Statistical and Process Models for Cognitive Neuroscience and Aging
Edited by Michael J. Wenger, Christof Schuster
Psychology Press – 2007 – 358 pages
Statistical and Process Models for Cognitive Neuroscience and Aging addresses methodological techniques for researching cognitive impairment, Alzheimer's disease, the biophysics and structure of the nervous system, the physiology of memory, and the analysis of EEG data. Each chapter, written by the expert in the area, provides a carefully crafted introduction to the subject at hand and the key methodological challenges facing that area of study.
Although the chapters describe sophisticated techniques, each is accessible to scientists from a variety of fields. The editors' goal is to expose researchers working on a range of issues associated with cognitive aging to a variety of approaches and technologies, in an effort to cross disciplinary boundaries and further research in cognitive aging.
Intended for researchers in cognitive, behavioral, and computational neuroscience, psychometrics, gerontology, cognitive, health, and developmental psychology, radiology, and medical research, this book also serves as a text for graduate level courses in cognitive science and cognitive aging.
Contents: M.J. Wenger, C. Schuster, Preface. S.D. Edland, R.C. Petersen, Longitudinal Study in Cognition and Aging. F.G. Ashby, V.V. Valentin, Computational Cognitive Neuroscience: Building and Testing Biologically Plausible Computational Models of Neuroscience, Neuroimaging, and Behavioral Data. M.E. Hasselmo, J. McGaughy, C. Linster, Modeling the Role of Acetylcholine and Hippocampal Theta Rhythm in Memory-Guided Behavior. J.M.J. Murre, M. Meeter, A.G. Chessa, Modeling Amnesia: Connectionist and Mathematical Approaches. A. Keinan, A. Kaufman, C.C. Hilgetag, I. Meilijson, E. Ruppin, Who Does What: Taking Measures. A. Cichocki, Generalized Component Analysis and Blind Source Separation Methods for Analyzing Multichannel Brain Signals. T.C. Ferree, M.A. Kramer, D.J. McGonigle, R.C. Hwa, Quantifying Scaling Properties in Neurophysiological Time Series. T.-P. Jung, T.-W. Lee, Applications of Independent Component Analysis to Electroencephalography.