# Terry Sejnowski Terry Sejnowski is a computational neuroscientist and Francis Crick Professor at the Salk Institute, known for pioneering neural network research and co-founding the Crick-Jacobs Center. Terrence Joseph Sejnowski (born 13 August 1947) is the Francis Crick Professor at the Salk Institute for Biological Studies, where he directs the Computational Neurobiology Laboratory and serves as director of the Crick-Jacobs Center for Theoretical and Computational Biology. His research has focused on [neural networks](https://www.wikiprompt.org/wiki/neural-network) and computational neuroscience, contributing foundational algorithms and models that bridge brain function and [machine learning](https://www.wikiprompt.org/wiki/machine-learning). He is also a professor of biological sciences and an adjunct professor in multiple departments at the University of California, San Diego, where he co-directs the Institute for Neural Computation. Sejnowski's career spans physics, neurobiology, and artificial intelligence, marked by influential collaborations and leadership in the field. With Barbara Oakley, he co-created and taught *Learning How To Learn*, a widely popular online course on Coursera. In 2025, he was elected to the American Philosophical Society, adding to a long list of honors. ## Early Life and Education Sejnowski was born in Cleveland, Ohio, in 1947. He earned a Bachelor of Science in physics from Case Western Reserve University in 1968. He then attended Princeton University, where he received a Master of Arts in physics in 1970 and a Doctor of Philosophy in physics in 1978, both advised by prominent physicists: John Archibald Wheeler for his master's and John Hopfield for his doctorate. During his graduate studies, Sejnowski analyzed gravitational wave sources and detector sensitivity, concluding that existing detectors were about 1,000 times too insensitive to detect signals. Predicting that adequate detectors would not emerge for roughly three decades, he shifted his focus to a different field, eventually gravitating toward neurobiology and computation. ## Career and Research Positions From 1978 to 1979, Sejnowski was a postdoctoral fellow in the Department of Biology at Princeton University, working with Alan Gelperin. He then moved to Harvard Medical School, where he was a postdoctoral fellow in neurobiology from 1979 to 1981 under Stephen Kuffler. In 1982, he joined the faculty of the Department of Biophysics at Johns Hopkins University, rising to the rank of professor before relocating to San Diego in 1988. Sejnowski maintained a long affiliation with the California Institute of Technology, serving as Wiersma Visiting Professor of Neurobiology in 1987, Sherman Fairchild Distinguished Scholar in 1993, and part-time visiting professor from 1995 to 1998. He was an investigator at the Howard Hughes Medical Institute from 1991 to 2018. In 2004, he was named the Francis Crick Professor at the Salk Institute and director of the Crick-Jacobs Center for Theoretical and Computational Biology. ## Neural Network Contributions Sejnowski's early work in the 1980s, following John Hopfield's research, helped demonstrate that simple neural networks could learn tasks of moderate sophistication. Collaborating with [Geoffrey Hinton](https://www.wikiprompt.org/wiki/geoffrey-hinton), he co-invented the Boltzmann machine, a stochastic neural network that became influential in [deep learning](https://www.wikiprompt.org/wiki/deep-learning). He also pioneered applications of learning algorithms to speech and vision, notably with NETtalk, a system that learned to pronounce written text. With his postdoctoral fellow Tony Bell, Sejnowski developed the infomax algorithm for Independent Component Analysis (ICA), a technique widely adopted in [machine learning](https://www.wikiprompt.org/wiki/machine-learning), signal processing, and data mining. In 1989, he founded *Neural Computation*, a leading journal published by MIT Press, and he has served as president of the Neural Information Processing Systems Foundation, which oversees the annual NeurIPS conference. ## Research Focus Sejnowski's long-term research goal is to understand the computational resources of brains and establish linking principles from brain to behavior using computational models. His work spans multiple levels, from biophysical properties of neurons to systems-level dynamics. Using hippocampal and cortical slice preparations, his laboratory explores single-neuron and synaptic properties, including spike firing precision and neuromodulator effects. He has developed biophysical models of electrical and chemical signaling within neurons, complementing physiological experiments. New techniques include Monte Carlo methods for cell signaling, implemented in software called MCell. His research addresses how dendrites integrate synaptic signals, how networks generate dynamical patterns, how sensory information is represented in the cerebral cortex, how memory representations form and consolidate during sleep, and how visuo-motor transformations are adaptively organized. His lab also developed methods for analyzing electrical and magnetic signals from the scalp and hemodynamic signals from functional neuroimaging. ## Honors and Awards Sejnowski has received numerous accolades throughout his career. In 1984, he received a Presidential Young Investigator Award from the National Science Foundation. He earned the Wright Prize from Harvey Mudd College in 1996 and the Hebb Prize from the International Neural Network Society in 1999. He became a Fellow of the Institute of Electrical and Electronics Engineers in 2000, receiving their Neural Network Pioneer Award in 2002. He was elected to the Johns Hopkins Society of Scholars in 2003, the National Academy of Medicine in 2008, the National Academy of Sciences in 2010, and the National Academy of Engineering in 2011. In 2017, he was elected to the National Academy of Inventors, placing him among only three living individuals elected to all four national academies. He was also elected to the American Academy of Arts and Sciences in 2013 and became a Fellow of the American Physical Society in 2014. Sejnowski received the 2015 Swartz Prize for Theoretical and Computational Neuroscience from the Society for Neuroscience and an honorary doctorate from the University of Zurich in 2017. In 2022, he was awarded the Gruber Neuroscience Prize. In 2024, he shared The Brain Prize with Larry Abbott and Haim Sompolinsky for pioneering work in theoretical neuroscience, and he received an honorary Doctor of Science from Princeton University. He was appointed a Foreign Member of the Royal Society in 2025. ## Educational Impact Beyond research, Sejnowski has contributed to education through the online course *Learning How To Learn*, co-created with Barbara Oakley. The course, available on Coursera, became one of the world's most popular online courses, helping learners master difficult subjects using evidence-based techniques. This initiative reflects his broader interest in translating scientific insights into practical tools for learning. ## Legacy and Influence Sejnowski's work has shaped both computational neuroscience and artificial intelligence, bridging biological and engineered systems. His algorithms, such as the Boltzmann machine and infomax ICA, have influenced generations of researchers in [neural networks](https://www.wikiprompt.org/wiki/neural-network) and [machine learning](https://www.wikiprompt.org/wiki/machine-learning). His leadership in founding journals and conferences has helped define the field's trajectory, and his interdisciplinary approach continues to inspire work at the intersection of neuroscience, physics, and computation. --- Source: https://www.wikiprompt.org/wiki/terry-sejnowski License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/) Last updated: 2026-09-08T15:35:01.665591+00:00