Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition

  • 7h 57m
  • Cesare Alippi, Francesco Carlo Morabito, Robert Kozma, Yoonsuck Choe
  • Elsevier Science and Technology Books, Inc.
  • 2023

Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives.

The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters.

  • Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN
  • Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making
  • Edited by high-level academics and researchers in intelligent systems and neural networks
  • Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks

About the Author

Dr. Robert Kozma Ph.D (Fellow of IEEE, Fellow of INNS) is Professor of Mathematical Sciences, the University of Memphis, and Professor of Computer Science at University of Massachusetts Amherst. He holds a PhD in Physics and 2 MSc degrees in Mathematics and Power Engineering. His research is focused on computational neurodynamics, large-scale brain networks, and applying biologically motivated and cognitive principles for the development of intelligent systems. Previous affiliations include visiting positions at NASA/JPL, Sarnoff Co., Princeton, NJ; Lawrence Berkeley Laboratory (LBL); AFRL, Dayton, OH; joint EECS/Neurobiology appointment at UC Berkeley; Associate Professor at Tohoku University, Sendai, Japan; Lecturer at Otago University, Dunedin, New Zealand. His research career started over 35 years ago as a research fellow at the Hungarian Academy of Sciences, Budapest, Hungary. He has published 8 books, 350+ papers, has 3 patent submissions. His research has been supported by NSF, NASA, JPL, AFRL, DARPA, FedEx, and by other agencies. He is President of INNS (2017-2018), serves on the Board of IEEE SMC Society (2016-2018); has served on the AdCom of the IEEE Computational Intelligence Society (2009-2012) and the Board of Governors of the International Neural Network Society (2007-2012). He has been General Chair of IJCNN2009, Atlanta, USA. He is Associate Editor of Neural Networks, Neurocomputing, Cognitive Systems Research, and Cognitive Neurodynamics. Dr. Kozma is the recipient of the INNS Gabor Award.

Dr. Cesare Alippi Ph.D received his degree in electronic engineering cum laude and his PhD from Politecnico di Milano, Italy. Currently, he is a Full Professor at the Politecnico di Milano, Milano, Italy and Università della Svizzera italiana, Lugano, Switzerland. He has been a visiting researcher at UCL (UK), MIT (USA), ESPCI (F), CASIA (RC), A*STAR (SIN), and UKobe (JP). Dr. Alippi is an IEEE Fellow, Board of Governors member of the International Neural Network Society, Board of Directors member of the European Neural Network Society, Past Vice-President education of the IEEE Computational Intelligence Society, past Associate editor of the IEEE Computational Intelligence Magazine, the IEEE-Transactions on Instrumentation and Measurements, the IEEE-Transactions on Neural Networks. In 2016 he received the Gabor award from the International Neural Networks Society and the IEEE Computational Intelligence Society Outstanding Transactions on Neural Networks and Learning Systems Paper Award; in 2013 the IBM Faculty award; in 2004 the IEEE Instrumentation and Measurement Society Young Engineer Award. Current research activity addresses adaptation and learning in non-stationary environments and Intelligence for embedded and cyber-physical systems. He holds 5 patents, has published one monograph book, 6 edited books and more than 200 papers in international journals and conference proceedings.

Dr. Yoonsuck Choe Ph.D. received his B.S. degree in computer science from Yonsei University, Seoul, Korea, and his M.S. and Ph.D. degrees in computer sciences from the University of Texas at Austin, Austin, Texas, USA. He is Professor and Director of the Brain Networks Laboratory in the Department of Computer Science and Engineering at Texas A&M University. During 2017 to 2019, he led the machine learning lab and the AI core team at Samsung Research as a corporate vice president. His research interest is broadly in imaging, modeling, and understanding brain function, from the local circuit level up to the whole brain scale, with a focus on the temporal and sensorimotor aspects of brain operation. He has published extensively in the above areas with over 140 publications that include three best paper awards and one best paper award nomination. He served as the program chair for the International Joint Conference on Neural Networks in 2015, and as the general chair in 2017. He is currently on the editorial board of IEEE Transactions on Cognitive and Developmental Systems.

Dr. Francesco Carlo Morabito Ph.D was born in Italy and educated in Europe. He earned the “Laurea” Degree (Summa cum laude) from the University of Napoli (Italy) and he began his career as a Research Associate at Selenia SpA, Rome, (Radar Department). He joined the University of Reggio Calabria, Italy, as research associate, then becoming Associate Professor and Full Professor of Electrical Engineering. He served as Chair of the Electronic Engineering Course and of the Industrial Engineering Course, as member of the University’s Inner Evaluation Committee, as Dean of the Faculty of Engineering, member of the Bylaws Committee, and serves now as Deputy Rector and Vice-Rector for Internationalization. He also taught at the EPFL, Lausanne, Switzerland (Ph.D. level), University of Naples, University of Messina, and University of Cosenza. He was a selected teacher at the Italian School of Doctorate in Electrical Engineering. Visiting Researcher Max-Planck Institute fuer Plasmaphysiks Munich, Germany. He is author/co-author of 300+ papers on international journals/conferences, 20 book chapters; and editor/co-editor of 10 international books, as well as Guest Editor for two special Issues of Neural Networks and IEEE Sensors. He is a Foreign Member of the Royal Academy of Doctors, Barcelona, Spain; Gold Medal “Henry Coanda”, Rumanian Academy for Researches in Neural Networks and Fuzzy Systems, Iasi, Rumania; President of the Italian Society of Neural Networks (SIREN). Positions held within INNS: Member of the Board of Governors; Secretary; Member of the Board of Governors; Chair of the Nomination Committee; Plenary Chair at IJCNN 2015; Tutorial Chair in many Editions of IJCNN. Senior Member of IEEE and INNS. Italian Representative in AMSE, SIGEF. Delegate for European University Association (EUA). He holds three patents. He founded the University Spin-Off “Neuratek” in 2005. Associate Editor of Neural Networks (Elsevier); Int. J. of Neural Systems; Editorial Board Member for: Renewable Energy (Elsevier); Applied Computational Intelligence and Soft Computing; Recent Patents in Computer Science; Intl. Journal of Computers, Signals and Systems; Fuzzy Economy Review; Micro- and Nano-Sensing Journal, Intl. Journal of Information Acquisition.

In this Book

  • Advances in AI, Neural Networks, and Brain Computing—An Introduction
  • Nature's Learning Rule—The Hebbian-LMS Algorithm
  • A Half Century of Progress toward a Unified Neural Theory of Mind and Brain with Applications to Autonomous Adaptive Agents and Mental Disorders
  • Meaning versus Information, Prediction versus Memory, and Question versus Answer
  • The Brain-Mind-Computer Trichotomy—Hermeneutic Approach
  • The New AI—Basic Concepts, and Urgent Risks and Opportunities in the Internet of Things
  • Computers versus Brains—Challenges of Sustainable Artificial and Biological Intelligence
  • Brain-Inspired Evolving and Spiking Connectionist Systems
  • Pitfalls and Opportunities in the Development and Evaluation of Artificial Intelligence Systems
  • Theory of the Brain and Mind—Visions and History
  • From Synapses to Ephapsis—Embodied Cognition and Wearable Personal Assistants
  • Explainable Deep Learning to Information Extraction in Diagnostics and Electrophysiological Multivariate Time Series
  • Computational Intelligence in Cyber-Physical Systems and the Internet of Things
  • Evolving Deep Neural Networks
  • Evolving GAN Formulations for Higher-Quality Image Synthesis
  • Multiview Learning in Biomedical Applications
  • Emergence of Tool Construction and Tool Use Through Hierarchical Reinforcement Learning
  • A Lagrangian Framework for Learning in Graph Neural Networks