Dr. Terrence Sejnowski stands as a transformative figure in integrative brain science and artificial intelligence. He currently holds the Francis Crick Professorship at The Salk Institute for Biological Studies and serves as Distinguished Professor of Neurobiology at the University of California, San Diego. Recognized among the rare scholars elected to all four National Academies—Sciences, Engineering, Medicine, and Inventors—he previously served as Howard Hughes Medical Institute Investigator from 1991 to 2017. His academic journey began with a physics education at Princeton University, where he earned his PhD under John Hopfield, following undergraduate studies at Case Western Reserve University where he was born.
Dr. Sejnowski pioneered foundational methodologies that revolutionized both neuroscience and artificial intelligence, most notably co-inventing the Boltzmann machine with Geoffrey Hinton in 1985, which established the first learning algorithm for multilayer neural networks and laid essential groundwork for modern deep learning. His laboratory developed Independent Component Analysis for blind source separation, now universally applied in analyzing EEG and fMRI brain imaging data across medical and engineering fields. He introduced the influential theory that dopamine neurons compute reward prediction error, a concept that became fundamental to neuroeconomics and reinforcement learning. These theoretical frameworks have generated enduring impact, with applications extending from improving mobile phone voice quality to advancing understanding of memory consolidation during sleep.
Beyond his technical contributions, Dr. Sejnowski has shaped scientific discourse as President of the Neural Information Processing Systems Foundation, which organizes the world's largest AI conference, and as founding editor of the seminal journal Neural Computation. He has strategically bridged disciplines through initiatives like the massively popular online course Learning How To Learn, co-created with Barbara Oakley, which has educated millions worldwide. His current research drives the emerging field of NeuroAI, exploring bidirectional connections between brain mechanisms and artificial intelligence systems. As a mentor and thought leader, he continues advancing computational principles that link neural mechanisms to behavior while training the next generation of interdisciplinary scientists to tackle neuroscience's most complex questions.
