LEADER 03565nam 22005775 450 001 9910483789103321 005 20200705132916.0 010 $a3-030-17840-4 024 7 $a10.1007/978-3-030-17840-6 035 $a(CKB)4100000007938140 035 $a(MiAaPQ)EBC5755030 035 $a(DE-He213)978-3-030-17840-6 035 $a(PPN)243770472 035 $a(EXLCZ)994100000007938140 100 $a20190415d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aReconfigurable Cellular Neural Networks and Their Applications /$fby Mü?tak E. Yalç?n, Tuba Ayhan, Ramazan Yeniçeri 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (79 pages) 225 1 $aSpringerBriefs in Nonlinear Circuits,$x2520-1433 311 $a3-030-17839-0 327 $aIntroduction -- Arti?cial Neural Network Models -- Arti?cial Olfaction System -- Implementations of CNNs -- Index. 330 $aThis book explores how neural networks can be designed to analyze sensory data in a way that mimics natural systems. It introduces readers to the cellular neural network (CNN) and formulates it to match the behavior of the Wilson?Cowan model. In turn, two properties that are vital in nature are added to the CNN to help it more accurately deliver mimetic behavior: randomness of connection, and the presence of different dynamics (excitatory and inhibitory) within the same network. It uses an ID matrix to determine the location of excitatory and inhibitory neurons, and to reconfigure the network to optimize its topology. The book demonstrates that reconfiguring a single-layer CNN is an easier and more flexible solution than the procedure required in a multilayer CNN, in which excitatory and inhibitory neurons are separate, and that the key CNN criteria of a spatially invariant template and local coupling are fulfilled. In closing, the application of the authors? neuron population model as a feature extractor is exemplified using odor and electroencephalogram classification. 410 0$aSpringerBriefs in Nonlinear Circuits,$x2520-1433 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aElectronic circuits 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 $aElectronic Circuits and Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/P31010 606 $aCircuits and Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/T24068 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aElectronic circuits. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aElectronic Circuits and Devices. 615 24$aCircuits and Systems. 676 $a006.32 700 $aYalç?n$b Mü?tak E$4aut$4http://id.loc.gov/vocabulary/relators/aut$0515714 702 $aAyhan$b Tuba$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aYeniçeri$b Ramazan$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910483789103321 996 $aReconfigurable Cellular Neural Networks and Their Applications$92844595 997 $aUNINA