00891cam2 22002531 450 SOBE0001715520220615073926.020110830d1985 |||||ita|0103 bagerDE<<3: >>Wissen, Glaube uns Skepsiszur Kritik von Religion und TheologieKarl LöwithStuttgartMetzlerc1985469 p.22 cm001SOBE000171482001 Sämtliche Schriften / Karl LöwithLöwith, KarlA600200025865070375663ITUNISOB20220615RICAUNISOBUNISOB100|Coll|51|K45258SOBE00017155M 102 Monografia moderna SBNM100|Coll|51|K000003SI45258acquistoIrovitoUNISOBUNISOB20110830073938.020220615073804.0AlfanoWissen, Glaube uns Skepsis1721246UNISOB03766nam 22007335 450 991025420260332120200704033134.09783319244068331924406X10.1007/978-3-319-24406-8(CKB)3710000000498920(EBL)4085631(SSID)ssj0001584572(PQKBManifestationID)16265740(PQKBTitleCode)TC0001584572(PQKBWorkID)14865569(PQKB)11116850(DE-He213)978-3-319-24406-8(MiAaPQ)EBC4085631(PPN)19052670X(EXLCZ)99371000000049892020151030d2016 u| 0engur|n|---|||||txtccrCognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields /by Robert Kozma, Walter J. Freeman1st ed. 2016.Cham :Springer International Publishing :Imprint: Springer,2016.1 online resource (267 p.)Studies in Systems, Decision and Control,2198-4182 ;39Description based upon print version of record.9783319244044 3319244043 Includes bibliographical references at the end of each chapters and index.This intriguing book was born out of the many discussions the authors had in the past 10 years about the role of scale-free structure and dynamics in producing intelligent behavior in brains. The microscopic dynamics of neural networks is well described by the prevailing paradigm based in a narrow interpretation of the neuron doctrine. This book broadens the doctrine by incorporating the dynamics of neural fields, as first revealed by modeling with differential equations (K-sets).  The book broadens that approach by application of random graph theory (neuropercolation). The book concludes with diverse commentaries that exemplify the wide range of mathematical/conceptual approaches to neural fields. This book is intended for researchers, postdocs, and graduate students, who see the limitations of network theory and seek a beachhead from which to embark on mesoscopic and macroscopic neurodynamics.Studies in Systems, Decision and Control,2198-4182 ;39Computational intelligenceArtificial intelligenceComputational complexityCognitive psychologyComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/T11022Cognitive Psychologyhttps://scigraph.springernature.com/ontologies/product-market-codes/Y20060Computational intelligence.Artificial intelligence.Computational complexity.Cognitive psychology.Computational Intelligence.Artificial Intelligence.Complexity.Cognitive Psychology.612.8233Kozma Robertauthttp://id.loc.gov/vocabulary/relators/aut761237Freeman Walter Jauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910254202603321Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields2532689UNINA