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Artificial intelligence in the age of neural networks and brain computing / / edited by Robert Kozma [and three others]
| Artificial intelligence in the age of neural networks and brain computing / / edited by Robert Kozma [and three others] |
| Pubbl/distr/stampa | London, United Kingdom : , : Academic Press, An imprint of Elsevier, , [2019] |
| Descrizione fisica | 1 online resource (352 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Neural networks (Computer science) |
| ISBN |
0-12-816250-3
0-12-815480-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover -- Artificial Intelligence in the Age of Neural Networks and Brain Computing -- Artificial Intelligence in the Age of Neural Networks and Brain Computing -- Copyright -- Contents -- List of Contributors -- Editors' Brief Biographies -- Introduction -- 1 - Nature's Learning Rule: The Hebbian-LMS Algorithm -- 1. INTRODUCTION -- 2. ADALINE AND THE LMS ALGORITHM, FROM THE 1950S -- 3. UNSUPERVISED LEARNING WITH ADALINE, FROM THE 1960S -- 4. ROBERT LUCKY'S ADAPTIVE EQUALIZATION, FROM THE 1960S -- 5. BOOTSTRAP LEARNING WITH A SIGMOIDAL NEURON -- 6. BOOTSTRAP LEARNING WITH A MORE "BIOLOGICALLY CORRECT" SIGMOIDAL NEURON -- 6.1 TRAINING A NETWORK OF HEBBIAN-LMS NEURONS -- 7. OTHER CLUSTERING ALGORITHMS -- 7.1 K-MEANS CLUSTERING -- 7.2 EXPECTATION-MAXIMIZATION ALGORITHM -- 7.3 DENSITY-BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE ALGORITHM -- 7.4 COMPARISON BETWEEN CLUSTERING ALGORITHMS -- 8. A GENERAL HEBBIAN-LMS ALGORITHM -- 9. THE SYNAPSE -- 10. POSTULATES OF SYNAPTIC PLASTICITY -- 11. THE POSTULATES AND THE HEBBIAN-LMS ALGORITHM -- 12. NATURE'S HEBBIAN-LMS ALGORITHM -- 13. CONCLUSION -- APPENDIX: TRAINABLE NEURAL NETWORK INCORPORATING HEBBIAN-LMS LEARNING -- ACKNOWLEDGMENTS -- REFERENCES -- 2 - A Half Century of Progress Toward a Unified Neural Theory of Mind and Brain With Applications to Autonomous Ada ... -- 1. TOWARDS A UNIFIED THEORY OF MIND AND BRAIN -- 2. A THEORETICAL METHOD FOR LINKING BRAIN TO MIND: THE METHOD OF MINIMAL ANATOMIES -- 3. REVOLUTIONARY BRAIN PARADIGMS: COMPLEMENTARY COMPUTING AND LAMINAR COMPUTING -- 4. THE WHAT AND WHERE CORTICAL STREAMS ARE COMPLEMENTARY -- 5. ADAPTIVE RESONANCE THEORY -- 6. VECTOR ASSOCIATIVE MAPS FOR SPATIAL REPRESENTATION AND ACTION -- 7. HOMOLOGOUS LAMINAR CORTICAL CIRCUITS FOR ALL BIOLOGICAL INTELLIGENCE: BEYOND BAYES.
8. WHY A UNIFIED THEORY IS POSSIBLE: EQUATIONS, MODULES, AND ARCHITECTURES -- 9. ALL CONSCIOUS STATES ARE RESONANT STATES -- 10. THE VARIETIES OF BRAIN RESONANCES AND THE CONSCIOUS EXPERIENCES THAT THEY SUPPORT -- 11. WHY DOES RESONANCE TRIGGER CONSCIOUSNESS? -- 12. TOWARDS AUTONOMOUS ADAPTIVE INTELLIGENT AGENTS AND CLINICAL THERAPIES IN SOCIETY -- REFERENCES -- 3 - Third Gen AI as Human Experience Based Expert Systems -- 1. INTRODUCTION -- 2. THIRD GEN AI -- 2.1 MAXWELL-BOLTZMANN HOMEOSTASIS [8] -- 2.2 THE INVERSE IS CONVOLUTION NEURAL NETWORKS -- 2.3 FUZZY MEMBERSHIP FUNCTION (FMF AND DATA BASIS) -- 3. MFE GRADIENT DESCENT -- 3.1 UNSUPERVISED LEARNING RULE -- 4. CONCLUSION -- ACKNOWLEDGMENT -- REFERENCES -- FURTHER READING -- 4 - The Brain-Mind-Computer Trichotomy: Hermeneutic Approach -- 1. DICHOTOMIES -- 1.1 THE BRAIN-MIND PROBLEM -- 1.2 THE BRAIN-COMPUTER ANALOGY/DISANALOGY -- 1.3 THE COMPUTATIONAL THEORY OF MIND -- 2. HERMENEUTICS -- 2.1 SECOND-ORDER CYBERNETICS -- 2.2 HERMENEUTICS OF THE BRAIN -- 2.3 THE BRAIN AS A HERMENEUTIC DEVICE -- 2.4 NEURAL HERMENEUTICS -- 3. SCHIZOPHRENIA: A BROKEN HERMENEUTIC CYCLE -- 3.1 HERMENEUTICS, COGNITIVE SCIENCE, SCHIZOPHRENIA -- 4. TOWARD THE ALGORITHMS OF NEURAL/MENTAL HERMENEUTICS -- 4.1 UNDERSTANDING SITUATIONS: NEEDS HERMENEUTIC INTERPRETATION -- ACKNOWLEDGMENTS -- REFERENCES -- FURTHER READING -- 5 - From Synapses to Ephapsis: Embodied Cognition and Wearable Personal Assistants -- 1. NEURAL NETWORKS AND NEURAL FIELDS -- 2. EPHAPSIS -- 3. EMBODIED COGNITION -- 4. WEARABLE PERSONAL ASSISTANTS -- REFERENCES -- 6 - Evolving and Spiking Connectionist Systems for Brain-Inspired Artificial Intelligence -- 1. FROM ARISTOTLE'S LOGIC TO ARTIFICIAL NEURAL NETWORKS AND HYBRID SYSTEMS -- 1.1 ARISTOTLE'S LOGIC AND RULE-BASED SYSTEMS FOR KNOWLEDGE REPRESENTATION AND REASONING. 1.2 FUZZY LOGIC AND FUZZY RULE-BASED SYSTEMS -- 1.3 CLASSICAL ARTIFICIAL NEURAL NETWORKS (ANN) -- 1.4 INTEGRATING ANN WITH RULE-BASED SYSTEMS: HYBRID CONNECTIONIST SYSTEMS -- 1.5 EVOLUTIONARY COMPUTATION (EC): LEARNING PARAMETER VALUES OF ANN THROUGH EVOLUTION OF INDIVIDUAL MODELS AS PART OF POPULATIO ... -- 2. EVOLVING CONNECTIONIST SYSTEMS (ECOS) -- 2.1 PRINCIPLES OF ECOS -- 2.2 ECOS REALIZATIONS AND AI APPLICATIONS -- 3. SPIKING NEURAL NETWORKS (SNN) AS BRAIN-INSPIRED ANN -- 3.1 MAIN PRINCIPLES, METHODS, AND EXAMPLES OF SNN AND EVOLVING SNN (ESNN) -- 3.2 APPLICATIONS AND IMPLEMENTATIONS OF SNN FOR AI -- 4. BRAIN-LIKE AI SYSTEMS BASED ON SNN. NEUCUBE. DEEP LEARNING ALGORITHMS -- 4.1 BRAIN-LIKE AI SYSTEMS. NEUCUBE -- 4.2 DEEP LEARNING AND DEEP KNOWLEDGE REPRESENTATION IN NEUCUBE SNN MODELS: METHODS AND AI APPLICATIONS [6] -- 4.2.1 Supervised Learning for Classification of Learned Patterns in a SNN Model -- 4.2.2 Semisupervised Learning -- 5. CONCLUSION -- ACKNOWLEDGMENT -- REFERENCES -- 7 - Pitfalls and Opportunities in the Development and Evaluation of Artificial Intelligence Systems -- 1. INTRODUCTION -- 2. AI DEVELOPMENT -- 2.1 OUR DATA ARE CRAP -- 2.2 OUR ALGORITHM IS CRAP -- 3. AI EVALUATION -- 3.1 USE OF DATA -- 3.2 PERFORMANCE MEASURES -- 3.3 DECISION THRESHOLDS -- 4. VARIABILITY AND BIAS IN OUR PERFORMANCE ESTIMATES -- 5. CONCLUSION -- ACKNOWLEDGMENT -- REFERENCES -- 8 - The New AI: Basic Concepts, and Urgent Risks and Opportunities in the Internet of Things -- 1. INTRODUCTION AND OVERVIEW -- 1.1 DEEP LEARNING AND NEURAL NETWORKS BEFORE 2009-11 -- 1.2 THE DEEP LEARNING CULTURAL REVOLUTION AND NEW OPPORTUNITIES -- 1.3 NEED AND OPPORTUNITY FOR A DEEP LEARNING REVOLUTION IN NEUROSCIENCE -- 1.4 RISKS OF HUMAN EXTINCTION, NEED FOR NEW PARADIGM FOR INTERNET OF THINGS -- 2. BRIEF HISTORY AND FOUNDATIONS OF THE DEEP LEARNING REVOLUTION. 2.1 OVERVIEW OF THE CURRENT LANDSCAPE -- 2.2 HOW THE DEEP REVOLUTION ACTUALLY HAPPENED -- 2.3 BACKPROPAGATION: THE FOUNDATION WHICH MADE THIS POSSIBLE -- 2.4 CONNS, 3 LAYERS, AND AUTOENCODERS: THE THREE MAIN TOOLS OF TODAY'S DEEP LEARNING -- 3. FROM RNNS TO MOUSE-LEVEL COMPUTATIONAL INTELLIGENCE: NEXT BIG THINGS AND BEYOND -- 3.1 TWO TYPES OF RECURRENT NEURAL NETWORK -- 3.2 DEEP VERSUS BROAD: A FEW PRACTICAL ISSUES -- 3.3 ROADMAP FOR MOUSE-LEVEL COMPUTATIONAL INTELLIGENCE (MLCI) -- 3.4 EMERGING NEW HARDWARE TO ENHANCE CAPABILITY BY ORDERS OF MAGNITUDE -- 4. NEED FOR NEW DIRECTIONS IN UNDERSTANDING BRAIN AND MIND -- 4.1 TOWARD A CULTURAL REVOLUTION IN HARD NEUROSCIENCE -- 4.2 FROM MOUSE BRAIN TO HUMAN MIND: PERSONAL VIEWS OF THE LARGER PICTURE -- 5. INFORMATION TECHNOLOGY (IT) FOR HUMAN SURVIVAL: AN URGENT UNMET CHALLENGE -- 5.1 EXAMPLES OF THE THREAT FROM ARTIFICIAL STUPIDITY -- 5.2 CYBER AND EMP THREATS TO THE POWER GRID -- 5.3 THREATS FROM UNDEREMPLOYMENT OF HUMANS -- 5.4 PRELIMINARY VISION OF THE OVERALL PROBLEM, AND OF THE WAY OUT -- REFERENCES -- 9 - Theory of the Brain and Mind: Visions and History -- 1. EARLY HISTORY -- 2. EMERGENCE OF SOME NEURAL NETWORK PRINCIPLES -- 3. NEURAL NETWORKS ENTER MAINSTREAM SCIENCE -- 4. IS COMPUTATIONAL NEUROSCIENCE SEPARATE FROM NEURAL NETWORK THEORY? -- 5. DISCUSSION -- REFERENCES -- 10 - Computers Versus Brains: Game Is Over or More to Come? -- 1. INTRODUCTION -- 2. AI APPROACHES -- 3. METASTABILITY IN COGNITION AND IN BRAIN DYNAMICS -- 4. MULTISTABILITY IN PHYSICS AND BIOLOGY -- 5. PRAGMATIC IMPLEMENTATION OF COMPLEMENTARITY FOR NEW AI -- ACKNOWLEDGMENTS -- REFERENCES -- 11 - Deep Learning Approaches to Electrophysiological Multivariate Time-Series Analysis∗∗ -- 1. INTRODUCTION -- 2. THE NEURAL NETWORK APPROACH -- 3. DEEP ARCHITECTURES AND LEARNING -- 3.1 DEEP BELIEF NETWORKS -- 3.2 STACKED AUTOENCODERS. 3.3 CONVOLUTIONAL NEURAL NETWORKS -- 4. ELECTROPHYSIOLOGICAL TIME-SERIES -- 4.1 MULTICHANNEL NEUROPHYSIOLOGICAL MEASUREMENTS OF THE ACTIVITY OF THE BRAIN -- 4.2 ELECTROENCEPHALOGRAPHY (EEG) -- 4.3 HIGH-DENSITY ELECTROENCEPHALOGRAPHY -- 4.4 MAGNETOENCEPHALOGRAPHY -- 5. DEEP LEARNING MODELS FOR EEG SIGNAL PROCESSING -- 5.1 STACKED AUTOENCODERS -- 5.2 SUMMARY OF THE PROPOSED METHOD FOR EEG CLASSIFICATION -- 5.3 DEEP CONVOLUTIONAL NEURAL NETWORKS -- 5.4 OTHER DL APPROACHES -- 6. FUTURE DIRECTIONS OF RESEARCH -- 6.1 DL INTERPRETABILITY -- 6.2 ADVANCED LEARNING APPROACHES IN DL -- 6.3 ROBUSTNESS OF DL NETWORKS -- 7. CONCLUSIONS -- REFERENCES -- FURTHER READING -- 12 - Computational Intelligence in the Time of Cyber-Physical Systems and the Internet of Things -- 1. INTRODUCTION -- 2. SYSTEM ARCHITECTURE -- 3. ENERGY HARVESTING AND MANAGEMENT -- 3.1 ENERGY HARVESTING -- 3.2 ENERGY MANAGEMENT AND RESEARCH CHALLENGES -- 4. LEARNING IN NONSTATIONARY ENVIRONMENTS -- 4.1 PASSIVE ADAPTATION MODALITY -- 4.2 ACTIVE ADAPTATION MODALITY -- 4.3 RESEARCH CHALLENGES -- 5. MODEL-FREE FAULT DIAGNOSIS SYSTEMS -- 5.1 MODEL-FREE FAULT DIAGNOSIS SYSTEMS -- 5.2 RESEARCH CHALLENGES -- 6. CYBERSECURITY -- 6.1 HOW CAN CPS AND IOT BE PROTECTED FROM CYBERATTACKS? -- 6.2 CASE STUDY: DARKNET ANALYSIS TO CAPTURE MALICIOUS CYBERATTACK BEHAVIORS -- 7. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- 13 - Multiview Learning in Biomedical Applications -- 1. INTRODUCTION -- 2. MULTIVIEW LEARNING -- 2.1 INTEGRATION STAGE -- 2.2 TYPE OF DATA -- 2.3 TYPES OF ANALYSIS -- 3. MULTIVIEW LEARNING IN BIOINFORMATICS -- 3.1 PATIENT SUBTYPING -- 3.2 DRUG REPOSITIONING -- 4. MULTIVIEW LEARNING IN NEUROINFORMATICS -- 4.1 AUTOMATED DIAGNOSIS SUPPORT TOOLS FOR NEURODEGENERATIVE DISORDERS -- 4.2 MULTIMODAL BRAIN PARCELLATION -- 5. DEEP MULTIMODAL FEATURE LEARNING. 5.1 DEEP LEARNING APPLICATION TO PREDICT PATIENT'S SURVIVAL. |
| Record Nr. | UNINA-9910583305603321 |
| London, United Kingdom : , : Academic Press, An imprint of Elsevier, , [2019] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Neurodynamics of Cognition and Consciousness [[electronic resource] /] / edited by Leonid I. Perlovsky, Robert Kozma
| Neurodynamics of Cognition and Consciousness [[electronic resource] /] / edited by Leonid I. Perlovsky, Robert Kozma |
| Edizione | [1st ed. 2007.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2007 |
| Descrizione fisica | 1 online resource (377 p.) |
| Disciplina | 612.8/233 |
| Collana | Understanding Complex Systems |
| Soggetto topico |
Applied mathematics
Engineering mathematics Statistical physics Dynamical systems Artificial intelligence Mathematical and Computational Engineering Complex Systems Artificial Intelligence Statistical Physics and Dynamical Systems |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-04570-5
9786611045708 3-540-73267-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Neurodynamics of Cognition and Consciousness -- Neurodynamics of Cognition and Consciousness -- Neurocognition and Human Consciousness -- Proposed Cortical “Shutter” Mechanism in Cinematographic Perception -- Toward a Complementary Neuroscience: Metastable Coordination Dynamics of the Brain -- The Formation of Global Neurocognitive State -- Neural Dynamic Logic of Consciousness: the Knowledge Instinct -- Using ADP to Understand and Replicate Brain Intelligence: The Next Level Design? -- Neurodynamics of Intentional Behavior Generation -- How Does the Brain Create, Change, and Selectively Override its Rules of Conduct? -- Cognitive Computing for Sensory Perception -- Shape Recognition Through Dynamic Motor Representations -- A Biologically Inspired Dynamic Model for Object Recognition -- A Brain-Inspired Model for Recognizing Human Emotional States from Facial Expression -- Engineering Applications of Olfactory Model from Pattern Recognition to Artificial Olfaction -- Recursive Nodes with Rich Dynamics as Modeling Tools for Cognitive Functions -- Giving Meaning to Cycles to Go Beyond the Limitations of Fixed Point Attractors -- Complex Biological Memory Conceptualized as an Abstract Communication System–Human Long Term Memories Grow in Complexity during Sleep and Undergo Selection while Awake -- Nonlinear High-Order Model for Dynamic Synapse with Multiple Vesicle Pools. |
| Record Nr. | UNINA-9910145435103321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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