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Artificial intelligence in the age of neural networks and brain computing /
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. --
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London, United Kingdom :
Academic Press, an imprint of Elsevier,
[2019]
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| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 245 | 0 | 0 | |a Artificial intelligence in the age of neural networks and brain computing / |c edited by Robert Kozma, Cesare Alippi, Yoonsuck Choe, Francesco Carlo Morabito. |
| 264 | 1 | |a London, United Kingdom : |b Academic Press, an imprint of Elsevier, |c [2019] | |
| 300 | |a 1 online resource (xiii, 324 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a data file | ||
| 385 | |m Occupation/field of activity group: |n occ |a Engineers |2 lcdgt | ||
| 386 | |m Occupation/field of activity group: |n occ |a University and college faculty members |2 lcdgt | ||
| 386 | |m Gender group: |n gdr |a Men |2 lcdgt | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (ScienceDirect, viewed November 5, 2018). | |
| 520 | |a 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. -- |c Edited summary from book. | ||
| 505 | 0 | 0 | |g Chapter 1 |t Nature's learning rule: The Hebbian-LMS algorithm / |r Bernard Widrow, Youngsik Kim, Dookun Park and Jose Krause Perin -- |t Introduction -- |t ADALINE and the LMS algorithm, From the 1950s -- |t Unsupervised learning with Adaline, From the 1960s -- |t Robert Lucky's adaptive equalization, From the 1960s -- |t Bootstrap learning with a Sigmoidal neuron -- |t Bookstrap learning with a more "Biologically correct" Sigmoidal neuron -- |t Other clustering algorithms -- |t A general Hebbian-LMS algorithm -- |t The synapse -- |t Postulates of synaptic plasticity -- |t The postulates and the Hebbian-LMS algorithm -- |t Nature's Hebbian-LMS algorithm -- |t Conclusion -- |g Chapter 2 |t A half century of progress toward a unified neural theory of mind and brain with applications to autonomous adaptive agents and mental disorders / |r Stephen Grossberg -- |t Towards a unified theory of mind and brain -- |t A theoretical method for linking brain to mind: The method of minimal anatomies -- |t Revolutionary brain paradigms: Complementary computing and laminar computing -- |t The what and where cortical streams are complementary -- |t Adaptive resonance theory -- |t Vector associative maps for spatial representation and action -- |t Homologous laminar cortical circuits for all biological intelligence: Beyond Bayes -- |t Why a unified theory is possible: Equations, modules, and architectures -- |t All conscious states are resonant states -- |t The varieties of brain resonances and the conscious experiences that they support -- |t Why does resonance trigger consciousness? -- |t Towards autonomous adaptive intelligent agents and clinical therapies in society -- |t References -- |g Chapter 3 |t Third Gen AI as human experience based expert systems / |r Harold Szu and the AI working group -- |t Introduction -- |t Third gen AI -- |t MFE gradient descent -- |t Conclusion -- |g 4 |t The brain-mind-computer trichotomy: Hermeneutic approach / |r �Pter �rdi -- |t Dichotomies -- |t Hermeneutics -- |t Schizophrenia: A broken hermeneutic cycle -- |t Toward the algorithms of neural/mental hermeneutic interpretation -- |g Chapter 5 |t From synapses to ephapsis: Embodied cognition and wearable personal assistants / Roman Ormandy -- |t Neural networks and neural fields -- |t Ephapsis -- |t Embodied cognition -- |t Wearable personal assistants -- |t References -- |g Chapter 6 |t Evolving and spiking connectionist systems for brain-inspired artificial intelligence / |r Nikola Kasabov -- |t From Aristotle's logic to artificial neural networks and hybrid systems -- |t Evolving connectionist systems (ECOS) -- |t Spiking neural networks (SNN) as brain-inspired ANN -- |t Brain-like AI systems based on SNN, NeuCube, deep learning algorithms -- |t Conclusion -- |g Chapter 7 |t Pitfalls and opportunities in the development and evaluation of artificial intelligence systems / |r David G. Brown and Frank W. Samuelson -- |t Introduction -- |t AI development -- |t AI evaluation -- |t Variability and bias in our performance estimates -- |t Conclusion -- |g Chapter 8 |t The new AI: Basic concepts, urgent risks and opportunities in the Internet of Things / |r Paulo J. Werbos -- |t Introduction and overview -- |t Brief history and foundations of the deep learning revolution -- |t From RNNs to mouse-level computational intelligence: Next big things and beyond -- |t Need for new directions in understanding brain and mind -- |t Information technology (IT) for human survival: An urgent unmet challenge -- |t References -- |g Chapter 9 |t Theory of the brain and mind: Visions and history / |r Daniel S. Levine -- |t Early history -- |t Emergence of some neural network principles -- |t Neural networks enter mainstream science -- |t Is computational neuroscience separate from neural network theory? -- |t Discussion -- |t References -- |g Chapter 10 |t Computers versus brains: Game is over or more to come? / |r Robert Kozma -- |t Introduction -- |t AI approaches -- |t Metastability in cognition and in brain dynamics -- |t Pragmatic implementation of complementarity for new AI -- |t Acknowledgments -- |t References -- |g Chapter 11 |t Deep learning apporaches to electrophysiological multivariate time-series analysis / |r Francesco Carlo Morabito, Maurizio Campolo, Cosimo leracitano and Nadia Mammone -- |t Introduction -- |t The neural network approach -- |t Deep architectures and learning -- |t Electrophysiological time-series -- |t Deep learning models for EEG signal processing -- |t Future directions of research -- |t Conclusion -- |t Further reading -- |g Chapter 12 |t Computational intelligence in the time of cyber-physical systems and the Internet of Things / |r Cesare Alippi and Seiichi Ozawa -- |t Introduction -- |t System architecture -- |t Energy harvesting and management -- |t Learning in nonstationary environments -- |t Model-free fault diagnosis systems -- |t Cybersecurity -- |t Conclusions -- |t Acknowledgments -- |t References -- |g Chapter 13 |t Multiview learning in biomedical applications / |r Angela Serra, Paola Galdi and Roberto Tagliaferri -- |t Introduction -- |t Multiview learning -- |t Multiview learning in bioinformatics -- |t Multiview learning in neuroinformatics -- |t Deep multimodal feature learning -- |t Conclusions -- |t References -- |g Chapter 14 |t Meaning versus information, prediction versus memory, and question versus answer / |r Yoonsuck Choe -- |t Introduction -- |t Meaning versus information -- |t Prediction versus memory -- |t Question versus answer -- |t Discussion -- |t Conclusion -- |t Acknowledgments -- |t References -- |g Chapter 15 |t Evolving deep neural networks / |r Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Daniel Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy and Babak Hodjat -- |t Introduction -- |t Background and related work -- |t Evolution of deep learning architectures -- |t Evolution of LSTM architectures -- |t Evolution of LSTM architectures -- |t Application case study: Image captioning for the blind -- |t Discussion and future work -- |t Conclusion -- |t References. |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 0 | |a Brain-computer interfaces. | |
| 650 | 2 | |a Artificial Intelligence |0 (DNLM)D001185 | |
| 650 | 2 | |a Neural Networks, Computer |0 (DNLM)D016571 | |
| 650 | 6 | |a Intelligence artificielle. |0 (CaQQLa)201-0008626 | |
| 650 | 6 | |a R�eseaux neuronaux (Informatique) |0 (CaQQLa)201-0209597 | |
| 650 | 6 | |a Interfaces cerveau-ordinateur. |0 (CaQQLa)000261628 | |
| 650 | 7 | |a artificial intelligence. |2 aat |0 (CStmoGRI)aat300251574 | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Artificial intelligence |2 fast |0 (OCoLC)fst00817247 | |
| 650 | 7 | |a Brain-computer interfaces |2 fast |0 (OCoLC)fst01742078 | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 (OCoLC)fst01036260 | |
| 700 | 1 | |a Kozma, Robert, |e editor. | |
| 700 | 1 | |a Alippi, Cesare, |e editor. | |
| 700 | 1 | |a Choe, Yoonsuck, |e editor. | |
| 700 | 1 | |a Morabito, F. C. |q (Francesco Carlo), |e editor. | |
| 776 | 0 | 8 | |i Print version: |t Artificial intelligence in the age of neural networks and brain computing. |d London, United Kingdom : Academic Press, an imprint of Elsevier, [2019] |z 0128154802 |z 9780128154809 |w (OCoLC)1013727193 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128154809 |z Texto completo |
| 856 | 4 | 1 | |u https://sciencedirect.uam.elogim.com/book/9780128154809 |z Texto completo |