Six principles for biologically based computational models of cortical cognition

[1]  Dana H. Ballard,et al.  Category Learning Through Multimodality Sensing , 1998, Neural Computation.

[2]  R. O’Reilly,et al.  Figure-ground organization and object recognition processes: an interactive account. , 1998, Journal of experimental psychology. Human perception and performance.

[3]  V. D. de Sa Category learning through multimodality sensing. , 1998, Neural computation.

[4]  Dario Floreano,et al.  Contextually guided unsupervised learning using local multivariate binary processors , 1998, Neural Networks.

[5]  James L. McClelland,et al.  Rethinking infant knowledge: toward an adaptive process account of successes and failures in object permanence tasks. , 1997, Psychological review.

[6]  Geoffrey E. Hinton,et al.  Generative models for discovering sparse distributed representations. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[7]  S C Rao,et al.  Integration of what and where in the primate prefrontal cortex. , 1997, Science.

[8]  T. Landauer,et al.  A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. , 1997 .

[9]  Tomoki Fukai,et al.  A Simple Neural Network Exhibiting Selective Activation of Neuronal Ensembles: From Winner-Take-All to Winners-Share-All , 1997, Neural Computation.

[10]  Soo-Young Lee,et al.  Merging Back-propagation and Hebbian Learning Rules for Robust Classifications , 1996, Neural Networks.

[11]  Randall C. O'Reilly,et al.  Biologically Plausible Error-Driven Learning Using Local Activation Differences: The Generalized Recirculation Algorithm , 1996, Neural Computation.

[12]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[13]  Lee Soo-Young,et al.  Merging Back-propagation and Hebbian Learning Rules for Robust Classifications. , 1996, Neural networks : the official journal of the International Neural Network Society.

[14]  Suzanna Becker,et al.  Mutual information maximization: models of cortical self-organization. , 1996, Network.

[15]  Geoffrey E. Hinton,et al.  The Helmholtz Machine , 1995, Neural Computation.

[16]  R. Zemel,et al.  Learning sparse multiple cause models , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[17]  Eric Saund,et al.  A Multiple Cause Mixture Model for Unsupervised Learning , 1995, Neural Computation.

[18]  M. Peterson Object Recognition Processes Can and Do Operate Before Figure–Ground Organization , 1994 .

[19]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

[20]  R. Zemel A minimum description length framework for unsupervised learning , 1994 .

[21]  J. B. Levitt,et al.  Topography of pyramidal neuron intrinsic connections in macaque monkey prefrontal cortex (areas 9 and 46) , 1993, The Journal of comparative neurology.

[22]  V. Marchman,et al.  From rote learning to system building: acquiring verb morphology in children and connectionist nets , 1993, Cognition.

[23]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[24]  S Pinker,et al.  Overregularization in language acquisition. , 1992, Monographs of the Society for Research in Child Development.

[25]  J. Shonkoff,et al.  Development of infants with disabilities and their families: implications for theory and service delivery. , 1992, Monographs of the Society for Research in Child Development.

[26]  Michael C. Mozer,et al.  Induction of Multiscale Temporal Structure , 1991, NIPS.

[27]  William A. Phillips,et al.  A Biologically Supported Error-Correcting Learning Rule , 1991, Neural Computation.

[28]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[29]  P. Goldman-Rakic,et al.  Preface: Cerebral Cortex Has Come of Age , 1991 .

[30]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[31]  Javier R. Movellan,et al.  Contrastive Hebbian Learning in the Continuous Hopfield Model , 1991 .

[32]  David E. Rumelhart,et al.  Generalization by Weight-Elimination with Application to Forecasting , 1990, NIPS.

[33]  Geoffrey E. Hinton,et al.  Distributed Representations , 1986, The Philosophy of Artificial Intelligence.

[34]  Geoffrey E. Hinton Connectionist Learning Procedures , 1989, Artif. Intell..

[35]  D. Massaro Testing between the TRACE model and the fuzzy logical model of speech perception , 1989, Cognitive Psychology.

[36]  Geoffrey E. Hinton Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space , 1989, Neural Computation.

[37]  Francis Crick,et al.  The recent excitement about neural networks , 1989, Nature.

[38]  E. White Cortical Circuits: Synaptic Organization of the Cerebral Cortex , 1989 .

[39]  Steven J. Nowlan,et al.  Maximum Likelihood Competitive Learning , 1989, NIPS.

[40]  Kurt Hornik,et al.  Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.

[41]  Richard A. Andersen,et al.  A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons , 1988, Nature.

[42]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[43]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[44]  James L. McClelland,et al.  An interactive activation model of context effects in letter perception: part 1.: an account of basic findings , 1988 .

[45]  T. Bliss,et al.  NMDA receptors - their role in long-term potentiation , 1987, Trends in Neurosciences.

[46]  Carsten Peterson,et al.  A Mean Field Theory Learning Algorithm for Neural Networks , 1987, Complex Syst..

[47]  Geoffrey E. Hinton,et al.  Learning Representations by Recirculation , 1987, NIPS.

[48]  Geoffrey E. Hinton,et al.  The appeal of parallel distributed processing , 1986 .

[49]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[50]  Paul Smolensky,et al.  Information processing in dynamical systems: foundations of harmony theory , 1986 .

[51]  David Zipser,et al.  Feature Discovery by Competive Learning , 1986, Cogn. Sci..

[52]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[53]  E. Bienenstock,et al.  Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[54]  E. Oja Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

[55]  James L. McClelland,et al.  An interactive activation model of context effects in letter perception: I. An account of basic findings. , 1981 .

[56]  John J. L. Morton,et al.  Interaction of information in word recognition. , 1969 .

[57]  J. Haldane The interaction of nature and nurture. , 1946, Annals of eugenics.