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dc:title "HTML Summary of #2380 \n\nPre-integration lateral inhibition enhances unsupervised learning\n\n";
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bibo:abstract "A large and influential class of neural network architectures use\npost-integration lateral inhibition as a mechanism for competition. We argue\nthat these algorithms are computationally deficient in that they fail to\ngenerate, or learn, appropriate perceptual representations under certain\ncircumstances. An alternative neural network architecture is presented in which\nnodes compete for the right to receive inputs rather than for the right to\ngenerate outputs. This form of competition, implemented through pre-integration\nlateral inhibition, does provide appropriate coding properties and can be used\nto efficiently learn such representations. Furthermore, this architecture is\nconsistent with both neuro-anatomical and neuro-physiological data. We thus\nargue that pre-integration lateral inhibition has computational advantages over\nconventional neural network architectures while remaining equally biologically\nplausible."^^xsd:string;
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bibo:issue "9";
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bibo:volume "14";
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dct:date "2002";
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skos:prefLabel "Computational Neuroscience" .
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foaf:familyName "Johnson"^^xsd:string;
foaf:givenName "M. H."^^xsd:string;
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foaf:givenName "M. W."^^xsd:string;
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