Creator of Worlds

Throughout the course of his extraordinary life and career, John Hopfield ’54, H’92 has been driven by one simple question: How does this work?

It’s what motivated him to disassemble bikes as a child; what inspired him to pursue advanced studies in physics; what moved him into the fledgling field of theoretical neuroscience.

And, ultimately, what led him to receive one of science’s most prestigious honors: the Franklin Institute’s Benjamin Franklin Medal in Physics.

“I grew up in a household that taught me that the world is understandable,” says Hopfield, the son of two physicists and the Howard A. Prior Professor Emeritus of Molecular Biology at Princeton. “You can take it apart, put it back together, and understand how it functions—even build something new.”

By following this approach, Hopfield bridged a scientific divide, crossing from physics to biology, engineering, psychology, and beyond. In its award citation, the Franklin Institute recognized Hopfield “for applying concepts of theoretical physics to provide new insights on important biological questions in a variety of areas, including neuroscience and genetics, with significant impact on machine learning, an area of computer science.”

“No Franklin Medal in Physics before this has had even a little toe in biology,” notes Hopfield, making the award especially meaningful to the 2019 honoree. “In recognizing my work, the Franklin Institute is including the physics of biology as a part of the broad enterprise called physics.”

Hopfield arrived at Swarthmore planning to study physics or chemistry, but his adviser—familiar with his upbringing—immediately crossed the latter off the list.

“I think that’s what would have happened anyway,” Hopfield says, “but in hindsight, it was a powerful piece of guidance.”

Beyond the science labs, Hopfield found inspiration at the weekly, mandatory Collection, where he heard such speakers as the ACLU’s Roger Nash Baldwin and Socialist presidential candidate Norman Thomas. The assemblies broadened his outlook on moral, political, and economic issues, and solidified his belief that our complex world could be explained.

Hopfield went on to receive a Ph.D. from Cornell before joining the technical staff of the prestigious Bell Laboratories, focusing on solid-state physics. But 10 years into his career, the problems that initially piqued his interest were being solved.

“You would look at something and ask, ‘I wonder what caused that effect?’” says Hopfield. “But if the world was less a place of wonder because you understood many more of those things, then where were you going to get your questions from?”

He found them in biology, attracted by a chemical physicist who had begun taking measurements of biological molecules. Hopfield showed that the chemical reaction pathways in a cell are arranged in a pattern that produces accuracy enhancement in critical processes. This molecular- level process is closely related to our macroscopic ability to type a page accurately by proofreading and correcting errors.

In the 1980s, drawn to the mysteries of the mind, Hopfield developed an artificial neural network model of the change with time of nerve cell activity patterns. The construct— which can mimic several brain functions, like the ability to recall simple memories from a fragmentary clue—is central to many “deep learning” technologies of today, such as verbal communication between humans and machines and self- driving cars.

Hopfield acknowledges that his gift is in framing simple questions that contain the essence of a complex situation. Since retiring a decade ago from Princeton University’s Department of Molecular Biology, Hopfield has served as a mentor for postdoctoral scientists interested in the intersection of physics and biology, chiefly at the Institute of Advanced Study. But rather than helping them solve problems, Hopfield helps them find new puzzles to pursue.

“My scientific life,” he says, “has always been about finding a problem to work on.”