Introduction
Characterizing Complex, Multi-Scale Neural Phenomena Using State-Space Models
Latent Variable Modeling of Neural Population Dynamics
What Can Trial-to-Trial Variability Tell Us? A Distribution-Based Approach to Spike Train Decoding in the Rat Hippocampus and Entorhinal Cortex
Sparsity Meets Dynamics: Robust Solutions to Neuronal Identification and Inverse Problems
Artifact Rejection for Concurrent TMS-EEG Data
Characterizing Complex Human Behaviors and Neural Responses Using Dynamic Models
Brain-Machine Interfaces
Control-Theoretic Approaches for Modeling, Analyzing, and Manipulating Neuronal (In)activity
From Physiological Signals to Pulsatile Dynamics: A Sparse System Identification Approach
Neural Engine Hypothesis
Inferring Neuronal Network Mechanisms Underlying Anesthesia-Induced Oscillations Using Mathematical Models
Epilogue.