CCNLab/publications

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Curriculum Vitae (CV's)

Copyright Notice

The documents distributed here have been provided as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Recommended Introductory Papers

These papers are good choices for learning about research in each of the major categories of research conducted in the CCNLab.

Leabra Integrated Biologically-Based Cognitive Architecture

  • OReillyHazyHerd16 O'Reilly, R.C., Hazy, T.E. & Herd, S.A. (2016). The Leabra Cognitive Architecture: How to Play 20 Principles with Nature and Win!. S. Chipman (Ed) Oxford Handbook of Cognitive Science, Oxford: Oxford University Press. pdf icon.png OReillyHazyHerd16.pdf (Web)
  • Also see our textbook on this site: CCNBook/Main

Working Memory and Cognitive Control in the Prefrontal Cortex and Basal Ganglia

  • OReilly06 OReilly, R.C. (2006). Biologically Based Computational Models of High-Level Cognition. Science, 314, 91-94. pdf icon.png OReilly06.pdf

Visual Perception and Object Recognition

Learning and Memory in the Hippocampus and Neocortex

Papers Organized by Theme

Leabra Integrated Biologically-Based Cognitive Architecture

  • OReillyWyatteRohrlich17 O'Reilly, R.C., Wyatte, D., and Rohrlich, J. (2017/submitted). Deep Predictive Learning: A Comprehensive Model of Three Visual Streams. Preprint avail at: https://arxiv.org/abs/1709.04654 pdf icon.png OReillyWyatteRohrlich17.pdf (Web)
  • JilkHerdReadEtAl17 Jilk, D. J., Herd, S. J., Read, S. J., & O’Reilly, R. C. (2017). Anthropomorphic reasoning about neuromorphic AGI safety. Journal of Experimental & Theoretical Artificial Intelligence, 0(0), 1–15. pdf icon.png JilkHerdReadEtAl17.pdf (Web)
  • OReillyHazyHerd16 O'Reilly, R.C., Hazy, T.E. & Herd, S.A. (2016). The Leabra Cognitive Architecture: How to Play 20 Principles with Nature and Win!. S. Chipman (Ed) Oxford Handbook of Cognitive Science, Oxford: Oxford University Press. pdf icon.png OReillyHazyHerd16.pdf (Web)
  • VerduzcoOReilly15 Verduzco-Flores, S. O. and O'Reilly, R. C. (2015). How the credit assignment problems in motor control could be solved after the cerebellum predicts increases in error. Frontiers in Computational Neuroscience, 9, 39. pdf icon.png VerduzcoOReilly15.pdf
  • OReillyWyatteRohrlich14 O'Reilly, R.C., Wyatte, D., and Rohrlich, J. (2014). Learning Through Time in the Thalamocortical Loops. Preprint avail at: http://arxiv.org/abs/1407.3432 pdf icon.png OReillyWyatteRohrlich14.pdf (Web)
  • SunOReillyBhattacharyyaEtAl15 Sun, Y., O'Reilly, R.C., Bhattacharyya, R., Smith, J.W., Liu, X., and Wang, H. (2015). Latent structure in random sequences drives neural learning toward a rational bias. Proceedings of the National Academy of Sciences (USA). pdf icon.png SunOReillyBhattacharyyaEtAl15.pdf (Web)
  • OReillyPetrovCohenEtAl14 O'Reilly, R. C., Petrov, A. A., Cohen, J. D., Lebiere, C. J., Herd, S. A., & Kriete, T. (2014). How Limited Systematicity Emerges: A Computational Cognitive Neuroscience Approach. Calvo, P. and Symons, J., Eds. The architecture of cognition: Rethinking Fodor and Pylyshyn¹s Systematicity Challenge. MIT Press. pdf icon.png OReillyPetrovCohenEtAl14.pdf (Web)
  • ZieglerChelianBenvenutoEtAl14 Ziegler, M. D., Chelian, S. E., Benvenuto, J., Krichmar, J. L., O'Reilly, R. C., Bhattacharyya, R. (2014). A model of proactive and reactive cognitive control with anterior cingulate cortex and the neuromodulatory system. Biologically Inspired Cognitive Architectures, 10, 61-67. pdf icon.png ZieglerChelianBenvenutoEtAl14.pdf (Web)
  • JilkLebiereOReillyAnderson08 Jilk, D.J., Lebiere, C., O'Reilly, R.C. & Anderson, J.R. (2008). SAL: An explicitly pluralistic cognitive architecture. Journal of Experimental and Theoretical Artificial Intelligence, 20, 197-218. pdf icon.png JilkLebiereOReillyAnderson08.pdf
  • AisaMingusOReilly08 Aisa, B., Mingus, B. & O'Reilly, R. (2008). The emergent neural modeling system. Neural networks, 21, 1146--1152. pdf icon.png AisaMingusOReilly08.pdf (Web)
  • HuberTianCurranEtAl08 Huber, D.E., Tian, X., Curran, T., O'Reilly, R.C. & Woroch, B. (2008). The dynamics of integration and separation: ERP MEG and neural network studies of immediate repetition effects. Journal of experimental psychology, 34, 1389--1416. pdf icon.png HuberTianCurranEtAl08.pdf (Web)
  • CerOReilly06 Cer, D.M. & O'Reilly, R.C. (2006). Neural mechanisms of binding in the hippocampus and neocortex: Insights from computational models. H.D. Zimmer, A. Mecklinger & U. Lindenberger (Eds) Binding in Memory, Oxford: Oxford University Press. pdf icon.png CerOReilly06.pdf
  • OReilly06ap O'Reilly, R.C. (2006). Modeling Integration and Dissociation in Brain and Cognitive Development. Y. Munakata & M.H. Johnson (Eds) Processes of Change in Brain and Cognitive Development: Attention and Performance XXI., Oxford: Oxford University Press. pdf icon.png OReilly06ap.pdf
  • AtallahFrankOReilly04 Atallah, H.E., Frank, M.J. & O'Reilly, R.C. (2004). Hippocampus, cortex and basal ganglia: Insights from computational models of complementary learning systems. Neurobiology of Learning and Memory, 82/3, 253-67. pdf icon.png AtallahFrankOReilly04.pdf
  • MunakataOReilly03 Munakata, Y. & O'Reilly, R.C. (2003). Developmental and Computational Neuroscience Approaches to Cognition: The Case of Generalization. Cognitive Studies, 10, 76-92. pdf icon.png MunakataOReilly03.pdf
  • JilkCerOReilly03 Jilk, D.J., Cer, D.M. & O'Reilly, R.C. (2003). Learning Rules Generated by a Biophysical Model of Synaptic Plasticity. Computational Neuroscience Conference, 2003, pdf icon.png JilkCerOReilly03.pdf
  • HuberOReilly03 Huber, D.E. & O'Reilly, R.C. (2003). Persistence and accommodation in short-term priming and other perceptual paradigms: Temporal segregation through synaptic depression. Cognitive Science, 27, 403-430. pdf icon.png HuberOReilly03.pdf
  • OReillyBusbySoto03 O'Reilly, R.C., Busby, R.S. & Soto, R. (2003). Three Forms of Binding and their Neural Substrates: Alternatives to Temporal Synchrony. A. Cleeremans (Ed) The Unity of Consciousness: Binding, Integration, and Dissociation, 168-192, Oxford: Oxford University Press. pdf icon.png OReillyBusbySoto03.pdf
  • OReillyMunakata03 O'Reilly, R.C. & Munakata, Y. (2003). Computational Neuroscience and Cognitive Modeling. L. Nadel (Ed) Encyclopedia of Cognitive Sciences, London: Macmillan. pdf icon.png OReillyMunakata03.pdf
  • OReillyBusby02 O'Reilly, R.C. & Busby, R.S. (2002). Generalizable Relational Binding from Coarse-coded Distributed Representations. Advances in Neural Information Processing Systems (NIPS) 14,T.G. Dietterich, S. Becker & Z. Ghahramani (Eds), Cambridge, MA; MIT Press pdf icon.png OReillyBusby02.pdf
  • OReillyMunakata02 O'Reilly, R.C. & Munakata, Y. (2002). Psychological Function in Computational Models of Neural Networks. M. Gallagher & R. Nelson (Eds) Handbook of Psychology, Vol 3, Biological Psychology, New York: Wiley. pdf icon.png OReillyMunakata02.pdf
  • OReilly01 O'Reilly, R.C. (2001). Generalization in Interactive Networks: The Benefits of Inhibitory Competition and Hebbian Learning. Neural Computation, 13, 1199-1242. pdf icon.png OReilly01.pdf
  • OReilly98 O'Reilly, R.C. (1998). Six Principles for Biologically-Based Computational Models of Cortical Cognition. Trends in Cognitive Sciences, 2, 455-462. pdf icon.png OReilly98.pdf
  • OReilly96 O'Reilly, R.C. (1996). Biologically Plausible Error-driven Learning using Local Activation Differences: The Generalized Recirculation Algorithm. Neural Computation, 8, 895-938. pdf icon.png OReilly96.pdf
  • OReilly96phd O'Reilly, R.C. (1996). The Leabra Model of Neural Interactions and Learning in the Neocortex. Phd Thesis, Carnegie Mellon University, Pittsburgh, PA pdf icon.png OReilly96phd.pdf (Web)
  • OReilly94 O'Reilly, R.C. (1994). Temporally Local Unsupervised Learning: The MaxIn Algorithm for Maximizing Input Information. Proceedings of the 1993 Connectionist Models Summer School,M.C. Mozer, P. Smolensky & A.S. Weigend (Eds), Hillsdale, NJ; Lawrence Erlbaum Associates pdf icon.png OReilly94.pdf

Working Memory and Cognitive Control in the Prefrontal Cortex and Basal Ganglia

Visual Perception and Object Recognition

Learning and Memory in the Hippocampus and Neocortex