ABSTRACT

Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required.Time Series Modeling of Neuroscience Data shows how to

chapter 1|14 pages

Introduction

chapter 3|36 pages

Discrete-Time Dynamic Models

chapter 4|44 pages

Multivariate Dynamic Models

chapter 5|32 pages

Continuous-Time Dynamic Models

chapter 6|34 pages

Some More Models

chapter 7|16 pages

Prediction and Doob Decomposition

chapter 8|24 pages

Dynamics and Stationary Distributions

chapter 10|62 pages

Likelihood of Dynamic Models

chapter 11|66 pages

Inference Problem (a) for State Space Models

chapter 12|38 pages

Inference Problem (b) for State Space Models

chapter 13|44 pages

Art of Likelihood Maximization

chapter 14|36 pages

Causality Analysis

chapter 15|4 pages

Conclusion: The New and Old Problems