LEADER 03766nam  22007335  450 
001 9910254202603321
005 20200704033134.0
010   $a9783319244068
010   $a331924406X
024 7 $a10.1007/978-3-319-24406-8
035   $a(CKB)3710000000498920
035   $a(EBL)4085631
035   $a(SSID)ssj0001584572
035   $a(PQKBManifestationID)16265740
035   $a(PQKBTitleCode)TC0001584572
035   $a(PQKBWorkID)14865569
035   $a(PQKB)11116850
035   $a(DE-He213)978-3-319-24406-8
035   $a(MiAaPQ)EBC4085631
035   $a(PPN)19052670X
035   $a(EXLCZ)993710000000498920
100   $a20151030d2016      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aCognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields /$fby Robert Kozma, Walter J. Freeman
205   $a1st ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016.
215   $a1 online resource (267 p.)
225 1 $aStudies in Systems, Decision and Control,$x2198-4182 ;$v39
300   $aDescription based upon print version of record.
311 08$a9783319244044 
311 08$a3319244043 
320   $aIncludes bibliographical references at the end of each chapters and index.
330   $aThis intriguing book was born out of the many discussions the authors had in the past 10 years about the role of scale-free structure and dynamics in producing intelligent behavior in brains. The microscopic dynamics of neural networks is well described by the prevailing paradigm based in a narrow interpretation of the neuron doctrine. This book broadens the doctrine by incorporating the dynamics of neural fields, as first revealed by modeling with differential equations (K-sets).  The book broadens that approach by application of random graph theory (neuropercolation). The book concludes with diverse commentaries that exemplify the wide range of mathematical/conceptual approaches to neural fields. This book is intended for researchers, postdocs, and graduate students, who see the limitations of network theory and seek a beachhead from which to embark on mesoscopic and macroscopic neurodynamics.
410  0$aStudies in Systems, Decision and Control,$x2198-4182 ;$v39
606   $aComputational intelligence
606   $aArtificial intelligence
606   $aComputational complexity
606   $aCognitive psychology
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aComplexity$3https://scigraph.springernature.com/ontologies/product-market-codes/T11022
606   $aCognitive Psychology$3https://scigraph.springernature.com/ontologies/product-market-codes/Y20060
615  0$aComputational intelligence.
615  0$aArtificial intelligence.
615  0$aComputational complexity.
615  0$aCognitive psychology.
615 14$aComputational Intelligence.
615 24$aArtificial Intelligence.
615 24$aComplexity.
615 24$aCognitive Psychology.
676   $a612.8233
700   $aKozma$b Robert$4aut$4http://id.loc.gov/vocabulary/relators/aut$0761237
702   $aFreeman$b Walter J$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910254202603321
996   $aCognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields$92532689
997   $aUNINA