be traced back to homologous structures in amphibians. The amphibian brain may thus be regarded as a kind of "microcosm" of the highly complex primate brain, as far as certain homologous structures, sensory functions, and assigned ballistic (pre-planned and pre-pro grammed) motor and behavioral processes are concerned. A variety of fundamental operations that underlie perception, cognition, sensorimotor transformation and its modulation appear to proceed in primate's brain in a way understandable in terms of basic principles which can be investigated more easily by experiments in amphibians. We have learned that progress in the quantitative description and evaluation of these principles can be obtained with guidance from theory. Modeling - supported by simulation - is a process of transforming abstract theory derived from data into testable structures. Where empirical data are lacking or are difficult to obtain because of structural constraints, the modeler makes assumptions and approximations that, by themselves, are a source of hypotheses. If a neural model is then tied to empirical data, it can be used to predict results and hence again to become subject to experimental tests whose resulting data in tum will lead to further improvements of the model. By means of our present models of visuomotor coordination and its modulation by state-dependent inputs, we are just beginning to simulate and analyze how external information is represented within different brain structures and how these structures use these operations to control adaptive behavior.
Unlike any other species, humans can learn and use language. This book explains how the brain evolved to make language possible, through what Michael Arbib calls the Mirror System Hypothesis. Because of mirror neurons, monkeys, chimps, and humans can learn by imitation, but only "complex imitation," which humans exhibit, is powerful enough to support the breakthrough to language. This theory provides a path from the openness of manual gesture, which we share with nonhuman primates, through the complex imitation of manual skills, pantomime, protosign (communication based on conventionalized manual gestures), and finally to protospeech. The theory explains why we humans are as capable of learning sign languages as we are of learning to speak. This fascinating book shows how cultural evolution took over from biological evolution for the transition from protolanguage to fully fledged languages. The author explains how the brain mechanisms that made the original emergence of languages possible, perhaps 100,000 years ago, are still operative today in the way children acquire language, in the way that new sign languages have emerged in recent decades, and in the historical processes of language change on a time scale from decades to centuries. Though the subject is complex, this book is highly readable, providing all the necessary background in primatology, neuroscience, and linguistics to make the book accessible to a general audience.
In Neural Organization, Arbib, Erdi, and Szentagothai integrate structural, functional, and dynamical approaches to the interaction of brain models and neurobiologcal experiments. Both structure-based "bottom-up" and function- based "top-down" models offer coherent concepts by which to evaluate the experimental data. The goal of this book is to point out the advantages of a multidisciplinary, multistrategied approach to the brain.Part I of Neural Organization provides a detailed introduction to each of the three areas of structure, function, and dynamics. Structure refers to the anatomical aspects of the brain and the relations between different brain regions. Function refers to skills and behaviors, which are explained by means of functional schemas and biologically based neural networks. Dynamics refers to the use of a mathematical framework to analyze the temporal change of neural activities and synaptic connectivities that underlie brain development and plasticity--in terms of both detailed single-cell models and large-scale network models.In part II, the authors show how their systematic approach can be used to analyze specific parts of the nervous system--the olfactory system, hippocampus, thalamus, cerebral cortex, cerebellum, and basal ganglia--as well as to integrate data from the study of brain regions, functional models, and the dynamics of neural networks. In conclusion, they offer a plan for the use of their methods in the development of cognitive neuroscience.
Computability theory is at the heart of theoretical computer science. Yet, ironically, many of its basic results were discovered by mathematical logicians prior to the development of the first stored-program computer. As a result, many texts on computability theory strike today's computer science students as far removed from their concerns. To remedy this, we base our approach to computability on the language of while-programs, a lean subset of PASCAL, and postpone consideration of such classic models as Turing machines, string-rewriting systems, and p. -recursive functions till the final chapter. Moreover, we balance the presentation of un solvability results such as the unsolvability of the Halting Problem with a presentation of the positive results of modern programming methodology, including the use of proof rules, and the denotational semantics of programs. Computer science seeks to provide a scientific basis for the study of information processing, the solution of problems by algorithms, and the design and programming of computers. The last 40 years have seen increasing sophistication in the science, in the microelectronics which has made machines of staggering complexity economically feasible, in the advances in programming methodology which allow immense programs to be designed with increasing speed and reduced error, and in the develop ment of mathematical techniques to allow the rigorous specification of program, process, and machine.
Each brain enlivens a body in interaction with the social and physical environment. Peter Zumthor's Therme at Vals exemplifies the interplay of interior with surroundings, and ways the actions of users fuse with their multi-modal experience. The action-perception cycle includes both practical and contemplative actions. We analyze what Louis Sullivan meant by "form ever follows function" but will more often talk of aesthetics and utility. Not only are action, perception and emotion intertwined, but so are remembering and imagination. Architectural design leads to the physical construction of buildings - but much of what our brains achieve can be seen as a form of mental construction. A first look at neuroscience offers schema theory as a bridge from cognitive processes to neural circuitry. Some architects fear that neuroscience will strip the architect of any creativity. In counterpoint, two-way reduction explores how neuroscience can "dissect" phenomenology by showing how first-person experiences arise from melding diverse subconscious processes. This raises the possibility that neuroscience can extend the effectiveness of architectural design by showing how different aspects of a building may affect human experience in ways that are not apparent to self-reflection"--
This is a book whose time has come-again. The first edition (published by McGraw-Hill in 1964) was written in 1962, and it celebrated a number of approaches to developing an automata theory that could provide insights into the processing of information in brainlike machines, making it accessible to readers with no more than a college freshman's knowledge of mathematics. The book introduced many readers to aspects of cybernetics-the study of computation and control in animal and machine. But by the mid-1960s, many workers abandoned the integrated study of brains and machines to pursue artificial intelligence (AI) as an end in itself-the programming of computers to exhibit some aspects of human intelligence, but with the emphasis on achieving some benchmark of performance rather than on capturing the mechanisms by which humans were themselves intelligent. Some workers tried to use concepts from AI to model human cognition using computer programs, but were so dominated by the metaphor "the mind is a computer" that many argued that the mind must share with the computers of the 1960s the property of being serial, of executing a series of operations one at a time. As the 1960s became the 1970s, this trend continued. Meanwhile, experi mental neuroscience saw an exploration of new data on the anatomy and physiology of neural circuitry, but little of this research placed these circuits in the context of overall behavior, and little was informed by theoretical con cepts beyond feedback mechanisms and feature detectors.
Simulation in NSL - Modeling in NSL - Schematic Capture System - User Interface and Graphical Windows - The Modeling Language NSLM - The Scripting Language NSLS - Adaptive Resonance Theory - Depth Perception - Retina - Receptive Fields - The Associative Search Network: Landmark Learning and Hill Climbing - A Model of Primate Visual-Motor Conditional Learning - The Modular Design of the Oculomotor System in Monkeys - Crowley-Arbib Saccade Model - A Cerebellar Model of Sensorimotor Adaptation - Learning to Detour - Face Recognition by Dynamic Link Matching - Appendix I : NSLM Methods - NSLJ Extensions - NSLC Extensions - NSLJ and NSLC Differences - NSLJ and NSLC Installation Instructions.
The study of formal languages and of related families of automata has long been at the core of theoretical computer science. Until recently, the main reasons for this centrality were connected with the specification and analy sis of programming languages, which led naturally to the following ques tions. How might a grammar be written for such a language? How could we check whether a text were or were not a well-formed program generated by that grammar? How could we parse a program to provide the structural analysis needed by a compiler? How could we check for ambiguity to en sure that a program has a unique analysis to be passed to the computer? This focus on programming languages has now been broadened by the in creasing concern of computer scientists with designing interfaces which allow humans to communicate with computers in a natural language, at least concerning problems in some well-delimited domain of discourse. The necessary work in computational linguistics draws on studies both within linguistics (the analysis of human languages) and within artificial intelligence. The present volume is the first textbook to combine the topics of formal language theory traditionally taught in the context of program ming languages with an introduction to issues in computational linguistics. It is one of a series, The AKM Series in Theoretical Computer Science, designed to make key mathematical developments in computer science readily accessible to undergraduate and beginning graduate students.
In the 1930s, mathematical logicians studied the notion of "effective comput ability" using such notions as recursive functions, A-calculus, and Turing machines. The 1940s saw the construction of the first electronic computers, and the next 20 years saw the evolution of higher-level programming languages in which programs could be written in a convenient fashion independent (thanks to compilers and interpreters) of the architecture of any specific machine. The development of such languages led in turn to the general analysis of questions of syntax, structuring strings of symbols which could count as legal programs, and semantics, determining the "meaning" of a program, for example, as the function it computes in transforming input data to output results. An important approach to semantics, pioneered by Floyd, Hoare, and Wirth, is called assertion semantics: given a specification of which assertions (preconditions) on input data should guarantee that the results satisfy desired assertions (postconditions) on output data, one seeks a logical proof that the program satisfies its specification. An alternative approach, pioneered by Scott and Strachey, is called denotational semantics: it offers algebraic techniques for characterizing the denotation of (i. e. , the function computed by) a program-the properties of the program can then be checked by direct comparison of the denotation with the specification. This book is an introduction to denotational semantics. More specifically, we introduce the reader to two approaches to denotational semantics: the order semantics of Scott and Strachey and our own partially additive semantics.
The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of "intelligent" machines. Thus the title of this volume has two interpretations: It presents models and data on the dynamic interactions occurring in the brain, and it exhibits the dynamic interactions between research in computational neuroscience and in neural computing, as scientists seek to find common principles to guide the understanding of the living brain and the design of artificial neural networks. This collection of contributions presents the current state of research, future trends and open problems in an exciting field of today's science.
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