Chapter 1 Introduction

Chapter 2 Characterizing Complex, Multi-Scale Neural Phenomena Using State-Space Models

Chapter 4 What Can Trial-to-Trial Variability Tell Us? A Distribution-Based Approach to Spike Train Decoding in the Rat Hippocampus and Entorhinal Cortex

Chapter 5 Sparsity Meets Dynamics: Robust Solutions to Neuronal Identification and Inverse Problems

Chapter 6 Artifact Rejection for Concurrent TMS-EEG Data

Chapter 7 Characterizing Complex Human Behaviors and Neural Responses Using Dynamic Models

Chapter 8 Brain–Machine Interfaces

Chapter 9 Control-Theoretic Approaches for Modeling, Analyzing, and Manipulating Neuronal (In)activity

Chapter 10 From Physiological Signals to Pulsatile Dynamics: A Sparse System Identification Approach

Chapter 11 Neural Engine Hypothesis

Chapter 12 Inferring Neuronal Network Mechanisms Underlying Anesthesia-Induced Oscillations Using Mathematical Models

Chapter 13 Retraction Note to: Artifact Rejection for Concurrent TMS-EEG Data