R
Randall C. O'Reilly
Researcher at University of California, Davis
Publications - 166
Citations - 23831
Randall C. O'Reilly is an academic researcher from University of California, Davis. The author has contributed to research in topics: Working memory & Cognition. The author has an hindex of 59, co-authored 158 publications receiving 21813 citations. Previous affiliations of Randall C. O'Reilly include Carnegie Mellon University & University of Colorado Boulder.
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Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.
TL;DR: The account presented here suggests that memories are first stored via synaptic changes in the hippocampal system, that these changes support reinstatement of recent memories in the neocortex, that neocortical synapses change a little on each reinstatement, and that remote memory is based on accumulated neocorticals changes.
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By Carrot or by Stick: Cognitive Reinforcement Learning in Parkinsonism
TL;DR: It is shown, using two cognitive procedural learning tasks, that Parkinson's patients off medication are better at learning to avoid choices that lead to negative outcomes than they are at learning from positive outcomes.
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Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach.
TL;DR: A computational neural-network model is presented of how the hippocampus and medial temporal lobe cortex contribute to recognition memory and the stochastic relationship between recall and familiarity and the effects of partial versus complete hippocampal lesions on recognition.
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Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia
TL;DR: This article presents an attempt to deconstruct this homunculus through powerful learning mechanisms that allow a computational model of the prefrontal cortex to control both itself and other brain areas in a strategic, task-appropriate manner.
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Conjunctive representations in learning and memory: principles of cortical and hippocampal function.
TL;DR: This framework suggests that tasks involving rapid, incidental conjunctive learning are better tests of hippocampal function, and is implemented in a computational neural network model that can account for a wide range of data in animal learning.