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Artificial intelligence in the age of neural networks and brain computing /

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Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel proces...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Kozma, Robert (Editor), Alippi, Cesare (Editor), Choe, Yoonsuck (Editor), Morabito, F. C. (Francesco Carlo) (Editor)
Format: eBook
Language:English
Published: London, United Kingdom ; San Diego, CA : Academic Press ia an imprint of Elsevier, [2024]
Edition:Second edition.
Subjects:
Artificial intelligence.
Neural networks (Computer science)
Brain-computer interfaces.
Intelligence artificielle.
Réseaux neuronaux (Informatique)
Interfaces cerveau-ordinateur.
artificial intelligence.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Artificial Intelligence in the Age of Neural Networks and Brain Computing
  • Copyright
  • Contents
  • Contributors
  • Editors' brief biographies
  • Introduction
  • Chapter 1: Advances in AI, neural networks, and brain computing: An introduction
  • 1. Introduction
  • Part 1: Fundamentals of neural networks and brain computing
  • Chapter 2: Nature's learning rule: The Hebbian-LMS algorithm
  • Chapter outlines
  • 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
  • Chapter postscript
  • Acknowledgments
  • References
  • Further reading
  • Chapter 3: A half century of progress toward a unified neural theory of mind and brain with applications to autonomous ad ...
  • Chapter outlines
  • 1. Toward 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. Toward autonomous adaptive intelligent agents and clinical therapies in society
  • References
  • Chapter 4: Meaning versus information, prediction versus memory, and question versus answer
  • Chapter outlines
  • 1. Introduction
  • 2. Meaning vs information
  • 3. Prediction vs memory
  • 4. Question vs answer
  • 5. Discussion
  • 6. Conclusion
  • Acknowledgments
  • References
  • Chapter 5: The brain-mind-computer trichotomy: Hermeneutic approach
  • Chapter outlines
  • 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
  • Part 2: Brain-inspired AI systems
  • Chapter 6: The new AI: Basic concepts, and urgent risks and opportunities in the internet of things
  • Chapter outlines
  • 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, &gt
  • 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
  • 6. From deep learning to the future: How to build and use true quantum artificial general intelligence
  • 6.1. The deep learning revolution
  • 6.2. From COPN to mammal level artificial general intelligence AGI
  • 6.3. From classical artificial general intelligence to QAGI
  • Appendix: Neural network revolutions, past and future: From BP (neural and fuzzy) to quantum artificial general intelligenc ...
  • References
  • Chapter 7: Computers versus brains: Challenges of sustainable artificial and biological intelligence
  • Chapter outlines
  • 1. The dream of creating artificial intelligence
  • 1.1. From ancient Greeks to digital computers.
  • 1.2. The birth of artificial intelligence and machine intelligence
  • 1.3. New AI with super-human performance, and the deep learning frontier
  • 2. What can AI learn from biological intelligence (BI)?
  • 2.1. The brain-computer metaphor
  • 2.2. Neural networks and deep learning
  • 3. Always at the edge-The miracle of life and intelligence
  • 3.1. Multistability in physics and biology
  • 3.2. Metastability in cognition and brain dynamics
  • 3.3. Metastability and the complementarity principle
  • 4. Implementation of complementarity for new AI
  • 4.1. Manifestation of complementarity in brains through intermittent local-global spatial patterns
  • 4.2. Brain-inspired integration of top-down and bottom-up AI
  • 5. Conclusion: Sustainable AI to support humanity
  • Acknowledgments
  • References
  • Chapter 8: Brain-inspired evolving and spiking connectionist systems
  • Chapter outlines
  • 1. The historic merge of artificial neural networks and fuzzy logic
  • 2. Evolving connectionist systems (ECOS)
  • 2.1. Principles of ECOS
  • 2.2. EFuNN and DENFIS
  • 2.3. Incremental and life-long learning in EFuNN and DENFIS
  • 2.4. Other ECOS realizations and ECOS applications
  • 3. Spiking neural networks (SNNs)
  • 3.1. Main principles, methods and examples of SNN and evolving SNN (eSNN)
  • 3.2. Applications and hardware implementations of SNN and eSNN
  • 4. Brain-inspired SNN. NeuCube
  • 4.1. Life-long learning in the brain
  • 4.2. Brain-inspired SNN architectures. NeuCube
  • 4.3. Developing application-oriented SNN systems for life-long learning using NeuCube
  • 5. What role can evolutionary computation (EC) methods play for life-long learning in evolving connectionist systems (ECOS)?
  • 6. Conclusion
  • Acknowledgment
  • References
  • Chapter 9: Pitfalls and opportunities in the development and evaluation of artificial intelligence systems
  • Chapter outlines.
  • 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
  • Chapter 10: Theory of the brain and mind: Visions and history
  • Chapter outlines
  • 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
  • Chapter 11: From synapses to ephapsis: Embodied cognition and wearable personal assistants
  • Chapter outlines
  • 1. Neural networks and neural fields
  • 2. Ephapsis
  • 3. Embodied cognition
  • 4. Wearable personal assistants
  • References
  • Part 3: Cutting-edge developments in deep learning and intelligent systems
  • Chapter 12: Explainable deep learning to information extraction in diagnostics and electrophysiological multivariate time ...
  • Chapter outlines
  • 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 auto-encoders
  • 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 Explainability/interpretability
  • 6.1.1. explainable artificial intelligence (xAI)
  • 6.1.2. Occlusion sensitivity analysis.