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    Cover
    Artificial Intelligence in the Age of Neural Networks and Brain Computing
    Copyright
    List of Contributors
    Editors' Brief Biographies
    Introduction
    1 - Nature's Learning Rule: The Hebbian-LMS Algorithm
    1. Introduction
    2. ADALINE and the LMS Algorithm, From the 1950s
    3. Unsupervised Learning With Adaline, From the 1960s
    4. Robert Lucky's Adaptive Equalization, From the 1960s
    5. Bootstrap Learning With a Sigmoidal Neuron
    6. Bootstrap Learning With a More “Biologically Correct” Sigmoidal Neuron
    6.1 Training a Network of Hebbian-LMS Neurons
    7. Other Clustering Algorithms
    7.1 K-Means Clustering
    7.2 Expectation-Maximization Algorithm
    7.3 Density-Based Spatial Clustering of Application With Noise Algorithm
    7.4 Comparison Between Clustering Algorithms
    8. A General Hebbian-LMS Algorithm
    9. The Synapse
    10. Postulates of Synaptic Plasticity
    11. The Postulates and the Hebbian-LMS Algorithm
    12. Nature's Hebbian-LMS Algorithm
    13. Conclusion
    Appendix: Trainable Neural Network Incorporating Hebbian-LMS Learning
    ACKNOWLEDGMENTS
    References
    2 - A Half Century of Progress Toward a Unified Neural Theory of Mind and Brain With Applications to Autonomous Adaptive Agents ...
    1. Towards a Unified Theory of Mind and Brain
    2. A Theoretical Method for Linking Brain to Mind: The Method of Minimal Anatomies
    3. Revolutionary Brain Paradigms: Complementary Computing and Laminar Computing
    4. The What and Where Cortical Streams Are Complementary
    5. Adaptive Resonance Theory
    6. Vector Associative Maps for Spatial Representation and Action
    7. Homologous Laminar Cortical Circuits for All Biological Intelligence: Beyond Bayes
    8. Why a Unified Theory Is Possible: Equations, Modules, and Architectures
    9. All Conscious States Are Resonant States
    10. The Varieties of Brain Resonances and the Conscious Experiences That They Support
    11. Why Does Resonance Trigger Consciousness?
    12. Towards Autonomous Adaptive Intelligent Agents and Clinical Therapies in Society
    References
    3 - Third Gen AI as Human Experience Based Expert Systems
    1. Introduction
    2. Third Gen AI
    2.1 Maxwell–Boltzmann Homeostasis [8]
    2.2 The Inverse Is Convolution Neural Networks
    2.3 Fuzzy Membership Function (FMF and Data Basis)
    3. MFE Gradient Descent
    3.1 Unsupervised Learning Rule
    4. Conclusion
    ACKNOWLEDGMENT
    References
    Further Reading
    4 - The Brain-Mind-Computer Trichotomy: Hermeneutic Approach
    1. Dichotomies
    1.1 The Brain-Mind Problem
    1.2 The Brain-Computer Analogy/Disanalogy
    1.3 The Computational Theory of Mind
    2. Hermeneutics
    2.1 Second-Order Cybernetics
    2.2 Hermeneutics of the Brain
    2.3 The Brain as a Hermeneutic Device
    2.4 Neural Hermeneutics
    3. Schizophrenia: A Broken Hermeneutic Cycle
    3.1 Hermeneutics, Cognitive Science, Schizophrenia
    4. Toward the Algorithms of Neural/Mental Hermeneutics
    4.1 Understanding Situations: Needs Hermeneutic Interpretation
    ACKNOWLEDGMENTS
    References
    Further Reading
    5 - From Synapses to Ephapsis: Embodied Cognition and Wearable Personal Assistants
    1. Neural Networks and Neural Fields
    2. Ephapsis
    3. Embodied Cognition
    4. Wearable Personal Assistants
    References
    6 - Evolving and Spiking Connectionist Systems for Brain-Inspired Artificial Intelligence
    1. From Aristotle's Logic to Artificial Neural Networks and Hybrid Systems
    1.1 Aristotle's Logic and Rule-Based Systems for Knowledge Representation and Reasoning
    1.2 Fuzzy Logic and Fuzzy Rule–Based Systems
    1.3 Classical Artificial Neural Networks (ANN)
    1.4 Integrating ANN With Rule-Based Systems: Hybrid Connectionist Systems
    1.5 Evolutionary Computation (EC): Learning Parameter Values of ANN Through Evolution of Individual Models as Part of Populatio ...
    2. Evolving Connectionist Systems (ECOS)
    2.1 Principles of ECOS
    2.2 ECOS Realizations and AI Applications
    3. Spiking Neural Networks (SNN) as Brain-Inspired ANN
    3.1 Main Principles, Methods, and Examples of SNN and Evolving SNN (eSNN)
    3.2 Applications and Implementations of SNN for AI
    4. Brain-Like AI Systems Based on SNN. NeuCube. Deep Learning Algorithms
    4.1 Brain-Like AI Systems. NeuCube
    4.2 Deep Learning and Deep Knowledge Representation in NeuCube SNN Models: Methods and AI Applications [6]
    4.2.1 Supervised Learning for Classification of Learned Patterns in a SNN Model
    4.2.2 Semisupervised Learning
    5. Conclusion
    ACKNOWLEDGMENT
    References
    7 - Pitfalls and Opportunities in the Development and Evaluation of Artificial Intelligence Systems
    1. Introduction
    2. AI Development
    2.1 Our Data Are Crap
    2.2 Our Algorithm Is Crap
    3. AI Evaluation
    3.1 Use of Data
    3.2 Performance Measures
    3.3 Decision Thresholds
    4. Variability and Bias in Our Performance Estimates
    5. Conclusion
    ACKNOWLEDGMENT
    References
    8 - The New AI: Basic Concepts, and Urgent Risks and Opportunities in the Internet of Things
    1. Introduction and Overview
    1.1 Deep Learning and Neural Networks Before 2009–11
    1.2 The Deep Learning Cultural Revolution and New Opportunities
    1.3 Need and Opportunity for a Deep Learning Revolution in Neuroscience
    1.4 Risks of Human Extinction, Need for New Paradigm for Internet of Things
    2. Brief History and Foundations of the Deep Learning Revolution
    2.1 Overview of the Current Landscape
    2.2 How the Deep Revolution Actually Happened
    2.3 Backpropagation: The Foundation Which Made This Possible
    2.4 CoNNs, ﹥3 Layers, and Autoencoders: The Three Main Tools of Today's Deep Learning
    3. From RNNs to Mouse-Level Computational Intelligence: Next Big Things and Beyond
    3.1 Two Types of Recurrent Neural Network
    3.2 Deep Versus Broad: A Few Practical Issues
    3.3 Roadmap for Mouse-Level Computational Intelligence (MLCI)
    3.4 Emerging New Hardware to Enhance Capability by Orders of Magnitude
    4. Need for New Directions in Understanding Brain and Mind
    4.1 Toward a Cultural Revolution in Hard Neuroscience
    4.2 From Mouse Brain to Human Mind: Personal Views of the Larger Picture
    5. Information Technology (IT) for Human Survival: An Urgent Unmet Challenge
    5.1 Examples of the Threat from Artificial Stupidity
    5.2 Cyber and EMP Threats to the Power Grid
    5.3 Threats From Underemployment of Humans
    5.4 Preliminary Vision of the Overall Problem, and of the Way out
    References
    9 - Theory of the Brain and Mind: Visions and History
    1. Early History
    2. Emergence of Some Neural Network Principles
    3. Neural Networks Enter Mainstream Science
    4. Is Computational Neuroscience Separate From Neural Network Theory?
    5. Discussion
    References
    10 - Computers Versus Brains: Game Is Over or More to Come?
    1. Introduction
    2. AI Approaches
    3. Metastability in Cognition and in Brain Dynamics
    4. Multistability in Physics and Biology
    5. Pragmatic Implementation of Complementarity for New AI
    ACKNOWLEDGMENTS
    References
    11 - Deep Learning Approaches to Electrophysiological Multivariate Time-Series Analysis
    1. Introduction
    2. The Neural Network Approach
    3. Deep Architectures and Learning
    3.1 Deep Belief Networks
    3.2 Stacked Autoencoders
    3.3 Convolutional Neural Networks
    4. Electrophysiological Time-Series
    4.1 Multichannel Neurophysiological Measurements of the Activity of the Brain
    4.2 Electroencephalography (EEG)
    4.3 High-Density Electroencephalography
    4.4 Magnetoencephalography
    5. Deep Learning Models for EEG Signal Processing
    5.1 Stacked Autoencoders
    5.2 Summary of the Proposed Method for EEG Classification
    5.3 Deep Convolutional Neural Networks
    5.4 Other DL Approaches
    6. Future Directions of Research
    6.1 DL Interpretability
    6.2 Advanced Learning Approaches in DL
    6.3 Robustness of DL Networks
    7. Conclusions
    References
    Further Reading
    12 - Computational Intelligence in the Time of Cyber-Physical Systems and the Internet of Things
    1. Introduction
    2. System Architecture
    3. Energy Harvesting and Management
    3.1 Energy Harvesting
    3.2 Energy Management and Research Challenges
    4. Learning in Nonstationary Environments
    4.1 Passive Adaptation Modality
    4.2 Active Adaptation Modality
    4.3 Research Challenges
    5. Model-Free Fault Diagnosis Systems
    5.1 Model-Free Fault Diagnosis Systems
    5.2 Research Challenges
    6. Cybersecurity
    6.1 How Can CPS and IoT Be Protected From Cyberattacks?
    6.2 Case Study: Darknet Analysis to Capture Malicious Cyberattack Behaviors
    7. Conclusions
    ACKNOWLEDGMENTS
    References
    13 - Multiview Learning in Biomedical Applications
    1. Introduction
    2. Multiview Learning
    2.1 Integration Stage
    2.2 Type of Data
    2.3 Types of Analysis
    3. Multiview Learning in Bioinformatics
    3.1 Patient Subtyping
    3.2 Drug Repositioning
    4. Multiview Learning in Neuroinformatics
    4.1 Automated Diagnosis Support Tools for Neurodegenerative Disorders
    4.2 Multimodal Brain Parcellation
    5. Deep Multimodal Feature Learning
    5.1 Deep Learning Application to Predict Patient’s Survival
    5.2 Multimodal Neuroimaging Feature Learning With Deep Learning
    6. Conclusions
    References
    14 - Meaning Versus Information, Prediction Versus Memory, and Question Versus Answer
    1. Introduction
    2. Meaning Versus Information
    3. Prediction Versus Memory
    4. Question Versus Answer
    5. Discussion
    6. Conclusion
    ACKNOWLEDGMENTS
    References
    15 - Evolving Deep Neural Networks
    1. Introduction
    2. Background and Related Work
    3. Evolution of Deep Learning Architectures
    3.1 Extending NEAT to Deep Networks
    3.2 Cooperative Coevolution of Modules and Blueprints
    3.3 Evolving DNNs in the CIFAR-10 Benchmark
    4. Evolution of LSTM Architectures
    4.1 Extending CoDeepNEAT to LSTMs
    4.2 Evolving DNNs in the Language Modeling Benchmark
    5. Application Case Study: Image Captioning for the Blind
    5.1 Evolving DNNs for Image Captioning
    5.2 Building the Application
    5.3 Image Captioning Results
    6. Discussion and Future Work
    7. Conclusion
    References
    Index
    A
    B
    C
    D
    E
    F
    G
    H
    I
    J
    K
    L
    M
    N
    O
    P
    Q
    R
    S
    T
    U
    V
    W
    X
    Back Cover
    Artificial Intelligence in the Age of Neural Networks and Brain Computing Edited by Robert Kozma University of Memphis, Department of Mathematics, Memphis, TN, United States University of Massachusetts Amherst, Department of Computer Science, Amherst, MA, United States Cesare Alippi Politecnico di Milano, Milano, Italy Universita` della Svizzera italiana, Lugano, Switzerland Yoonsuck Choe Samsung Research & Texas A&M University, College Station, TX, United States Francesco Carlo Morabito University Mediterranea of Reggio Calabria, Reggio Calabria, Italy
    Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2019 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-815480-9 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals Publisher: Mara Conner Acquisition Editor: Chris Katsaropoulos Editorial Project Manager: John Leonard Production Project Manager: Kamesh Ramajogi Cover Designer: Christian J. Bilbow Typeset by TNQ Technologies
    List of Contributors Cesare Alippi Politecnico di Milano, Milano, Italy; Universita` della Svizzera Italiana, Lugano, Switzerland David G. Brown US Food and Drug Administration, Silver Spring, MD, United States Maurizio Campolo NeuroLab, DICEAM, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy Yoonsuck Choe Samsung Research, Seoul, Korea; Department of Computer Science and Engineering, Texas A&M University Nigel Duffy Sentient Technologies, Inc., San Francisco, CA, United States Pe´ter E´rdi Center for Complex Systems Studies, Kalamazoo College, Kalamazoo, MI, United States; Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest, Hungary Daniel Fink Sentient Technologies, Inc., San Francisco, CA, United States Olivier Francon Sentient Technologies, Inc., San Francisco, CA, United States Paola Galdi NeuRoNe Lab, DISA-MIS, University of Salerno, Fisciano, Italy Stephen Grossberg Center for Adaptive Systems Graduate Program in Cognitive and Neural Systems, Departments of Mathematics & Statistics, Psychological & Brain Sciences, and Biomedical Engineering, Boston University, Boston, MA, United States Babak Hodjat Sentient Technologies, Inc., San Francisco, CA, United States Cosimo Ieracitano NeuroLab, DICEAM, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy Nikola Kasabov Knowledge Engineering and Discovery Research Institute e KEDRI, Auckland University of Technology, Auckland, New Zealand xv
    xvi List of Contributors Youngsik Kim Department of Electrical Engineering, Stanford University, Stanford, CA, United States Robert Kozma University of Memphis, Department of Mathematics, Memphis, TN, United States; University of Massachusetts Amherst, Department of Computer Science, Amherst, MA, United States Daniel S. Levine University of Texas at Arlington, Arlington, TX, United States Jason Liang Sentient Technologies, Inc., San Francisco, CA, United States; The University of Texas at Austin, Austin, TX, United States Nadia Mammone NeuroLab, DICEAM, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy Elliot Meyerson Sentient Technologies, Inc., San Francisco, CA, United States; The University of Texas at Austin, Austin, TX, United States Risto Miikkulainen Sentient Technologies, Inc., San Francisco, CA, United States; The University of Texas at Austin, Austin, TX, United States Francesco Carlo Morabito NeuroLab, DICEAM, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy Arshak Navruzyan Sentient Technologies, Inc., San Francisco, CA, United States Roman Ormandy Embody Corporation, Los Gatos, CA, United States Seiichi Ozawa Kobe University, Kobe, Japan Dookun Park Department of Electrical Engineering, Stanford University, Stanford, CA, United States Jose Krause Perin Department of Electrical Engineering, Stanford University, Stanford, CA, United States Bala Raju Sentient Technologies, Inc., San Francisco, CA, United States
    List of Contributors xvii Aditya Rawal Sentient Technologies, Inc., San Francisco, CA, United States; The University of Texas at Austin, Austin, TX, United States Frank W. Samuelson US Food and Drug Administration, Silver Spring, MD, United States Angela Serra NeuRoNe Lab, DISA-MIS, University of Salerno, Fisciano, Italy Hormoz Shahrzad Sentient Technologies, Inc., San Francisco, CA, United States Harold Szu Catholic University of America, Washington, DC, United States Roberto Tagliaferri NeuRoNe Lab, DISA-MIS, University of Salerno, Fisciano, Italy The Al Working Group Catholic University of America, Washington, DC, United States Paul J. Werbos US National Science Foundation, retired, and IntControl LLC, Arlington, VA, United States Bernard Widrow Department of Electrical Engineering, Stanford University, Stanford, CA, United States
    Editors’ Brief Biographies Robert Kozma ROBERT KOZMA is a Professor of Mathe- matics, Director of Center for Large-Scale Integrated Optimization and Networks, Univer- sity of Memphis, TN, USA; and Visiting Professor of Computer Science, University of Massachusetts Amherst. He is Fellow of IEEE and Fellow of the International Neural Network Society (INNS). He is President of INNS (2017e18) and serves on the Governing Board of IEEE Systems, Man, and Cybernetics Society (2016e18). He has served on the AdCom of the IEEE Computational Intelligence Society and on the Board of Governors of INNS. Dr. Kozma is the recipient of the INNS Gabor Award (2011) and Alumni Association Distinguished Research Achievement Award (2010). He has also served as Senior Fellow (2006e08) of US Air Force Research Laboratory. His research includes robust decision support systems, autonomous robotics and navigation, distributed sensor networks, brain networks, and braine computer interfaces. Cesare Alippi CESARE ALIPPI is a Professor with the Politec- nico di Milano, Milano, Italy, and Universita` della Svizzera italiana, Lugano, Switzerland. He is an IEEE Fellow, Member of the Adminis- trative Committee of the IEEE Computational Intelligence Society, Board of Governors member of the International Neural Network Society (INNS), and Board of Directors member of the European Neural Network Society. In 2018, he received the IEEE CIS Outstanding Computa- tional Intelligence Magazine Award, the (2016) Gabor award from the INNS, and the IEEE Computational Intelligence Society Outstanding Transactions on Neural Networks and Learning Systems Paper Award; and in 2004, he received the IEEE Instrumentation and Mea- surement Society Young Engineer Award. His current research activity addresses adaptation and learning in nonstationary and time-variant environments, graphs learning, and intelligence for embedded and cyberphysical systems. xix
    xx Editors’ Brief Biographies Yoonsuck Choe YOONSUCK CHOE is a Corporate Vice Presi- dent at Samsung Research Artificial Intelligence Center (2017epresent) and Professor and Direc- tor of the Brain Networks Laboratory at Texas A&M University (2001epresent). He received his PhD degree in computer science from the University of Texas at Austin in 2001. His research interests are in neural networks and computational neuroscience and has published over 100 papers on these topics, including a research monograph on computations in the vi- sual cortex. He serves on the Executive Commit- tee of the International Neural Network Society (INNS). He served as Program Chair and General Chair for IJCNN2015 and IJCNN2017, respectively, and served on the editorial board of IEEE Transactions on Neural Networks and the INNS journal Neural Networks. Francesco Carlo Morabito FRANCESCO CARLO MORABITO is a Profes- sor of Electrical Engineering with the University “Mediterranea” of Reggio Calabria, Italy, and the Former Dean of the Faculty of Engineering (2001e08) and Deputy Rector of the University. He is now serving as the Vice-Rector for Interna- tional Relations (2012e18). He is a Foreign Member of the Royal Academy of Doctors, Spain (2004), and Member of the Institute of Spain, Barcelona Economic Network (2017). He served as the Governor of the International Neural Network Society for 12 years and as the President of the Italian Society of Neural Networks (2008e14). He served in the organization of IJCNN conferences (Tutorial, International Liaison, European Link, Plenary). He has coauthored over 400 papers in various fields of engineering. He is coauthor of 15 books and has three international patents. He is an Associate Editor for Interna- tional Journal of Neural Systems, Neural Networks, Sensors, and Renewable Energy.
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