Description
Book SynopsisThis book shows how to develop efficient quantitative methods to characterize neural data and extra information that reveals underlying dynamics and neurophysiological mechanisms. Written by active experts in the field, it contains an exchange of innovative ideas among researchers at both computational and experimental ends, as well as those at the interface. Authors discuss research challenges and new directions in emerging areas with two goals in mind: to collect recent advances in statistics, signal processing, modeling, and control methods in neuroscience; and to welcome and foster innovative or cross-disciplinary ideas along this line of research and discuss important research issues in neural data analysis. Making use of both tutorial and review materials, this book is written for neural, electrical, and biomedical engineers; computational neuroscientists; statisticians; computer scientists; and clinical engineers.
Trade Review“This is a short monograph on the computational neurosciences of single and populations of neurons. … This serves as a reference for advanced engineers and mathematical neurobiologists primarily. Dayan's Theoretical Neuroscience, and Neural Engineering by MIT Press are useful for more background material. Brain Machine and Brain-Computer interfacing is also considered here.” (Joseph Grenier, Amazon.com, June, 2018)
Table of Contents1. IntroductionPart I Statistics & Signal Processing2 Characterizing Complex, Multi-scale Neural Phenomena Using State-Space Models3 Latent Variable Modeling of Neural Population Dynamics4 What Can Trial-to-Trial Variability Tell Us? A Distribution-Based Approach to Spike Train Decoding in the Rat Hippocampus and Entorhinal Cortex5 Sparsity Meets Dynamics: Robust Solutions to Neuronal Identification and Inverse Problems6 Artifact Rejection for Concurrent TMS-EEG DataPart II Modeling & Control Theory7 Characterizing Complex Human Behaviors and Neural Responses Using Dynamic Models8 Brain-Machine Interfaces9 Control-theoretic Approaches for Modeling, Analyzing and Manipulating Neuronal (In)activity10 From Physiological Signals to Pulsatile Dynamics: A Sparse System Identification Approach11 Neural Engine Hypothesis12 Inferring Neuronal Network Mechanisms Underlying Anesthesia induced Oscillations Using Mathematical ModelsEpilogue
