Swartz Prize awarded to John Hopfield for contributions to computational neuroscience
In recognition of a lifetime of breakthroughs that have shaped our understanding of the brain, John Hopfield has been awarded the Society for Neuroscience Swartz Prize for Theoretical and Computational Neuroscience.
The $25,000 prize recognizes an individual who has produced a significant cumulative contribution to theoretical models or computational methods in neuroscience. Hopfield, Princeton University's Howard A. Prior Professor in the Life Sciences and Professor of Molecular Biology Emeritus, was presented with the award Oct. 13 during the society's 2012 conference in New Orleans.
The prize acknowledges Hopfield's decades of contributions to neuroscience and his creation of a new framework for understanding how the neurons interact to create learning and memory. His ideas have inspired generations of neuroscientists to explore how networks of connected neurons give rise to brain function.
"John Hopfield is one of the few people in theoretical neuroscience who have had enormous impact on the entire field," said William Bialek, Princeton's John Archibald Wheeler/Battelle Professor in Physics and the Lewis-Sigler Institute for Integrative Genomics. "He touched off a revolution that continues to this day."
Hopfield's best known work centered on a theory for "neural networks," describing the behavior of the large, interconnected groups of cells that we see in real brains. A key question he addressed was what happens in the brain during memory retrieval. Studying simplified models, Hopfield showed how collective network dynamics could explain previously mysterious functions such as retrieving an entire memory from a single fragment. Further, his work provided a framework for understanding how the strengthening and weakening of connections between neurons could allow the brain to store new memories and even generate new functions.
Two papers in the 1980s laid out the groundwork for how neural networks behave. The first, in 1982, outlined how physical systems can exhibit emergent collective computational abilities, such as memory retrieval, now known as the "Hopfield Model." The second paper, published in 1985 with David Tank, Princeton's Henry L. Hillman Professor in Molecular Biology, explored neural computation of decisions in optimization problems.
Hopfield’s subsequent work continues to provide new directions for research in neuroscience, Bialek said, contributing to a better understanding of brain's ability to interpret speech and perceive smells. Some of his most recent work includes provocative ideas about the neural dynamics underlying “mental exploration” as a model for what we experience as “thinking.”
A physicist, Hopfield made significant contributions to understanding the interaction of light and matter physics before turning to problems inspired by the phenomena of life. He was a member of the technical staff at Bell Laboratories in New Jersey before becoming a research physicist at Ecole Normale Superieure in Paris and later an associate professor of physics at the University of California-Berkeley. He then joined the faculty at Princeton University and worked as a professor of physics for 16 years before departing for Bell Laboratories and then the California Institute of Technology, where he became a professor of chemistry and biology. He returned to Princeton University in 1997, this time as a professor in the Department of Molecular Biology.
"Hopfield launched the modern era of theoretical physicists thinking about biological problems," said Bialek. "He has changed the kinds of questions people ask."
The prize is supported by the Swartz Foundation, which aims to better understand the relationship between the physical and cognitive brain using interdisciplinary techniques. The Society for Neuroscience is an organization of more than 42,000 basic scientists and clinicians who study the brain and nervous system.
J. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities", Proceedings of the National Academy of Sciences of the USA. Vol. 79, no. 8 pp. 2554–2558, 1982.
J. J. Hopfield and D. Tank. "Neural" computation of decisions in optimization problems. Biol. Cybern. Vol. 52, pp. 141-152, 1985.