Dr. O’Reilly has made significant scientific advances in understanding the function of multiple different brain systems, through his pioneering use of computer models of the learning mechanisms operating in these systems. He is internationally recognized as a founder of the field of Computational Cognitive Neuroscience, and wrote the definitive textbook in this area. His early work on learning and memory in the hippocampus and neocortex established an important foundation for modern theories. He then developed core models for understanding how the prefrontal cortex and basal ganglia function to support executive functions such as decision-making and problem-solving, under the influence of dopamine-based learning signals. Currently he is developing an innovative and comprehensive framework for understanding how the brain learns by making predictions. Throughout, his work has informed our understanding of brain-based disorders such as schizophrenia and Parkinson’s disease.
Dr. O’Reilly received his Ph.D. from Carnegie Mellon University after graduating from Harvard University with highest honors in Psychology. After a postdoctoral position at the Massachusetts Institute of Technology, he was promoted to Professor of Psychology and Neuroscience at the University of Colorado at Boulder, and has just moved to UC Davis in the Summer of 2019. His research has been continually funded from a variety of sponsors including the ONR, NIH, NSF, including leading large-scale ambitious projects funded by IARPA and DARPA. He co-founded eCortex, Inc. in 2006 and serves as Chief Scientific Officer for this small but thriving R&D company. Dr. O’Reilly has received numerous awards, including membership in the prestigious Society of Experimental Psychologists.
Randall O’Reilly is internationally recognized as a founder of the field of Computational Cognitive Neuroscience, publishing a widely-cited textbook (O’Reilly & Munakata, 2000; 2014) and a number of influential papers in this field. He develops large-scale systems-neuroscience computational models of learning, memory, and motivated cognitive control, to learn how neurons give rise to human cognitive function and to inform our understanding of brain-based disorders such as schizophrenia and Parkinson’s disease.