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References 345 Ede93. Gerald Edelman. Neural darwinism: Selection and reentrant signaling in higher brain function. Neuron, 10, 1993. Elm91. J. Elman. Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7:195-226, 1991. EMC12. Effective-Mind-Contr
2008. Dfir02. Dietrich Darner. Die Alechanik des Seelenwegens. Eine neuronale Theorie der Handfungsregula- tion. Verlag Hans Huber, 2002. EBJ+97. J. Elman, E. Bates, M. Johnson, A. Karmiloff-Smith, D. Parisi, and K. Plunkett. Rethinking Innateness: A Connection-jai Perspective on Development. MIT Press
, by having another agent explicitly guide it in doing the thing. For instance, teaching a dog to sit by pushing its butt to the ground. • CSDLN: (Compositional Spatiotemporal Deep Learning Network): A hierarchical pattern recognition network, in which each layer corresponds to a certain spatiotemporal granularity, the nodes on a given layer c
7. Ferdinand de Saussure. Course in General Linguistics. Flmtana/Collins, 1977. Orig. published 1916 as "emirs de linguistique generale". EBJ+97. J. Elman, E. Bates, M. Johnson, A. Karmiloff-Smith, D. Parisi. and K. Plunkett. Rethinking Innateness: A Connectionist Perspective on Development. MIT Press,
, by having another agent explicitly guide it in doing the thing. For instance, teaching a dog to sit by pushing its butt to the ground. • CSDLN: (Compositional Spatiotemporal Deep Learning Network): A hierarchical pattern recognition network, in which each layer corresponds to a certain spatiotemporal granularity, the nodes on a given layer c
cond Conf. on AGI, 2008. Dietrich Dérner. Die Mechantk des Seelenwagens. Eine neuronale Theorie der Handlungsregula- tion. Verlag Hans Huber, 2002. J. Elman, E. Bates, M. Johnson, A. Karmiloff-Smith, D. Parisi, and K. Plunkett. Rethinking Innateness: A Connectionist Perspective on Development. MIT Press,
hing, by having another agent explicitly guide it in doing the thing. For instance, teaching a dog to sit by pushing its butt to the ground. CSDLN: (Compositional Spatiotemporal Deep Learning Network): A hierarchical pattern recognition network, in which each layer corresponds to a certain spatiotemporal granularity, the nodes on a given layer cor
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