Dynamic Neuroscience: Statistics, Modeling, and Control (Paperback, Softcover Repri)

1. Introduction

Part I Statistics & Signal Processing

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

3 Latent Variable Modeling of Neural Population Dynamics

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

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

6 Artifact Rejection for Concurrent TMS-EEG Data

Part II Modeling & Control Theory

7 Characterizing Complex Human Behaviors and Neural Responses Using Dynamic Models

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

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

11 Neural Engine Hypothesis

12 Inferring Neuronal Network Mechanisms Underlying Anesthesia induced Oscillations Using Mathematical Models

Epilogue

Zhe Chen (엮은이)    정보 더보기

Sridevi V. Sarma (엮은이)    정보 더보기

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