DOCSLIB.ORG

Predicting and Facilitating the Emergence of Optimal Solutions for a Cooperative “Herding” Task and Testing Their Similitude to Contexts Utilizing Full-Body Motion

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Predicting and Facilitating the Emergence of Optimal Solutions for a Cooperative “Herding” Task and Testing Their Similitude to Contexts Utilizing Full-Body Motion Predicting and Facilitating the Emergence of Optimal Solutions for a Cooperative “Herding” Task and Testing their Similitude to Contexts Utilizing Full-Body Motion A dissertation submitted to the Division of Graduate Education and Research of the University of Cincinnati in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in the Department of Psychology of the McMicken College of Arts and Sciences by Patrick Nalepka M. A. University of Cincinnati, 2016 January, 2018 Committee Chair: Kevin Shockley, Ph.D. Committee: Michael J. Richardson, Ph.D. Anthony Chemero, Ph.D. Rachel W. Kallen, Ph.D. Paula Silva, Ph.D. ii Abstract Multi-agent activity is an emergent process, with the roles and responsibilities of individual actors a self-organized consequence of task-dynamic constraints and perturbations. The shepherding paradigm, first investigated by Nalepka, Kallen, Chemero, Saltzman, and Richardson, (2017), was directed towards exploring the emergence of stable multi-agent behavioral modes within dynamically changing task-environments. The task involved pairs of participants using their hands to contain a herd of autonomous and reactive “sheep” within a virtual game field projected on a tabletop display. Initially, all participants employed a search-and-recover (S&R) mode of behavior, moving from sheep-to-sheep to corral the herd to a target containment region in the center of the game field. However, a subset of dyads learned to coordinate their movements, forming an oscillating “wall” that contained the herd (termed coupled oscillatory containment)— a behavioral mode termed coupled oscillatory containment (COC)—which allowed dyads to achieve superior task performance. Experiment 1 investigated a potential control parameter to promote the emergence of COC behavior, as well as determine whether changes in oculomotor behavior might predict its emergence using recurrence quantification analysis (RQA). Experiment 2 sought to validate weather S&R and COC behavior also defined a more realistic herding situation that involved the full-body movement of participants in a large task space. Results indicated that manipulating task difficulty, by controlling how fast the sheep could move, promoted the use of more coordinated modes of behavior (Experiment 1 and 2). Significant changes in the determinism and complexity (entropy) of oculomotor behavior was observed two trials prior to the discovery of COC behavior using RQA (Experiment 1). In full-body herding (Experiment 2), a subset of dyads discovered a coordinated mode of behavior that involved a joint circling pattern in a fixed direction, with the frequency with which this behavioral mode emerged increasing with increases iii in task difficulty. Future work will investigate the informational basis that leads to the discovery of optimal solutions (i.e., oscillatory behavioral coordination) in the shepherding task, as well as the design of flexible, adaptive artificial (machine) systems that can learn and work alongside humans in these unstable task-environments. Keywords: multi-agent coordination, shepherding, strategy discovery, recurrence quantification analysis iv v Acknowledgments Special thanks to the committee who reviewed this work, my instructors, both past and present, for giving me the tools needed to carve my own path, and to my friends and family for supporting me throughout my academic career. I would like to acknowledge Elliot Saltzman and Maurice Lamb, who are collaborators on the “shepherding” project. I would also like to acknowledge Carl Bou Mansour, Christopher Riehm, Guilherme Sanches De Oliveira and Riley Mayr, who assisted in either equipment setup, data collection or data post-processing on one or more of the “shepherding” experiments throughout the years. Finally, I would like to acknowledge Melissa Kathryn Ridgley for her love and support the past four years that made my time in Cincinnati unforgettable. This research was supported by the National Institutes of Health (R01GM105045). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. vi Contents List of Figures ................................................................................................................................ ix CHAPTER 1 Introduction............................................................................................................... 1 Current Dissertation .................................................................................................................... 5 CHAPTER 2 Experiment 1 Facilitating and Predicting the Emergence of Coupled Oscillatory Containment .................................................................................................................................... 8 Dynamics of Behavioral Change ................................................................................................ 8 Predictors to Behavioral Change .............................................................................................. 11 Current Experiment ................................................................................................................... 16 Method ...................................................................................................................................... 18 Results ....................................................................................................................................... 25 Discussion ................................................................................................................................. 38 CHAPTER 3 Experiment 2 Shepherding Dynamic Similitude to Full-Body Movement ............ 44 Dynamic Similitude of Shepherding and Herding Behavior .................................................... 45 Dynamic Motor Primitives of Task-Dynamics ......................................................................... 47 Current Experiment ................................................................................................................... 48 Method ...................................................................................................................................... 48 Results ....................................................................................................................................... 52 Discussion ................................................................................................................................. 59 CHAPTER 4 General Discussion ................................................................................................. 67 Summary of Findings in Experiment 1 ..................................................................................... 67 Summary of Findings in Experiment 2 ..................................................................................... 68 vii General Conclusions ................................................................................................................. 69 References ..................................................................................................................................... 73 viii List of Figures Figure 1. Depiction of experiment setup from Nalepka, Kallen et al. (2017). .............................. 2 Figure 2. Illustrations of the behavioral modes observed in Nalepka, Kallen et al., (2017). ....... 4 Figure 3. Example gear-problems from Stephen, Boncoddo et al., (2009). ................................. 13 Figure 4. Change in Entropy during a phase transition in a Lorenz system indexed using recurrence quantification analysis. Taken from Stephen, Dixon and Isenhower (2009). ............................... 14 Figure 5. Example recurrence plot used for recurrence quantification analysis (RQA). Taken from Webber & Zbilut (2005). .............................................................................................................. 15 Figure 6. Depiction of Experiment 1. ........................................................................................... 19 Figure 7. Behavior classification for Experiment 1. ................................................................... 22 Figure 8. Summary plots depicting within-subject differences in behavioral mode on shepherding performance.for Experiment 1. .................................................................................................... 28 Figure 9. Summary plots depicting within-subject differences on ocular descriptive measures for unsuccessful performance, successful S&R performance, and successful COC performance for Experiment 1. ................................................................................................................................ 30 Figure 10. Summary plots depicting within-subject differences on ocular dynamics for unsuccessful performance, successful S&R performance, and successful COC performance for Experiment 1. ................................................................................................................................ 31 Figure 11. Summary plots depicting within-subject differences on ocular descriptive measures for unsuccessful performance, successful S&R performance, and successful COC performance for Experiment 1 using a 0.5 Hz cutoff criterion for COC trials. ...................................................... 33 ix Figure 12. Summary plots depicting within-subject differences on ocular dynamics for unsuccessful performance, successful
Load more

Recommended publications
  • Synergetic Environments Form, Social Behavior, and Coordination
    Synergetic Environments Form, Social Behavior, and Coordination “In the most general sense, computation is the process of storing, transmitting, and transforming information from one form to another” (Santa Fe Institute). ARCH 6307/4050/6050, IT IS 6010, ITCS 5010 Wednesdays 6:00pm-8:45pm, Storrs 255 School of Architecture, UNC Charlotte Prof Dr. Dimitrios Papanikolaou / [email protected] Office hours: Wednesdays 10am-12pm by appointment Office: Storrs 146 Premise The seminar critically reviews the evolution and design principles of systems of urban and territorial intelligence, from the ancient networks of optical telegraphy to today’s internet of things. Through discussions, we will examine technologies, systems, and mechanisms for turning information into decision and action in large scales; we will question the role of the physical environment, resource scarcity, and human behavior in shaping equilibrium conditions; we will investigate the role of data, modeling, and simulation in exploring dynamics of urban systems; and we will critically speculate where the design field of urban computing might be heading in the future. Objectives By the end of the course, students will develop a broad yet critical understanding of what urban intelligence is, how it can be constructed, and what limits it may reach, from a sociotechnical and a systemic perspective. Topics include: Theory and technologies of computation, information, communication, and human factors in relation to the built environment; systems theory; cybernetics; 1 ARCH 4050/6050 FALL 2017, SoA, UNC Charlotte urban dynamics; ecology; social cooperation; collective intelligence; game theory; mechanism design; as well as applications in mobility, resource allocation, sustainability, and energy. Method The course combines discussions, lectures, and workshops.
    [Show full text]
  • Paul Smolensky
    Vita PAUL SMOLENSKY Department of Cognitive Science 11824 Mays Chapel Road 239A Krieger Hall Timonium, MD 21093-1821 Johns Hopkins University (667) 229-9509 Baltimore, MD 21218-2685 May 5, 1955 (410) 516-5331 Citizenship: USA [email protected] cogsci.jhu.edu/directory/paul-smolensky/ DEGREES Ph.D. in mathematical physics, Indiana University, 1981. M.S. in physics, Indiana University, 1977. A.B. summa cum laude in physics, Harvard University, 1976. PROFESSIONAL POSITIONS Partner Researcher, Microsoft Research Artificial Intelligence, Redmond WA, Dec. 2016−present. Krieger-Eisenhower Professor of Cognitive Science, Johns Hopkins University, 2006–present. Full Professor, Department of Cognitive Science, Johns Hopkins University, 1994–2006. Chair, Department of Cognitive Science, Johns Hopkins University, Jan. 1997−June 1998 (Acting), July 1998−June 2000 Professor, Department of Computer Science, University of Colorado at Boulder, Full Professor, 1994–95 (on leave, 1994–95). Associate Professor, 1990–94. Assistant Professor, 1985–90. Assistant Research Cognitive Scientist (Assistant Professor – Research), Institute for Cognitive Science, University of California at San Diego, 1982–85. Visiting Scholar, Program in Cognitive Science, University of California at San Diego, 1981–82. Adjunct Professor, Department of Linguistics, University of Maryland at College Park, 1994–2010. Assistant Director, Center for Language and Speech Processing, Johns Hopkins University, 1995–2008. Director, NSF IGERT Training Program, Unifying the Science of Language, 2006−2015. Director, NSF IGERT Training Program in the Cognitive Science of Language, 1999−2006. International Chair, Inria Paris (National Institute for Research in Computer Science and Automation), 2017−2021. Visiting Scientist, Inserm-CEA Cognitive Neuroimaging Unit, NeuroSpin Center, Paris, France, 2016.
    [Show full text]
  • 8418B0e55972ecac157afb730f8
    Baltic Journal of Economic Studies Vol. 4, No. 2, 2018 DOI: https://doi.org/10.30525/2256-0742/2018-4-2-254-260 ANALYTIC OVERLOOK OF THE METHODOLOGY OF SYNERGETICS IN POSTNONCLASSICAL SCIENCE Viktor Yakimtsov1 Ukrainian National Forestry University, Ukraine Abstract. Purpose. This article reveals the main definitions of synergetics and methods that are being used in synergetic research. The differences-characteristics of classical, nonclassical, and postnonclassical science and their schematic illustration are described. There are criteria, by which the main methodological principles of synergetics are being chosen. The reasons that have caused an appearance of synergetics and its methodological apparatus and the framework of this apparatus are considered. The special aspects of nonlinearity of complicated systems, in our opinion, include the economic ones. Methodology. Such foreign and domestic scientists as Wiener N. (2003), Thom R. (1975, 1996), Prigogine I., Stengers I. (1986), Zang V.B. (1999), and Arnold V. (2004) have used methodological apparatus of synergetics in modern science. Methodologically synergetics is open for those new conceptions that are being formed in certain disciplines. Methodological principles of synergetics that cause the “colostral” principles are nonlinearity, nonclosure, and instability. The main principle – the rule of nonlinearity is a contravention of the principle of the super offer in the certain phenomenon (process): the result of adding the impacts on the system is not the adding these impacts’ results. The causes’ results cannot be added. This means that the result of adding the causes does not equal to the union of causes’ results. Results. For the synergy concept, the idea is typical that we see everything at once: the whole and its parts.
    [Show full text]
  • History of Cybernetics - R
    SYSTEMS SCIENCE AND CYBERNETICS – Vol. III - History of Cybernetics - R. Vallee HISTORY OF CYBERNETICS R. Vallée Université Paris-Nord, France Keywords: Autopoiesis, cybernetics, entropy, feedback, information, noise, observation, variety. Contents 1. Origins of Cybernetics 1.1 Contemporary Initiators 1.2 Past Contributors 2. Basic Concepts 2.1 Foundations 2.1.1.Retroaction 2.1.2.Information 2.2 Other Important Concepts 2.2.1.Variety 2.2.2.Observers 2.2.3.Epistemology and Praxiology 2.2.4.Isomorphism and Multidisciplinarity 3. Links with Other Theories 4. Future of Cybernetics Appendix Glossary Bibliography Biographical Sketch Summary The most important initiator of cybernetics was Norbert Wiener (l894–1964) with his book “Cybernetics or Control and Communication in the Animal and the Machine”. The contribution of Warren S. McCulloch (1898–1969) may be compared to that of Wiener. Cybernetics UNESCOhas also had precursors such as– A. M.EOLSS Ampère who introduced the word cybernétique as well as B. Trentowski who did the same in Polish. H. Schmidt, S. Odobleja in the 1930s, and P. Postelnicu in the early 1940s recognized the general importance of the idea of negative feedback. SAMPLE CHAPTERS The basic concepts of cybernetics are negative feedback and information. A famous example of negative feedback is given by Watt’s governor, the purpose of which is to maintain the speed of the wheel of a steam engine, at a given value, despite perturbations. The theory of information, mainly due to Claude E. Shannon, gives a measure of the unexpectedness of a message carried by a signal. Other traits of cybernetics must be noted, such as the “principle of requisite variety” introduced by W.
    [Show full text]
  • Synergetics - Hermann Haken
    SYSTEMS SCIENCE AND CYBERNETICS – Vol. I - System Theories: Synergetics - Hermann Haken SYSTEM THEORIES: SYNERGETICS Hermann Haken Institute for Theoretical Physics 1, Center of Synergetics,University of Stuttgart, D- 70550 Stuttgart. Keywords: Self-organization, complex systems, instability, order parameter, slaving principle, control parameter, structure, spatial structure, temporal structure, symmetry breaking, fluctuations, chaos, laser, fluids, chemical patterns, pattern recognition, medicine, psychology, evolution, information, mathematics, physics, economy, humanities, open systems Contents 1. Review of Subject Articles 2. Definition of Synergetics 3. Goals and General Approaches 4. Some Typical Examples 4.1 The Laser 4.2 The Convection Instability of Fluid Dynamics 4.3 An Example from Sociology and Linguistics 5. Basic Concepts 6. Applications to Science 6.1 Physics 6.2 Chemistry 6.3 Mechanical Engineering 6.4 Electrical Engineering 6.5 Biology 6.6 Psychology 7. Applications to Technology 7.1 Computer Science 7.2 Informatics 7.3 Telecommunication 8. Applications to Humanities 8.1 EconomyUNESCO – EOLSS 8.2 Sociology 8.3 Linguistics 8.4 Culture Including Art and Literature 8.5 Philosophy SAMPLE CHAPTERS 8.6 Epistemology 9. Mathematical Tools 10. Relations to Other Approaches Glossary Bibliography Biographical Sketch Summary ©Encyclopedia of Life Support Systems (EOLSS) SYSTEMS SCIENCE AND CYBERNETICS – Vol. I - System Theories: Synergetics - Hermann Haken S. has links to many fields of the natural sciences, to technology and to the humanities. The outstanding feature of S. is its goal to unearth common features in the macroscopic behavior of a great variety of otherwise quite different systems. As this article witnesses, this goal has been achieved by means of central concepts, such as instability, order parameters and the slaving principle.
    [Show full text]
  • The Road to Servomechanisms: the Influence of Cybernetics on Hayek From
    The Road to Servomechanisms: The Influence of Cybernetics on Hayek from The Sensory Order to the Social Order by Gabriel Oliva CHOPE Working Paper No. 2015-11 October 2015 The Road to Servomechanisms: The Influence of Cybernetics on Hayek from The Sensory Order to the Social Order Gabriel Oliva University of São Paulo [email protected] October 2015 Abstract: This paper explores the ways in which cybernetics influenced the works of F. A. Hayek from the late 1940s onward. It shows that the concept of negative feedback, borrowed from cybernetics, was central to Hayek’s attempt of giving an explanation of the principle to the emergence of human purposive behavior. Next, the paper discusses Hayek’s later uses of cybernetic ideas in his works on the spontaneous formation of social orders. Finally, Hayek’s view on the appropriate scope of the use of cybernetics is considered. Keywords: Cybernetics, F. A. Hayek, Norbert Wiener, Garrett Hardin, Ludwig von Bertalanffy. JEL Codes: B25, B31, C60 2 The Road to Servomechanisms: The Influence of Cybernetics on Hayek from The Sensory Order to the Social Order1 Gabriel Oliva When I began my work I felt that I was nearly alone in working on the evolutionary formation of such highly complex self-maintaining orders. Meanwhile, researches on this kind of problem – under various names, such as autopoiesis, cybernetics, homeostasis, spontaneous order, self-organisation, synergetics, systems theory, and so on – have become so numerous that I have been able to study closely no more than a few of them. (Hayek, 1988, p. 9) 1. Introduction It is widely recognized in the literature2 that the Austrian economist F.
    [Show full text]
  • Haken & Tschacher-2017-Prepublication Version
    How to modify psychopathological states? Hypotheses based on complex systems theory Hermann Haken* and Wolfgang Tschacher** *Institute for Theoretical Physics, University of Stuttgart, Germany [email protected] **University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland [email protected] corresponding author: Wolfgang Tschacher pre-publication version Abstract In our mathematical analysis based on the assumptions of complexity science, the emergence of a pattern is the result of a competition of modes, which each have a parameter value attached. In the context of visual pattern recognition, a specific connectionist system (the synergetic computer SC) was developed, which was derived from the assumptions of synergetics, a theory of complex systems. We adapted the processes of visual pattern recognition performed by the SC to a different context, psychopathology and therapeutic interventions, assuming these scenarios are analogous. The problem then becomes, under which conditions will a previously established psychopathological pattern not be restituted? We discuss several cases by using the equations of the SC. Translated to the psychopathological context, we interpret the mathematical findings and proofs in such a way that successful corrective interventions, e.g. by psychotherapy, should focus on one alternative pattern only. This alternative cognition-behavior-experience pattern is to be constructed individually by a therapist and a patient in the therapeutic alliance. The alternative pattern must be provided with higher valence (i.e. affective and motivational intensity) than possessed by the psychopathological pattern. Our findings do not support a linear symptom- oriented therapy approach based on specific intervention techniques, but rather a holistic approach. This is consistent with empirical results of psychotherapy research, especially the theory of common factors.
    [Show full text]
  • The Road to Servomechanisms: the Influence Of
    Running head: The Road to Servomechanisms THE ROAD TO SERVOMECHANISMS: THE INFLUENCE OF CYBERNETICS ON HAYEK FROM THE SENSORY ORDER TO THE SOCIAL ORDER Gabriel Oliva University of São Paulo ABSTRACT This paper explores the ways in which cybernetics influenced the works of F. A. Hayek from the late 1940s onward. It shows that the concept of negative feedback, borrowed from cybernetics, was central to Hayek’s attempt to explain the principle of the emergence of human purposive behavior. Next, the paper discusses Hayek’s later uses of cybernetic ideas in his works on the spontaneous formation of social orders. Finally, Hayek’s view on the appropriate scope of the use of cybernetics is considered. Keywords: Cybernetics, F. A. Hayek, Norbert Wiener, Garrett Hardin, Ludwig von Bertalanffy. JEL Codes: B25; B31; C60 The Road to Servomechanisms 2 When I began my work I felt that I was nearly alone in working on the evolutionary formation of such highly complex self-maintaining orders. Meanwhile, researches on this kind of problem – under various names, such as autopoiesis, cybernetics, homeostasis, spontaneous order, self-organisation, synergetics, systems theory, and so on – have become so numerous that I have been able to study closely no more than a few of them. (Hayek, 1988, p. 9) 1. INTRODUCTION Recently, the new approach of complexity economics has gained a significant number of followers. Some authors, such as Colander et al. (2004), claim that a “new orthodoxy” is gradually emerging inside the mainstream of economics and that this orthodoxy will be founded on the vision of the economy as a complex system.
    [Show full text]
  • 1 Toward a New Grand Theory of Development? Connectionism
    Toward a New Grand Theory of Development? Connectionism and Dynamic Systems Theory Re-Considered J.P. Spencer, M. S. C. Thomas, & J. McClelland (Editors). To be published by Oxford University Press. Contributors and Book Structure Part I: Overview Introduction: Spencer, Thomas & McClelland Chapter 1: Jay McClelland—overview of connectionist approaches [Note: Rough draft only] Chapter 2: Gregor Schöner—overview of dynamic systems approaches Part II: Case Studies Chapter 3: Karl Newell, Liu & Mayer-Kress Chapter 4: Whit Tabor Chapter 5: John Spencer, Jeff Johnson & John Lipinski [Note: Outline only] Chapter 6: Matthew Schlesinger Chapter 7: Yuko Munakata & Bruce Morton Chapter 8: Linda Smith [Note: Abstract only] Chapter 9: Daniela Corbetta Chapter 10: Han van der Maas & Maartje Raijmakers Chapter 11: Denis Mareschal, Robert Leech & Rick Cooper Chapter 12: Bob McMurray, Jessica Horst & Larissa Samuelson Part III: Reactions from the “outside” Chapter 13: Guy Van Orden & Heidi Kloos Chapter 14: Lisa Oakes, Nora Newcombe & Jodie Plumert Chapter 15: Tim Johnston & Bob Lickliter Part IV: Reactions from the “inside” Chapter 16: Jay McClelland Chapter 17: Gregor Schöner Chapter 18: Kim Plunkett Chapter 19: Michael Thomas & Fiona Richardson Chapter 20: Paul van Geert & Kurt Fischer Conclusion: Thomas, Spencer & McClelland 1 McClelland and Vallabha 7/23/2006 Page 1 Connectionist Models of Development: Mechanistic Dynamical Models with Emergent Dynamical Properties James L. McClelland and Gautam Vallabha The symbolic paradigm of cognitive modeling, championed by Minsky and Papert, Newell and Simon, and other pioneers of the 1950’s and 1960’s, remains very much alive and well today. Yet an alternative paradigm, first championed in the 1950’s by Rosenblatt, in which cognitive processes are viewed as emergent functions of a complex stochastic dynamical system, has continued to have adherents.
    [Show full text]
  • Chapter 1 the BACKGROUND to SYSTEMICS
    Chapter 1 THE BACKGROUND TO SYSTEMICS 1.1 Introduction 1.2 What is Systemics? 1.3 A short, introductory history 1.4 Fundamental theoretical concepts 1.4.1 Set theory 1.4.2 Set theory and systems 1.4.3 Formalizing systems 1.4.4 Formalizing Systemics 1.5 Sets, structured sets, systems and subsystems 1.6 Other approaches 1.1 Introduction This book is devoted to the problems arising when dealing with emergent behaviors in complex systems and to a number of proposals advanced to solve them. The main concept around which all arguments revolve, is that of Collective Being. The latter expression roughly denotes multiple systems in which each component can belong simultaneously to different subsystems. Typical instances are swarms, flocks, herds and even crowds, social groups, sometimes industrial organisations and perhaps the human cognitive system itself. The study of Collective Beings is, of course, a matter of necessity when dealing with systems whose elements are agents, each of which is capable of some form of cognitive processing. The subject of this book could therefore be defined as "the study of emergent collective behaviors within assemblies of cognitive agents". 2 Chapter 1 Needless to say, such a topic involves a wide range of applications attracting the attention of a large audience. It integrates contributions from Artificial Life, Swarm Intelligence, Economic Theory, but also from Statistical Physics, Dynamical Systems Theory and Cognitive Science. It concerns domains such as organizational learning, the development or emergence of ethics (metaphorically intended as social software), the design of autonomous robots and knowledge management in the post-industrial society.
    [Show full text]
  • Buzsaki G. Rhythms of the Brain.Pdf
    Rhythms of the Brain György Buzsáki OXFORD UNIVERSITY PRESS Rhythms of the Brain This page intentionally left blank Rhythms of the Brain György Buzsáki 1 2006 3 Oxford University Press, Inc., publishes works that further Oxford University’s objective of excellence in research, scholarship, and education. Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam Copyright © 2006 by Oxford University Press, Inc. Published by Oxford University Press, Inc. 198 Madison Avenue, New York, New York 10016 www.oup.com Oxford is a registered trademark of Oxford University Press All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Oxford University Press. Library of Congress Cataloging-in-Publication Data Buzsáki, G. Rhythms of the brain / György Buzsáki. p. cm. Includes bibliographical references and index. ISBN-13 978-0-19-530106-9 ISBN 0-19-530106-4 1. Brain—Physiology. 2. Oscillations. 3. Biological rhythms. [DNLM: 1. Brain—physiology. 2. Cortical Synchronization. 3. Periodicity. WL 300 B992r 2006] I. Title. QP376.B88 2006 612.8'2—dc22 2006003082 987654321 Printed in the United States of America on acid-free paper To my loved ones. This page intentionally left blank Prelude If the brain were simple enough for us to understand it, we would be too sim- ple to understand it.
    [Show full text]
  • Connective Environments 2
    ARCH7211: CONNECTIVE ENVIRONMENTS 2 DESIGN COMPUTATION STUDIO LAB Tue/Thu 10:00am-12:30pm DARTS and studio space. Course Instructor: Dr. Dimitris Papanikolaou / [email protected] / Urban Synergetics Lab / Office: Storrs 146 Teaching Assistant: Atefeh Mahdavi Goloujeh / [email protected] Course website: TBD Office Hours: Wed (by appointment). PREMISE The deeper information technology penetrates urban life, the more pressing is the urgency for architects, engineers, and planners not only to design more intelligent and participatory systems but also to understand their complex emerging behavior. During the past decades, the design of “smart” urban environments followed a command-and-control approach in which the city is seen as a commander for serving the needs of its users. Today, widespread expansion of computing, and simultaneous sharing of resources between people, makes this approach increasingly infeasible for the simple reason that the needs and wants of one individual may often conflict those of another. The course investigates design, engineering, and empirical assessment of participatory cyberphysical systems of common pool resources. We will look at how information, human behavior, and physical constraints, shape the capabilities of cyber- physical sociotechnical systems, and how novel interactive technologies and design strategies may enhance them. We envision a future in which coordination emerges collectively from how humans interact with data, with and through the physical environment. Towards this goal, students will prototype enabling hardware and software technologies to connect humans, objects and places; they will design control mechanisms across scales to drive human behavior towards desired collective outcomes; and they will test these ideas through strategic game experiments.
    [Show full text]