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
A consensus on the definition of profound autism, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 5 July.
A consensus on the definition of profound autism, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 5 July.
‘Completely new learning mechanism’ drives navigation in fruit flies
The neuromodulator octopamine, the insect counterpart to norepinephrine, helps flies get their bearings in an unfamiliar environment.
‘Completely new learning mechanism’ drives navigation in fruit flies
The neuromodulator octopamine, the insect counterpart to norepinephrine, helps flies get their bearings in an unfamiliar environment.
How to use artificial intelligence to strengthen scientific processes and scholarly output
As AI-driven systems are integrated into all aspects of science, we need to make sure that they read and write to a shared data and knowledge space.
How to use artificial intelligence to strengthen scientific processes and scholarly output
As AI-driven systems are integrated into all aspects of science, we need to make sure that they read and write to a shared data and knowledge space.