Sunderland / New York / Rome / London / Chicago / /
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Nashua / Correa D. D. / MIT Press / Norton / J. B. & Teichmann S. A. / Oxford University Press / E. D. & Teichmann S. A. / /
Country
Netherlands / Italy / Jordan / / /
Facility
University of Chicago Press / Laboratory of Molecular Biology / Raffaele Calabretta Institute of Cognitive Sciences / Cambridge University / Yale University / /
IndustryTerm
information processing / visual neural networks / neural networks / non-modular systems / evolutionary connectionism approach using neural networks / natural systems / artificial neural networks / input systems / nonmodular networks / nonmodular neural networks / cognitive systems / evolutionary process neural networks / neural network / few cases network / genetic algorithm / cortical visual systems / possible solution / nonmodular visual neural networks / natural and artificial systems / dedicated synaptic systems / simulative tools / genetic algorithms / simulation networks / learning algorithm / show how using artificial neural networks / /
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Neurosurgery / /
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A.D. / /
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Jonathan Cape / /
Organization
Cambridge University / Italian National Research Council / MIT / European Society / Laboratory of Molecular Biology / Yale Center for Computational Ecology / Raffaele Calabretta Institute of Cognitive Sciences / Yale University / University of Chicago Press / Structural Studies Division / Cognitive Sciences and Technologies Italian National Research Council / Oxford University / Swiss army / /
Genetic interference reduces the evolvability of modular and nonmodular visual neural networks Raffaele Calabretta Institute of Cognitive Sciences and Technologies