LEADER 04421nam 22006135 450 001 9910254317403321 005 20200707010423.0 010 $a3-319-55310-0 024 7 $a10.1007/978-3-319-55310-8 035 $a(CKB)3710000001307188 035 $a(DE-He213)978-3-319-55310-8 035 $a(MiAaPQ)EBC4854509 035 $a(PPN)201472600 035 $a(EXLCZ)993710000001307188 100 $a20170504d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNeuromorphic Cognitive Systems $eA Learning and Memory Centered Approach /$fby Qiang Yu, Huajin Tang, Jun Hu, Kay Tan Chen 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XIV, 172 p.) 225 1 $aIntelligent Systems Reference Library,$x1868-4394 ;$v126 311 $a3-319-55308-9 320 $aIncludes bibliographical references at the end of each chapters. 327 $a Introduction -- Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons -- A Spike-Timing Based Integrated Model for Pattern Recognition -- Precise-Spike-Driven Synaptic Plasticity for Hetero Association of Spatiotemporal Spike Patterns -- A Spiking Neural Network System for Robust Sequence Recognition -- Temporal Learning in Multilayer Spiking Neural Networks Through Construction of Causal Connections -- A Hierarchically Organized Memory Model with Temporal Population Coding -- Spiking Neuron Based Cognitive Memory Model. 330 $aThis book presents neuromorphic cognitive systems from a learning and memory-centered perspective. It illustrates how to build a system network of neurons to perform spike-based information processing, computing, and high-level cognitive tasks. It is beneficial to a wide spectrum of readers, including undergraduate and postgraduate students and researchers who are interested in neuromorphic computing and neuromorphic engineering, as well as engineers and professionals in industry who are involved in the design and applications of neuromorphic cognitive systems, neuromorphic sensors and processors, and cognitive robotics. The book formulates a systematic framework, from the basic mathematical and computational methods in spike-based neural encoding, learning in both single and multi-layered networks, to a near cognitive level composed of memory and cognition. Since the mechanisms for integrating spiking neurons integrate to formulate cognitive functions as in the brain are little understood, studies of neuromorphic cognitive systems are urgently needed. The topics covered in this book range from the neuronal level to the system level. In the neuronal level, synaptic adaptation plays an important role in learning patterns. In order to perform higher-level cognitive functions such as recognition and memory, spiking neurons with learning abilities are consistently integrated, building a system with encoding, learning and memory functionalities. The book describes these aspects in detail. 410 0$aIntelligent Systems Reference Library,$x1868-4394 ;$v126 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aNeurosciences 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 $aNeurosciences$3https://scigraph.springernature.com/ontologies/product-market-codes/B18006 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aNeurosciences. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aNeurosciences. 676 $a006.32 700 $aYu$b Qiang$4aut$4http://id.loc.gov/vocabulary/relators/aut$0909796 702 $aTang$b Huajin$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aHu$b Jun$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aTan Chen$b Kay$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254317403321 996 $aNeuromorphic Cognitive Systems$92138029 997 $aUNINA