O'Reilly's research is aimed at developing detailed computational models of the biological basis of cognition. He is most famous for developing of the Leabra recirculating algorithm for learning in neural networks. He has developed a number of successful models of declarative memory, the visual system, and the basal ganglia circuit.
^McClelland, J.L., McNaughton, B.L. & O'Reilly, R.C. (1995). Why there are complementary learning systems in the Hippocampus and Neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 419-457.
^O'Reilly, R.C. & Rudy, J.W. (2000). Computational Principles of Learning in the Neocortex and Hippocampus. Hippocampus, 10, 389-397.
^Herd, S.A. & O'Reilly, R.C. (2005). Serial visual search from a parallel model. Vision Research, 45, 2987-2992.
^O'Reilly, R.C. & Frank, M.J. (2006). Making working memory work: A computational model of learning in the frontal cortex and basal ganglia. Neural Computation, 18, 283-328.
^Frank, M.J., Loughry, B. & O'Reilly, R.C. (2001). Interactions between the frontal cortex and basal ganglia in working memory: A computational model. Cognitive, Affective, and Behavioral Neuroscience, 1, 137-160.