Professor John Hopfield

Email:hopfield@Princeton.EDU
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Hopfield and his collaborators are working to understand how the neural circuits of the brain perform complex calculations. The human brain is astonishingly effective at processing information in many inexact problems computers find difficult, problems such as recognizing a particular person in a crowded scene or understanding natural language in a noisy environment. Understanding how the brain carries out such calculations is a major challenge to neuroscience. Through learning how a brain solves such problems, it may be possible to build faster and more powerful manmade systems.

In 1982 he discovered a network metaphor or for how the brain works, describing a computational process in terms of a dynamical attractor. More recently he has been pursuing how to use action potentials in computation. 100 billion of these are generated each second in the human brain. Hopfield is trying to understand how the times at which these occur can be used in the computational process of the brain. This research is also linked to describing how nerve cells process sensory information to turn it into perceptions, such as odors or sounds.

Hopfield came to computational science from a background in physics. After earning a Ph.D. from Cornell University in 1958, he next took a position in the Bell Laboratories theory group. He segued from physics to biology while a physics professor at Princeton, finding useful conceptual relationships between semiconductors and solids, which he had been studying as a condensed matter physicist, and the properties of protein molecules. As he became more involved with information processes in biology, he saw a need for formal mathematics and theory in neurobiology, and began his current line of research.