| Description |
1 online resource (xiii, 324 pages) |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
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data file |
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Occupation/field of activity group: occ Engineers lcdgt |
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Occupation/field of activity group: occ University and college faculty members lcdgt |
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Gender group: gdr Men lcdgt |
| Bibliography |
Includes bibliographical references and index. |
| Note |
Online resource; title from PDF title page (ScienceDirect, viewed November 5, 2018). |
| Summary |
With contributions from pioneers and experts in the field of neural networks, this book covers the major basic ideas of brain-like computing behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as future alternatives. -- Edited summary from book. |
| Contents |
Chapter 1 Nature's learning rule: The Hebbian-LMS algorithm / Bernard Widrow, Youngsik Kim, Dookun Park and Jose Krause Perin -- Introduction -- ADALINE and the LMS algorithm, From the 1950s -- Unsupervised learning with Adaline, From the 1960s -- Robert Lucky's adaptive equalization, From the 1960s -- Bootstrap learning with a Sigmoidal neuron -- Bookstrap learning with a more "Biologically correct" Sigmoidal neuron -- Other clustering algorithms -- A general Hebbian-LMS algorithm -- The synapse -- Postulates of synaptic plasticity -- The postulates and the Hebbian-LMS algorithm -- Nature's Hebbian-LMS algorithm -- Conclusion -- Chapter 2 A half century of progress toward a unified neural theory of mind and brain with applications to autonomous adaptive agents and mental disorders / Stephen Grossberg -- Towards a unified theory of mind and brain -- A theoretical method for linking brain to mind: The method of minimal anatomies -- Revolutionary brain paradigms: Complementary computing and laminar computing -- The what and where cortical streams are complementary -- Adaptive resonance theory -- Vector associative maps for spatial representation and action -- Homologous laminar cortical circuits for all biological intelligence: Beyond Bayes -- Why a unified theory is possible: Equations, modules, and architectures -- All conscious states are resonant states -- The varieties of brain resonances and the conscious experiences that they support -- Why does resonance trigger consciousness? -- Towards autonomous adaptive intelligent agents and clinical therapies in society -- References -- Chapter 3 Third Gen AI as human experience based expert systems / Harold Szu and the AI working group -- Introduction -- Third gen AI -- MFE gradient descent -- Conclusion -- 4 The brain-mind-computer trichotomy: Hermeneutic approach / Péter Érdi -- Dichotomies -- Hermeneutics -- Schizophrenia: A broken hermeneutic cycle -- Toward the algorithms of neural/mental hermeneutic interpretation -- Chapter 5 From synapses to ephapsis: Embodied cognition and wearable personal assistants / Roman Ormandy -- Neural networks and neural fields -- Ephapsis -- Embodied cognition -- Wearable personal assistants -- References -- Chapter 6 Evolving and spiking connectionist systems for brain-inspired artificial intelligence / Nikola Kasabov -- From Aristotle's logic to artificial neural networks and hybrid systems -- Evolving connectionist systems (ECOS) -- Spiking neural networks (SNN) as brain-inspired ANN -- Brain-like AI systems based on SNN, NeuCube, deep learning algorithms -- Conclusion -- Chapter 7 Pitfalls and opportunities in the development and evaluation of artificial intelligence systems / David G. Brown and Frank W. Samuelson -- Introduction -- AI development -- AI evaluation -- Variability and bias in our performance estimates -- Conclusion -- Chapter 8 The new AI: Basic concepts, urgent risks and opportunities in the Internet of Things / Paulo J. Werbos -- Introduction and overview -- Brief history and foundations of the deep learning revolution -- From RNNs to mouse-level computational intelligence: Next big things and beyond -- Need for new directions in understanding brain and mind -- Information technology (IT) for human survival: An urgent unmet challenge -- References -- Chapter 9 Theory of the brain and mind: Visions and history / Daniel S. Levine -- Early history -- Emergence of some neural network principles -- Neural networks enter mainstream science -- Is computational neuroscience separate from neural network theory? -- Discussion -- References -- Chapter 10 Computers versus brains: Game is over or more to come? / Robert Kozma -- Introduction -- AI approaches -- Metastability in cognition and in brain dynamics -- Pragmatic implementation of complementarity for new AI -- Acknowledgments -- References -- Chapter 11 Deep learning apporaches to electrophysiological multivariate time-series analysis / Francesco Carlo Morabito, Maurizio Campolo, Cosimo leracitano and Nadia Mammone -- Introduction -- The neural network approach -- Deep architectures and learning -- Electrophysiological time-series -- Deep learning models for EEG signal processing -- Future directions of research -- Conclusion -- Further reading -- Chapter 12 Computational intelligence in the time of cyber-physical systems and the Internet of Things / Cesare Alippi and Seiichi Ozawa -- Introduction -- System architecture -- Energy harvesting and management -- Learning in nonstationary environments -- Model-free fault diagnosis systems -- Cybersecurity -- Conclusions -- Acknowledgments -- References -- Chapter 13 Multiview learning in biomedical applications / Angela Serra, Paola Galdi and Roberto Tagliaferri -- Introduction -- Multiview learning -- Multiview learning in bioinformatics -- Multiview learning in neuroinformatics -- Deep multimodal feature learning -- Conclusions -- References -- Chapter 14 Meaning versus information, prediction versus memory, and question versus answer / Yoonsuck Choe -- Introduction -- Meaning versus information -- Prediction versus memory -- Question versus answer -- Discussion -- Conclusion -- Acknowledgments -- References -- Chapter 15 Evolving deep neural networks / Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Daniel Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy and Babak Hodjat -- Introduction -- Background and related work -- Evolution of deep learning architectures -- Evolution of LSTM architectures -- Evolution of LSTM architectures -- Application case study: Image captioning for the blind -- Discussion and future work -- Conclusion -- References. |
| Subject |
Artificial intelligence.
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Neural networks (Computer science)
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Brain-computer interfaces.
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Artificial Intelligence |
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Neural Networks, Computer |
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Intelligence artificielle.
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Réseaux neuronaux (Informatique)
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Interfaces cerveau-ordinateur.
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artificial intelligence.
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COMPUTERS -- General.
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Artificial intelligence. (OCoLC)fst00817247
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Brain-computer interfaces. (OCoLC)fst01742078
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Neural networks (Computer science) (OCoLC)fst01036260
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| Genre/Form |
Electronic books.
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| Added Author |
Kozma, Robert, editor.
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Alippi, Cesare, editor.
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Choe, Yoonsuck, editor.
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Morabito, F. C. (Francesco Carlo), editor.
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| Other Form: |
Print version: Artificial intelligence in the age of neural networks and brain computing. London, United Kingdom : Academic Press, an imprint of Elsevier, [2019] 0128154802 9780128154809 (OCoLC)1013727193 |
| ISBN |
9780128162507 (electronic bk.) |
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0128162503 (electronic bk.) |
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9780128154809 (print) |
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0128154802 (print) |
| Standard No. |
AU@ 000064448503 |
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AU@ 000064848627 |
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AU@ 000065141262 |
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AU@ 000068582531 |
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CHBIS 011318714 |
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CHVBK 55150417X |
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UKMGB 019118948 |
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