Dynamic Neuroscience

<div>1. Introduction</div><div>Part I Statistics & Signal Processing</div><div>2 Characterizing Complex, Multi-scale Neural Phenomena Using State-Space Models</div><div>3 Latent Variable Modeling of Neural Population Dynamics</div><div>4 What Can Trial-to-Trial Variability Tell Us? A Distribution-Based&nbsp;Approach to Spike Train Decoding in the Rat Hippocampus and&nbsp;Entorhinal Cortex</div><div>5 Sparsity Meets Dynamics: Robust Solutions to Neuronal&nbsp;Identification and Inverse Problems</div><div>6 Artifact Rejection for Concurrent TMS-EEG Data</div><div>Part II Modeling & Control Theory</div><div>7 Characterizing Complex Human Behaviors and Neural Responses&nbsp;Using Dynamic Models</div>8 Brain-Machine Interfaces<div>9 Control-theoretic Approaches for Modeling, Analyzing and&nbsp;Manipulating Neuronal (In)activity</div><div>10 From Physiological Signals to Pulsatile Dynamics: A Sparse System&nbsp;Identification Approach</div><div>11 Neural Engine Hypothesis</div><div>12 Inferring Neuronal Network Mechanisms Underlying Anesthesia induced&nbsp;Oscillations Using Mathematical Models</div><div>Epilogue</div>