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Relatively local neurons in a distributed representation: A neurophysiological perspective
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- 19 May 2011, pp. 489-491
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What connectionists learn: Comparisons of model and neural nets
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- 19 May 2011, pp. 491-492
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Representational systems and symbolic systems
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- 19 May 2011, pp. 492-493
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Connectionism and classical computation
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- 19 May 2011, pp. 493-494
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Are connectionist models just statistical pattern classifiers?
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- 19 May 2011, pp. 494-495
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Expose hidden assumptions in network theory
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- 19 May 2011, pp. 495-496
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But what is the substance of connectionist representation?
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- 19 May 2011, pp. 496-497
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A non-empiricist perspective on learning in layered networks
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- 19 May 2011, pp. 497-498
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How connectionist models learn: The course of learning in connectionist networks
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- 19 May 2011, pp. 498-499
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What can psychologists learn from hidden-unit nets?
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- 19 May 2011, pp. 499-500
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Approaches to learning and representation
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- 19 May 2011, pp. 500-501
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On learnability, empirical foundations, and naturalness
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- 19 May 2011, p. 501
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Toward a unification of conditioning and cognition in animal learning
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- 19 May 2011, pp. 501-502
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Keeping representations at bay
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- 19 May 2011, pp. 502-503
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Learning from learned networks
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- 19 May 2011, pp. 503-504
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Realistic neural nets need to learn iconic representations
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- Published online by Cambridge University Press:
- 19 May 2011, p. 505
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The analysis of the learning needs to be deeper
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- Published online by Cambridge University Press:
- 19 May 2011, pp. 505-506
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There is more to learning then meeth the eye (or ear)
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- 19 May 2011, pp. 506-507
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Problems of extension, representation, and computational irreducibility
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- 19 May 2011, pp. 507-508
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Connectionist models: Too little too soon?
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- Published online by Cambridge University Press:
- 19 May 2011, pp. 508-509
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