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as control shifts between competing brain regions, again, based on high-level "cognitive demand." Deacon's intuition is similar to the one that led Edelman to propose Neural Darwinism tEcle931, and Calvin and Bickerton IC13001 to pose the notion of mind as a "Darwin Machine". The latter have given plau
front of the robot, via pinching it between two fingers and then lifting it. In this case, • The visual scene, including the block, is perceived by DeSTIN; and appropriate patterns in various DeSTIN nodes are formed • Predicates corresponding to the distribution of patterns among DeSTIN nodes are acti
r-level behaviors like reasoning or subgoaling. • NOMAD (Neurally Organized Mobile Adaptive Device) automata and its successors [K EMI are based on Edelman's "Neural Darwinism" model of the brain, and feature large numbers of simulated neurons evolving by natural selection into configurations that carry
spatiotemporally hier- archical deep learning system is an effective way to handle representation and learning of low-level sensorimotor knowledge. DeSTIN is one example of a deep learning system of this nature that can be effective in this context. i. One effective way to handle goals is to represent
er-level behaviors like reasoning or subgoaling. e NOMAD (Neurally Organized Mobile Adaptive Device) automata and its successors [KE06] are based on Edelman’s “Neural Darwinism” model of the brain, and feature large numbers of simulated neurons evolving by natural selection into configurations that carry
a spatiotemporally hier- archical deep learning system is an effective way to handle representation and learning of low-level sensorimotor knowledge. DeSTIN is one example of a deep learning system of this nature that can be effective in this context. i. One effective way to handle goals is to represent
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