Correlative Learning: A Basis for Brain and Adaptive Systems provides a bridge between three disciplines: computational neuroscience, neural networks, and signal processing. First, the authors lay down the preliminary neuroscience background for engineers. The book also presents an overview of the role of correlation in the human brain as well as in the adaptive signal processing world; unifies many well-established synaptic adaptations (learning) rules within the correlation-based learning framework, focusing on a particular correlative learning paradigm, ALOPEX; and presents case studies that illustrate how to use different computational tools and ALOPEX to help readers understand certain brain functions or fit specific engineering applications.
Autorentext
Zhe Chen, PhD, is currently a Research Fellow in the
Neuroscience Statistics Research Laboratory at Harvard Medical
School.
Simon Haykin, PhD, DSc, is a Distinguished University Professor
in the Department of Electrical and Computer Engineering at
McMaster University, Ontario, Canada.
Jos J. Eggermont, PhD, is a Professor in the Departments of
Physiology & Biophysics and Psychology at the University of
Calgary, Alberta, Canada.
Suzanna Becker, PhD, is a Professor in the Department of
Psychology, Neuroscience, and Behavior at McMaster University,
Ontario, Canada.
Klappentext
This book bridges the communication gap between neuroscientists and engineers through the unifying theme of correlation-based learning
Developing brain-style signal processing or machine learning algorithms has attracted many sharp minds from a range of disciplines. Now, coauthored by four researchers with varying backgrounds in signal processing, neuroscience, psychology, and computer science, Correlative Learning unifies the many cross-fertilized ideas in computational neuroscience and signal processing in a common language that will help engineers understand and appreciate the human brain as a highly sophisticated biosystem for building more intelligent machines.
First, the authors present the necessary neuroscience background for engineers, and then go on to relate the common intrinsic structures of the learning mechanisms of the brain to signal processing, machine learning, kernel learning, complex-valued domains, and the ALOPEX learning paradigm.
This correlation-based approach to building complex, reliable (robust), and adaptive systems is vital for engineers, researchers, and graduate students from various fields of science and engineering. Figures, tables, worked examples, and case studies illustrate how to use computational tools for either helping to understand brain functions or fitting specific engineering applications, and a comprehensive bibliography covering over 1,000 references from major publications is included for further reading.
Inhalt
Foreword.
Preface.
Acknowledgments.
Acronyms.
Introduction.
1. The Correlative Brain.
1.1 Background.
1.1.1 Spiking Neurons.
1.1.2 Neocortex.
1.1.3 Receptive fields.
1.1.4 Thalamus.
1.1.5 Hippocampus.
1.2 Correlation Detection in Single Neurons.
1.3 Correlation in Ensembles of Neurons: Synchrony and Population Coding.
1.4 Correlation is the Basis of Novelty Detection and Learning.
1.5 Correlation in Sensory Systems: Coding, Perception, and Development.
1.6 Correlation in Memory Systems.
1.7 Correlation in Sensory-Motor Learning.
1.8 Correlation, Feature Binding, and Attention.
1.9 Correlation and Cortical Map Changes after Peripheral Lesions and Brain Stimulation.
1.10 Discussion.
2. Correlation in Signal Processing.
2.1 Correlation and Spectrum Analysis.
2.1.1 Stationary Process.
2.1.2 Non-stationary Process.
2.1.3 Locally Stationary Process.
2.1.4 Cyclostationary Process.
2.1.5 Hilbert Spectrum Analysis.
2.1.6 Higher Order Correlation-based Bispectra Analysis.
2.1.7 Higher Order Functions of Time, Frequency, Lag, and Doppler.
2.1.8 Spectrum Analysis of Random Point Process.
2.2 Wiener Filter.
2.3 Least-Mean-Square Filter.
2.4 Recursive Least-Squares Filter.
2.5 Matched Filter.
2.6 Higher Order Correlation-Based Filtering.
2.7 Correlation Detector.
2.7.1 Coherent Detection.
2.7.2 Correlation Filter for Spatial Target Detection.
2.8 Correlation Method for Time-Delay Estimation.
2.9 Correlation-Based Statistical Analysis.
2.9.1 Principal Component Analysis.
2.9.2 Factor Analysis.
2.9.3 Canonical Correlation Analysis.
2.9.4 Fisher Linear Discriminant Analysis.
2.9.5 Common Spatial Pattern Analysis.
2.10 Discussion.
Appendix: Eigenanalysis of Autocorrelation Function of Nonstationary Process.
Appendix: Estimation of the Intensity and Correlation Functions of Stationary Random Point Process.
Appendix: Derivation of Learning Rules with Quasi-Newton Method.
3. Correlation-Based Neural Learning and Machine Learning.
3.1 Correlation as a Mathematical Basis for Learning.
3.1.1 Hebbian and Anti-Hebbian Rules (Revisited).
3.1.2 Covariance Rule.
3.1.3 Grossberg's Gated Steepest Descent.
3.1.4 Competitive Learning Rule.
3.1.5 BCM Learning Rule.
3.1.6 Local PCA Learning Rule.
3.1.7 Generalizations of PCA Learning.
3.1.8 CCA Learning Rule.
3.1.9 Wake-Sleep Learning Rule for Factor Analysis.
3.1.10 Boltzmann Learning Rule.
3.1.11 Perceptron Rule and Error-Correcting Learning Rule.
3.1.12 Differential Hebbian Rule and Temporal Hebbian Learning.
3.1.13 Temporal Difference and Reinforcement Learning.
3.1.14 General Correlative Learning and Potential Function.
3.2 Information-Theoretic Learning.
3.2.1 Mutual Information vs. Correlation.
3.2.2 Barlow's Postulate.
3.2.3 Hebbian Learning and Maximum Entropy.
3.2.4 Imax Algorithm.
3.2.5 Local Decorrelative Learning.
3.2.6 Blind Source Separation.
3.2.7 Independent Component Analysis.
3.2.8 Slow Feature Analysis.
3.2.9 Energy-Efficient Hebbian Learning.
3.2.10 Discussion.
3.3 Correlation-Based Computational Neural Models.
3.3.1 Correlation Matrix Memory.
3.3.2 Hopfield Network.
3.3.3 Brain-State-in-a-Box Model.
3.3.4 Autoencoder Network.
3.3.5 Novelty Filter.
3.3.6 Neuronal Synchrony and Binding.
3.3.7 Oscillatory Correlation.
3.3.8 Modeling Auditory Functions.
3.3.9 Correlations in the Olfactory System.
3.3.10...