14 found
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  1.  20
    Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory.James L. McClelland, Bruce L. McNaughton & Randall C. O'Reilly - 1995 - Psychological Review 102 (3):419-457.
  2.  24
    Dissociated overt and covert recognition as an emergent property of a lesioned neural network.Martha J. Farah, Randall C. O'Reilly & Shaun P. Vecera - 1993 - Psychological Review 100 (4):571-588.
  3.  16
    Conjunctive representations in learning and memory: Principles of cortical and hippocampal function.Randall C. O'Reilly & Jerry W. Rudy - 2001 - Psychological Review 108 (2):311-345.
  4.  21
    Modeling hippocampal and neocortical contributions to recognition memory: A complementary-learning-systems approach.Kenneth A. Norman & Randall C. O'Reilly - 2003 - Psychological Review 110 (4):611-646.
  5.  45
    Six principles for biologically based computational models of cortical cognition.Randall C. O'Reilly - 1998 - Trends in Cognitive Sciences 2 (11):455-462.
  6. Hippocampal and neocortical contributions to memory: Advances in the complementary learning systems framework.Randall C. O'Reilly & Kenneth A. Norman - 2002 - Trends in Cognitive Sciences 6 (12):505-510.
  7.  23
    Early recurrent feedback facilitates visual object recognition under challenging conditions.Dean Wyatte, David J. Jilk & Randall C. O'Reilly - 2014 - Frontiers in Psychology 5.
  8.  39
    Persistence and accommodation in short‐term priming and other perceptual paradigms: temporal segregation through synaptic depression.David E. Huber & Randall C. O'Reilly - 2003 - Cognitive Science 27 (3):403-430.
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  9.  34
    Persistence and accommodation in short-term priming and other perceptual paradigms: Temporal segregation through synaptic depression.David E. Huber & Randall C. O'Reilly - 2003 - Cognitive Science 27 (3):403-430.
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  10.  5
    A systems-neuroscience model of phasic dopamine.Jessica A. Mollick, Thomas E. Hazy, Kai A. Krueger, Ananta Nair, Prescott Mackie, Seth A. Herd & Randall C. O'Reilly - 2020 - Psychological Review 127 (6):972-1021.
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  11.  71
    The Leabra architecture: Specialization without modularity.Alexander A. Petrov, David J. Jilk, Randall C. O'Reilly & Michael L. Anderson - 2010 - Behavioral and Brain Sciences 33 (4):286.
    The posterior cortex, hippocampus, and prefrontal cortex in the Leabra architecture are specialized in terms of various neural parameters, and thus are predilections for learning and processing, but domain-general in terms of cognitive functions such as face recognition. Also, these areas are not encapsulated and violate Fodorian criteria for modularity. Anderson's terminology obscures these important points, but we applaud his overall message.
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  12.  12
    Learning representations in a gated prefrontal cortex model of dynamic task switching.Nicolas P. Rougier & Randall C. O'Reilly - 2002 - Cognitive Science 26 (4):503-520.
    The prefrontal cortex is widely believed to play an important role in facilitating people's ability to switch performance between different tasks. We present a biologically‐based computational model of prefrontal cortex (PFC) that explains its role in task switching in terms of the greater flexibility conferred by activation‐based working memory representations in PFC, as compared with more slowly adapting weight‐based memory mechanisms. Specifically we show that PFC representations can be rapidly updated when a task switches via a dynamic gating mechanism based (...)
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  13.  32
    Computational Neuroscience: From Biology to Cognition.Randall C. O'Reilly & Yuko Munakata - 2003 - In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group.
  14.  34
    Learning representations in a gated prefrontal cortex model of dynamic task switching.Nicolas P. Rougier & Randall C. O'Reilly - 2002 - Cognitive Science 26 (4):503-520.
    The prefrontal cortex is widely believed to play an important role in facilitating people's ability to switch performance between different tasks. We present a biologically‐based computational model of prefrontal cortex (PFC) that explains its role in task switching in terms of the greater flexibility conferred by activation‐based working memory representations in PFC, as compared with more slowly adapting weight‐based memory mechanisms. Specifically we show that PFC representations can be rapidly updated when a task switches via a dynamic gating mechanism based (...)
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