Cybernetics 2.0

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.

• Introduction.- Hebbian Learning.- The LMS Algorithm.- The Hebbian-LMS Algorithm.-  Inside the membrane; The percent Hebbian-LMS algorithm.- Synaptic scaling and homeostasis.-  Synaptic plasticity.- Addiction.