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  1. Kai J. Miller, Dora Hermes, Christopher J. Honey, Mohit Sharma, Rajesh P. N. Rao, Marcel Den Nijs, Eberhard E. Fetz, Terrence J. Sejnowski, Adam O. Hebb, Jeffrey G. Ojemann, Scott Makeig & Eric C. Leuthardt (2010). Dynamic Modulation of Local Population Activity by Rhythm Phase in Human Occipital Cortex During a Visual Search Task. Frontiers in Human Neuroscience 4:197.
    Brain rhythms are more than just passive phenomena in visual cortex. For the first time, we show that the physiology underlying brain rhythms actively suppresses and releases cortical areas on a second-to-second basis during visual processing. Furthermore, their influence is specific at the scale of individual gyri. We quantified the interaction between broadband spectral change and brain rhythms on a second-to-second basis in electrocorticographic (ECoG) measurement of brain surface potentials in five human subjects during a visual search task. Comparison of (...)
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  2. Paul H. E. Tiesinga & Terrence J. Sejnowski (2010). Mechanisms for Phase Shifting in Cortical Networks and Their Role in Communication Through Coherence. Frontiers in Human Neuroscience 4.
    In the primate visual cortex, the phase of spikes relative to oscillations in the local field potential (LFP) in the gamma frequency range (30-80Hz) can be shifted by stimulus features such as orientation and thus the phase may carry information about stimulus identity. According to the principle of communication through coherence (CTC), the relative LFP phase between the LFPs in the sending and receiving circuits affects the effectiveness of the transmission. CTC predicts that phase shifting can be used for stimulus (...)
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  3. Dennis Garlick & Terrence J. Sejnowski (2006). There is More to Fluid Intelligence Than Working Memory Capacity and Executive Function. 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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  4. Luca A. Finelli & Terrence J. Sejnowski (2005). What is Consolidated During Sleep-Dependent Motor Skill Learning? 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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  5. L. van Hemmen & Terrence J. Sejnowski (eds.) (2003). Problems in Systems Neuroscience. Oxford University Press.
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  6. Kevin M. Franks & Terrence J. Sejnowski (2002). Complexity of Calcium Signaling in Synaptic Spines. Bioessays 24 (12):1130-1144.
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  7. David M. Eagleman & Terrence J. Sejnowski (2000). Motion Integration and Postdiction in Visual Awareness. Science 287 (5460):2036-2038.
  8. S. Makeig, T. Jung & Terrence J. Sejnowski (2000). Awareness During Drowsiness: Dynamics and Electrophysiological Correlates. Canadian Journal of Experimental Psychology 54 (4):266-273.
  9. Steven R. Quartz & Terrence J. Sejnowski (1997). The Neural Basis of Cognitive Development: A Constructivist Manifesto. 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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  10. Alain Destexhe, Zachary F. Mainen & Terrence J. Sejnowski (1995). Synaptic Currents, Neuromodulation, and Kinetic Models. In Michael A. Arbib (ed.), Handbook of Brain Theory and Neural Networks. Mit Press. 66--617.
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  11. Alexandre Pouget & Terrence J. Sejnowski (1994). Is Perception Isomorphic with Neural Activity? Behavioral and Brain Sciences 17 (2):274.
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  12. Steven R. Quartz & Terrence J. Sejnowski (1994). Beyond Modularity: Neural Evidence for Constructivist Principles in Development. Behavioral and Brain Sciences 17 (4):725.
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  13. Patricia S. Churchland, V. S. Ramachandran & Terrence J. Sejnowski (1993). A Critique of Pure Vision. In Christof Koch & Joel L. David (eds.), Large-scale neuronal theories of the brain. MIT Press. 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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  14. Mircea Steriade, D. A. McCormick & Terrence J. Sejnowski (1993). Thalamocortical Oscillations in the Sleeping and Aroused Brain. Science 262:679-85.
  15. Patricia Smith Churchland & Terrence J. Sejnowski (1990). Neural Representation and Neural Computation. Philosophical Perspectives 4:343-382.
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  16. Terrence J. Sejnowski & Gerald Tesauro (1990). Building Network Learning Algorithms From Hebbian Synapses. In J. Mcgaugh, Jerry Weinberger & G. Lynch (eds.), Brain Organization and Memory. Guilford Press. 338--355.
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  17. Patricia S. Churchland & Terrence J. Sejnowski (1989). Neural Representation and Neural Computation. In L. Nadel (ed.), Neural Connections, Mental Computations. MIT Press. 343-382.
  18. Terrence J. Sejnowski (1986). Computational Neuroscience. Behavioral and Brain Sciences 9 (1):104.
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  19. David H. Ackley, Geoffrey E. Hinton & Terrence J. Sejnowski (1985). A Learning Algorithm for Boltzmann Machines. Cognitive Science 9 (1):147-169.
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