Includes bibliographical references (p. 387-439) and index.

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 R

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.