Cognitive Memory: Human Memory | Machine Memory

How does human memory work?  How does human pattern recognition work?  The book’s motivation is twofold, to add to knowledge in the field of neuroscience, and to design a highly simplified cognitive memory constructed using software and existing electronic components.  Readers are taken on an inspiring journey through the fundamentals of human memory, how it is constructed, and how it works in everyday life.  The book goes more in-depth into the human side of cognitive memory — how seeing, hearing, walking and speaking works.  Impairments in cognitive memory are also discussed.  Lastly, the book sheds light on how meaning is extracted from sensory inputs and from stored data. This book is not without controversy.  Neuroscientists accept the engrams (or memory traces) model that long-term memory is stored in the brain’s neural networks. The authors believe that long-term human memory is stored digitally, in the DNA of brain cells, and not in analog neural networks.  Further, the authors believe that innate knowledge of humans and animals is inherited, transmitted from parents to offspring at the moment of conception.  The single cell contains the innate knowledge in the DNA of its nucleus.   Memory is stored in DNA.  The brain’s neural networks are for access and retrieval of memory and not for actual storage.  This book offers a unique, inspiring reading to researchers and other readers interested in the science of memory.

Bernard Widrow is Professor Emeritus in the Electrical Engineering Department at Stanford University. His research focuses on adaptive signal processing, adaptive control systems, adaptive neural networks, human memory, cybernetics, and human-like memory for computers. Applications include signal processing, prediction, noise cancelling, adaptive arrays, control systems, and pattern recognition.  He received the Doctor of Science Degree from MIT in 1956, and was appointed Professor from the same University. He has been active in the field of artificial neural networks since 1957, when there were only a half-dozen researchers working on this all over the world. In 1959, he moved to Stanford University. In the same year, together with his student Ted Hoff, he invented the Least Mean Square (LMS) algorithm, which has been the world’s most widely used learning algorithm to date. Since 2010, he has expanded his interest to living neural networks and biological adaptivity. A Life fellow of the Institute of Electrical and Electronic Engineering (IEEE), he was awarded with the IEEE Alexander Graham Bell Medal in 1986 and with the Benjamin Franklin Medal for Electrical Engineering in 2001. He has been inducted into both the US National Academy of Engineering and the Silicon Valley Engineering Hall of Fame, in 1995 and 1999, respectively. He is the author of  Cybernetics 2.0 - A General Theory of Adaptivity and Homeostasis in the Brain and in the Body, published by Springer in 2023.

Edward P. Katz is Senior Research Advisor, Stanford Intelligent System Laboratory (SISL), Stanford University contributing to research teams, post-doctoral scholars, and graduate students in the areas of advanced software development engineering, project management, and team coordination.  His research interests include task-level, robotics software middleware, autonomous software agents, and computational intelligence applications in robotics and automation.   He earned the Bachelor of Science degree in Mathematics from Purdue University, the  Master’s of Science in Applied Mathematics (Computer Science) from University of Missouri-Columbia and the Doctor of Philosophy in Computer Science from University of Louisiana-Lafayette.  Prior to his current position, he has been Associate Professor of Computer Science at Northeastern University, Associate Professor of Software Engineering  at Carnegie Mellon University,  Visiting Associate Professor of Computer Science at Loyola Marymount University. and Senior Software Researcher, HP Laboratories, Hewlett-Packard Company.  Invited to be a Visiting Scholar at the Stanford University Computer Science Department’s Robotics Laboratory, Dr. Katz collaborated with legendary AI Pioneer Professor Nils J. Nilsson.  This collaboration produced an extension of Professor Nilsson's Teleo-Reactive Paradigm pioneering work. resulting in the  Fuzzy Teleo-Reactive extended paradigm for autonomous robot agent control which completely generalized the original Teleo-Reactive Paradigm.  Dr. Katz is a Senior Member, Association for the Advancement of Artificial Intelligence (AAAI), a Senior Member, Association for Computing Machinery (ACM), and a Senior Member, Institute of Electrical and Electronics Engineers (IEEE).  He has been awarded two U.S. Patents.