MARC Record from marc_nuls

LEADER: 08696cam 2200421Ii 4500
001 9925411197701661
005 20190417170453.8
008 171203t20192019enka b 001 0 eng d
020 $a9780128154809$q(paperback)
020 $a0128154802$q(paperback)
035 $a99981933775
035 $a(OCoLC)1013727193
035 $a(OCoLC)on1013727193
040 $aYDX$beng$erda$cYDX$dOCLCQ$dLTSCA$dOCLCF$dOBE$dGYG
050 4 $aQ335$b.A78773 2019
082 04 $a006.3$223
245 00 $aArtificial intelligence in the age of neural networks and brain computing /$cedited by Robert Kozma, Cesare Alippi, Yoonsuck Choe, Francesco Carlo Morabito.
264 1 $aLondon, United Kingdom ;$aSan Diego, CA, United States :$bAcademic Press, an imprint of Elsevier,$c[2019]
264 4 $c℗♭2019
300 $axxv, 324 pages :$billustrations,$c24 cm
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
385 $mOccupation/field of activity group:$nocc$aEngineers$2lcdgt
386 $mOccupation/field of activity group:$nocc$aUniversity and college faculty members$2lcdgt
386 $mGender group:$ngdr$aMen$2lcdgt
504 $aIncludes bibliographical references and index.
505 00 $gChapter 1$tNature's learning rule: The Hebbian-LMS algorithm /$rBernard Widrow, Youngsik Kim, Dookun Park and Jose Krause Perin --$tIntroduction --$tADALINE and the LMS algorithm, From the 1950s --$tUnsupervised learning with Adaline, From the 1960s --$tRobert Lucky's adaptive equalization, From the 1960s --$tBootstrap learning with a Sigmoidal neuron --$tBookstrap learning with a more "Biologically correct" Sigmoidal neuron --$tOther clustering algorithms --$tA general Hebbian-LMS algorithm --$tThe synapse --$tPostulates of synaptic plasticity --$tThe postulates and the Hebbian-LMS algorithm --$tNature's Hebbian-LMS algorithm --$tConclusion --$gChapter 2$tA half century of progress toward a unified neural theory of mind and brain with applications to autonomous adaptive agents and mental disorders /$rStephen Grossberg --$tTowards a unified theory of mind and brain --$tA theoretical method for linking brain to mind: The method of minimal anatomies --$tRevolutionary brain paradigms: Complementary computing and laminar computing --$tThe what and where cortical streams are complementary --$tAdaptive resonance theory --$tVector associative maps for spatial representation and action --$tHomologous laminar cortical circuits for all biological intelligence: Beyond Bayes --$tWhy a unified theory is possible: Equations, modules, and architectures --$tAll conscious states are resonant states --$tThe varieties of brain resonances and the conscious experiences that they support --$tWhy does resonance trigger consciousness? --$tTowards autonomous adaptive intelligent agents and clinical therapies in society --$tReferences --$gChapter 3$tThird Gen AI as human experience based expert systems /$rHarold Szu and the AI working group --$tIntroduction --$tThird gen AI --$tMFE gradient descent --$tConclusion --$g4$tThe brain-mind-computer trichotomy: Hermeneutic approach /$rPe ter E rdi --$tDichotomies --$tHermeneutics --$tSchizophrenia: A broken hermeneutic cycle --$tToward the algorithms of neural/mental hermeneutic interpretation --$gChapter 5$tFrom synapses to ephapsis: Embodied cognition and wearable personal assistants / Roman Ormandy --$tNeural networks and neural fields --$tEphapsis --$tEmbodied cognition --$tWearable personal assistants --$tReferences --$gChapter 6$tEvolving and spiking connectionist systems for brain-inspired artificial intelligence /$rNikola Kasabov --$tFrom Aristotle's logic to artificial neural networks and hybrid systems --$tEvolving connectionist systems (ECOS) --$tSpiking neural networks (SNN) as brain-inspired ANN --$tBrain-like AI systems based on SNN, NeuCube, deep learning algorithms --$tConclusion --$gChapter 7$tPitfalls and opportunities in the development and evaluation of artificial intelligence systems /$rDavid G. Brown and Frank W. Samuelson --$tIntroduction --$tAI development --$tAI evaluation --$tVariability and bias in our performance estimates --$tConclusion --$gChapter 8$tThe new AI: Basic concepts, urgent risks and opportunities in the Internet of Things /$rPaulo J. Werbos --$tIntroduction and overview --$tBrief history and foundations of the deep learning revolution --$tFrom RNNs to mouse-level computational intelligence: Next big things and beyond --$tNeed for new directions in understanding brain and mind --$tInformation technology (IT) for human survival: An urgent unmet challenge --$tReferences --$gChapter 9$tTheory of the brain and mind: Visions and history /$rDaniel S. Levine --$tEarly history --$tEmergence of some neural network principles --$tNeural networks enter mainstream science --$tIs computational neuroscience separate from neural network theory? --$tDiscussion --$tReferences --$gChapter 10$tComputers versus brains: Game is over or more to come? /$rRobert Kozma --$tIntroduction --$tAI approaches --$tMetastability in cognition and in brain dynamics --$tPragmatic implementation of complementarity for new AI --$tAcknowledgments --$tReferences --$gChapter 11$tDeep learning apporaches to electrophysiological multivariate time-series analysis /$rFrancesco Carlo Morabito, Maurizio Campolo, Cosimo leracitano and Nadia Mammone --$tIntroduction --$tThe neural network approach --$tDeep architectures and learning --$tElectrophysiological time-series --$tDeep learning models for EEG signal processing --$tFuture directions of research --$tConclusion --$tFurther reading --$gChapter 12$tComputational intelligence in the time of cyber-physical systems and the Internet of Things /$rCesare Alippi and Seiichi Ozawa --$tIntroduction --$tSystem architecture --$tEnergy harvesting and management --$tLearning in nonstationary environments --$tModel-free fault diagnosis systems --$tCybersecurity --$tConclusions --$tAcknowledgments --$tReferences --$gChapter 13$tMultiview learning in biomedical applications /$rAngela Serra, Paola Galdi and Roberto Tagliaferri --$tIntroduction --$tMultiview learning --$tMultiview learning in bioinformatics --$tMultiview learning in neuroinformatics --$tDeep multimodal feature learning --$tConclusions --$tReferences --$gChapter 14$tMeaning versus information, prediction versus memory, and question versus answer /$rYoonsuck Choe --$tIntroduction --$tMeaning versus information --$tPrediction versus memory --$tQuestion versus answer --$tDiscussion --$tConclusion --$tAcknowledgments --$tReferences --$gChapter 15$tEvolving deep neural networks /$rRisto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Daniel Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy and Babak Hodjat --$tIntroduction --$tBackground and related work --$tEvolution of deep learning architectures --$tEvolution of LSTM architectures --$tEvolution of LSTM architectures --$tApplication case study: Image captioning for the blind --$tDiscussion and future work --$tConclusion --$tReferences.
520 $aArtificial Intelligence in the Age of Neural Networks and Brain Computing is the comprehensive guide for neural network advances in artificial intelligence (AI). It covers the major, basic ideas of "brain-like computing" behind AI, providing a framework to deep learning and launching novel and intriguing paradigms as possible future alternatives. Following an introduction, initial chapters discuss revolutionary new brain-mind approaches alternative to deep learning, the brain-mind-computer trichotomy, pitfalls and opportunities in the development of AI systems. Subsequent chapters explore a deep learning approach to electrophysiological multivariate time series analysis, multiview learning in biomedical applications, and the evolution of deep neural networks. This is an essential companion to researchers, engineers, advance AI practitioners, postdoctoral students in computational intelligence and neural engineering, and the technically oriented public. It provides access to the latest up-to-date knowledge from top, global experts working on theory and cutting-edge applications in signal processing, speech recognition, games, adaptive control, and decision-making. -- From back cover.
650 0 $aArtificial intelligence.
650 0 $aNeural networks (Computer science)
650 0 $aBrain-computer interfaces.
700 1 $aKozma, Robert,$eeditor.
700 1 $aAlippi, Cesare,$eeditor.
700 1 $aChoe, Yoonsuck,$eeditor.
700 1 $aMorabito, F. C.$q(Francesco Carlo),$eeditor.
947 $hCIRCSTACKS$r31786103139843
980 $a99981933775