Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

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Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence，非常有用的资料
The Springer Series on Bio-and Neurosystems publishes fundamental principles and state-of-the-art research at the intersection of biology, neuroscience, informa tion processing and the engineering sciences. The series covers general informatics methods and techniques, together with their use to answer biological or medical questions Of interest are both basics and new developments on traditional methods such as machine learning, artificial neural networks, statistical methods, nonlinear dynamics, information processing methods, and image and signal processing. New findings in biology and neuroscience obtained through informatics and engineering methods, topics in systems biology, medicine, neuroscience and ecology, as well as engineering applications such as robotic rehabilitation, health information tech nologies, and many more, are also examined. The main target group includes informaticians and engineers interested in biology, neuroscience and medicine as well as biologists and neuroscientists using computational and engineering tools Volumes published in the series include monographs, edited volumes, and selected conference proceedings. Books purposely devoted to supporting education at the graduate and post-graduate levels in bio-and neuroinformatics, computational biology and neuroscience, systems biology, systems neuroscience and other related areas are of particular interest Moreinformationaboutthisseriesathttp://www.springer.com/series/15821 Nikola k kasabov Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence 空 Springer Nikola k. kasabov Knowledge Engineering and Discovery Research Institute(KEDRD Auckland University of Technology Auckland. new zealand ISsN2520-8535 Issn 2520-8543 (electronic) Springer Series on Bio-and Neurosystems ISBN978-3-66257713-4 ISBN978-3-662-57715-8( e Book) https://doi.org/10.1007/978-3-662-57715-8 Library of Congress Control Number: 2018946569 C Springer-Verlag GmbH Germany, part of Springer Nature 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Printed on acid-free paper This Springer imprint is published by the registered company Springer-Verlag GmbH, dE part of Springer Nature The registered company address is: Heidelberger Platz 3, 14197 Berlin, Germany Time lives inside us and we live inside time 1 Levski-Apostola(1837-1873 Bulgarian Educator and Revolutionary To my mother Kapka nikolova Mankova-Kasabova(1920-2012 )and my father Kiril lvanoy Kasabov(1914-1996), who gave me the light of life, and for those who came earlier in time; to my family, Diana, Kapka and Assia, who give me the light of love; and to those who will come later in time I hope they will enjoy the light of life and the light of love as much as I do Foreword Professor Furber is ICL Professor of Computer Engineering in the School of Computer Science at the University of Manchester, UK. After completing his education at the University of Cambridge(BA, MA, MMath, Ph. D ) he spent the 1980s at Acorn Computers, where he was a principal designer of the BBC micro and the arM 32-bit RISC microprocessor. As of 2018, over 120 billion variants of the arm processor have been manufactured, powering much of the worlds mobile computing and embedded systems. He pioneered the development of SpiNNaker, a neuromorphic computer architecture that enables the implementation of massively parallel spiking neural network systems with a wide range of applications The last decade has seen an explosion in the deployment of artificial neural networks for machine learning applications ranging from consumer speech recog- nition systems through to vision systems for autonomous vehicles These artificial neural systems differ from biological neural systems in many important aspects, but most notably in their use of neurons with continuously varying outputs where biology predominantly uses spiking neurons-neurons that emit a pure electro-chemical unit impulse in response to recognising an input pattern the continuous output of the artificial neuron can be thought of as representing the Foreword mean firing rate of its biological equivalent but in using rates rather than spikes, the artificial network loses the ability to access the detailed spatio-temporal information that can be conveyed in a time sequence of spikes. Biological systems can clearly access this information, but how they use it effectively remains a mystery to Science Nik Kasabov has done as much as anyone to begin to unlock the secrets of the biological spatio-temporal patterns of spikes, and in this book, he reveals what he has learnt about those secrets and how he has applied that know ledge in exciting new ways. This is deep knowledge, and if we can harness such knowledge il brain-inspired ai systems, then the explosion in aI witnessed over the last decade will look like a damp squib in comparison with what is to follow. This book is not just a record of past work, but also a guidebook for an exciting future Steve Furber CBE, FRS, FREng Computer Science Department University of Manchester, UK Preface Everything exists and evolves within time-space and time-space is within every thing, from a molecule to the universe. Understanding the complex relationship between time and space has been one of the biggest scientific challenges of all times, including the understanding and modelling the time-space information processes in the human brain and understanding life. This is the strive for deep knowledge that has always been the main goal of the human race Now that an enormous amount of time-space data is available, science needs new methods to deal with the complexity of such data across domain areas Risk mitigation strategies from health to civil defence often depend on simple models But recent advances in machine learning offer the intriguing possibility that dis- astrous events, as diverse as strokes, earthquakes financial market crises, or degenerative brain diseases, could be predicted early if the patterns hidden deeply in the intricate and complex interactions between spatial and temporal components could be understood. Although such interactions are manifested at different spatial or temporal scales in different applications or domain areas, the same information processing principles may be applied a radically new approach to modelling such data and to obtaining deep knowledge is needed that could enable the creation of faster and significantly better machine learning and pattern recognition systems, offering the realistic prospect of much more accurate and earlier event prediction, and a better understanding of causal time-space relationships The term time-space coined in this book has two meanings The problem space, where temporal processes evolve in time; The functional space of time, as it goes by This book looks at evolving processes in time-space. It talks about how deep learning of time-space data is achieved in the human brain and how this results in deep knowledge, which is taken as inspiration to develop methods and systems for deep learning and deep knowledge representation in spiking neural networks SNN). And furthermore, how this could be used to develop a new type of artificial intelligence(AI) systems, here called brain-inspired AI (BI-AD. In turn, these BI-Al 【实例截图】
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