John Hopfield

If you have additional information or corrections regarding this mathematician, please use the update form.To submit students of this mathematician, please use the new data form, noting this mathematician's MGP ID of 15862 for the advisor ID. He enjoyed blanket permission to dismantle whatever he wished in the house—as long as everything was returned to its original condition. The output of each neuron should be the input of other neurons but not the input of self. A true adventurer, Hopfield is not focused on disciplinary constraints; he cares more about asking questions, tackling potential solutions, and moving on once the problem yields.Born to two physicists, as a child, Hopfield built model airplanes and crystal set radios. Becoming a scientist or engineer was never a question: Hopfield’s primary motivation had always been to understand how things work. article. In the 1980s, Hopfield boldly focused his attentions to the most complex and elusive system in the Universe: the human brain—whose mysteries he continues to pursue today, nearly 40 years later.In 1982, Hopfield developed a model of neural networks to explain how memories are recalled by the brain. wij = wji The ou… in Physics, Cornell University, 1958. It is essential in all steps of gene expression and in the immune system’s ability to identify foreign substances.But Hopfield was not one to linger on any particular question for too long. His honors include the Society for Neuroscience Prize for Theoretical and Computational Neuroscience (2012), the Harold Pender Award from the University of Pennsylvania School of Engineering and Applied Science (2002), the Dirac Medal and Prize from the International Center for Theoretical Physics (2001), and the International Neural Network Society’s Helmholtz Award (1999).

Over nearly six decades, he has used his knowledge and experience to cross borders between scientific disciplines, for example, to illuminate physical concepts underlying biological and biochemical processes. He attributes his tireless work ethic to the experts he encountered at Bell Labs and the quality of their research, and to the high standards he had to meet there.While at Bell Labs, Hopfield learned that his talent was often best expressed in finding simple answers to complex problems. John J. Hopfield.

They are guaranteed to converge to a local minimumand, therefore, may converge to a false pattern (wrong local minimum) rather than the stored pattern (expected local minimum) . Understanding emergent dynamics: Using a collective activity coordinate of a neural network to recognize time-varying patterns. His scientific contributions began to intersect with many fields, and would inspire not only fellow physicists, but, also biologists, engineers, computer scientists, and psychologists.A major contribution to the field of genetics came in 1974, when Hopfield demonstrated that the high degree of accuracy present in genetic expression could be explained by coupled chemical reactions called “kinetic proofreading.” Hopfield described kinetic proofreading as a mechanism for error correction in biological reactions, like such as protein synthesis. But the more he learned about a topic, the deeper his questions probed and the more difficult it became to limit his inquiries to the field of solid state physics. Over 1000 biographies of physicists and histories of institutions with information pertaining to their lives, careers, and research.Ph.D. The Hopfield model explains how systems of neurons interact to produce stable memories and, further, how neuronal systems apply simple processes to complete whole memories based on partial information.

It is why, he says, he is a poor piano player, sailor, skier, tennis player, and golfer—but it also provokes him to ask more complex questions of the world around him.