An ideal introduction to statistical neurocomputing, this book examines current statistical methods for solving emerging problems in neuroscience. These methods have been applied to data involving multichannel neural spike train, spike sorting, blind source separation, functional and effective neural connectivity, spatiotemporal modeling, and mu
An ideal introduction to statistical neurocomputing, this book examines current statistical methods for solving emerging problems in neuroscience. These methods have been applied to data involving multichannel neural spike train, spike sorting, blind source separation, functional and effective neural connectivity, spatiotemporal modeling, and mu
Young K. Truong, PhD, is a professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill, USA. He earned his BS in mathematics with Baccalaureate Honors at the University of Washington, Seattle, in 1978 and his MA (1980) and PhD (1985) degrees in statistics from the University of California, Berkeley, USA. He has published extensively, is the recipient of many prestigious awards, and is an often-invited professional speaker and presenter.
Inhaltsangabe
STATISTICAL ANALYSIS OF NEURAL SPIKE TRAIN DATA. Statistical Modeling of Neural Spike Train Data. Regression Spline. STATISTICAL ANALYSIS OF FMRI DATA. Hypothesis Testing Approach. An Efficient Estimate of HRF. Independent Component Analysis. Instantaneous Independent Component Analysis. Colored Independent Component Analysis. Group Blind Source Separation (GBSS). Diagnostic Probability Modeling. Supervised SVD. Appendices.
