18 found
  1.  50
    A Learning Algorithm for Boltzmann Machines.David H. Ackley, Geoffrey E. Hinton & Terrence J. Sejnowski - 1985 - Cognitive Science 9 (1):147-169.
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  2. A Critique of Pure Vision.Patricia S. Churchland, V. S. Ramachandran & Terrence J. Sejnowski - 1993 - In Christof Koch & Joel L. David (eds.), Large-scale neuronal theories of the brain. MIT Press. pp. 23.
    Anydomainofscientificresearchhasitssustainingorthodoxy. Thatis, research on a problem, whether in astronomy, physics, or biology, is con- ducted against a backdrop of broadly shared assumptions. It is these as- sumptionsthatguideinquiryandprovidethecanonofwhatisreasonable-- of what "makes sense." And it is these shared assumptions that constitute a framework for the interpretation of research results. Research on the problem of how we see is likewise sustained by broadly shared assump- tions, where the current orthodoxy embraces the very general idea that the business of the visual system is to (...)
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  3. The Neural Basis of Cognitive Development: A Constructivist Manifesto.Steven R. Quartz & Terrence J. Sejnowski - 1997 - Behavioral and Brain Sciences 20 (4):537-556.
    How do minds emerge from developing brains? According to the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity. Contrary to popular selectionist models that emphasize regressive mechanisms, the neurobiological evidence suggests that this growth is a progressive increase in the representational properties of cortex. The interaction between the environment and neural growth results in a flexible type of learning: minimizes the need for prespecification in accordance with recent neurobiological evidence (...)
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  4. Motion Integration and Postdiction in Visual Awareness.David M. Eagleman & Terrence J. Sejnowski - 2000 - Science 287 (5460):2036-2038.
  5.  86
    Neural Representation and Neural Computation.Patricia Smith Churchland & Terrence J. Sejnowski - 1990 - Philosophical Perspectives 4:343-382.
  6.  39
    Thalamocortical Oscillations in the Sleeping and Aroused Brain.Mircea Steriade, D. A. McCormick & Terrence J. Sejnowski - 1993 - Science 262:679-85.
  7. Neural Representation and Neural Computation.Patricia S. Churchland & Terrence J. Sejnowski - 1989 - In L. Nadel (ed.), Philosophical Perspectives. MIT Press. pp. 343-382.
  8.  22
    Computational Neuroscience.Terrence J. Sejnowski - 1986 - Behavioral and Brain Sciences 9 (1):104-105.
  9.  29
    Beyond Modularity: Neural Evidence for Constructivist Principles in Development.Steven R. Quartz & Terrence J. Sejnowski - 1994 - Behavioral and Brain Sciences 17 (4):725-726.
  10.  9
    Simulating a Lesion in a Basis Function Model of Spatial Representations: Comparison with Hemineglect.Alexandre Pouget & Terrence J. Sejnowski - 2001 - Psychological Review 108 (3):653-673.
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  11. Awareness During Drowsiness: Dynamics and Electrophysiological Correlates.S. Makeig, T. Jung & Terrence J. Sejnowski - 2000 - Canadian Journal of Experimental Psychology 54 (4):266-273.
  12.  69
    What is Consolidated During Sleep-Dependent Motor Skill Learning?Luca A. Finelli & Terrence J. Sejnowski - 2005 - Behavioral and Brain Sciences 28 (1):70-71.
    Learning procedural skills involves improvement in speed and accuracy. Walker proposes two stages of memory consolidation: enhancement, which requires sleep, and stabilization, which does not require sleep. Speed improvement for a motor learning task but not accuracy occurs after sleep-dependent enhancement. We discuss this finding in the context of computational models and underlying sleep mechanisms.
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  13. Problems in Systems Neuroscience.L. van Hemmen & Terrence J. Sejnowski (eds.) - 2003 - Oxford University Press.
  14.  59
    There is More to Fluid Intelligence Than Working Memory Capacity and Executive Function.Dennis Garlick & Terrence J. Sejnowski - 2006 - Behavioral and Brain Sciences 29 (2):134-135.
    Although working memory capacity and executive function contribute to human intelligence, we question whether there is an equivalence between them and fluid intelligence. We contend that any satisfactory neurobiological explanation of fluid intelligence needs to include abstraction as an important computational component of brain processing. (Published Online April 5 2006).
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  15.  21
    Complexity of Calcium Signaling in Synaptic Spines.Kevin M. Franks & Terrence J. Sejnowski - 2002 - Bioessays 24 (12):1130-1144.
  16.  18
    Synaptic Currents, Neuromodulation, and Kinetic Models.Alain Destexhe, Zachary F. Mainen & Terrence J. Sejnowski - 1995 - In Michael A. Arbib (ed.), Handbook of Brain Theory and Neural Networks. MIT Press. pp. 66--617.
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  17.  10
    Is Perception Isomorphic with Neural Activity?Alexandre Pouget & Terrence J. Sejnowski - 1994 - Behavioral and Brain Sciences 17 (2):274-274.
  18.  3
    Building Network Learning Algorithms From Hebbian Synapses.Terrence J. Sejnowski & Gerald Tesauro - 1990 - In J. Mcgaugh, Jerry Weinberger & G. Lynch (eds.), Brain Organization and Memory. Guilford Press. pp. 338--355.