Earl K. Miller is Picower Professor of Neuroscience at the Massachusetts Institute of Technology, with faculty roles in the Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences. His lab focuses on neural mechanisms of cognition, especially working memory, attention and executive control, using both experimental and computational methods. He holds a B.A. from Kent State University and an M.A. and Ph.D. from Princeton University. In 2020, he received an honorary Doctor of Science degree from Kent State University.
Earl K. Miller
Professor of neuroscience
Massachusetts Institute of Technology
Selected articles
- “An integrative theory of prefrontal cortex function” | Annual Review of Neuroscience
- “Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices” | Science
- “The importance of mixed selectivity in complex cognitive tasks” | Nature
- “Gamma and beta bursts during working memory readout suggest roles in its volitional control” | Nature Communications
Explore more from The Transmitter
Finally, a new route for the magnetic-sense field
Researchers have dueled for years over how the magnetic sense works. New data from monarch butterflies could finally help settle the debate.
Finally, a new route for the magnetic-sense field
Researchers have dueled for years over how the magnetic sense works. New data from monarch butterflies could finally help settle the debate.
Sensory over-responsivity tied to autism, anxiety but not other conditions
Negative reactions to sensations track with certain neurodevelopmental traits in more than 15,000 children—pointing to shared neurobiological roots.
Sensory over-responsivity tied to autism, anxiety but not other conditions
Negative reactions to sensations track with certain neurodevelopmental traits in more than 15,000 children—pointing to shared neurobiological roots.
Neuromechanical models deepen our understanding of animal motor control
Thanks to recent progress in physics-based simulators and robotics, it has never been easier for neuroscientists to use neuromechanical modeling to test hypotheses about animal movement.
Neuromechanical models deepen our understanding of animal motor control
Thanks to recent progress in physics-based simulators and robotics, it has never been easier for neuroscientists to use neuromechanical modeling to test hypotheses about animal movement.