目次

1. 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