1 Introduction
1.1 Systems Biology andIts Objective
1.2 Biological Feedback Loops
1.3 Identification Methods ofBiological Feedback Loops
1.4 0utline ofthe Book
2 Non-causallmpulse Response Component Methods
2.1 Basic Concepts about the Stochastic Process
2.2 Correlation Identification Methods
2.3 SpectralFactorAnalysis
2.4 Identification Algorithm of the NIRCM
2.5 Perturbation Methods
2.6 Factors Affecting the Identification Precision
3 Multi-step Granger Causality Methods
3.1 Multivariate Time-Series Analysis
3.2 Finite-order Vector Autoregressive Model and Its
Corresponding Infinite-order Vector Moving Average Model
3.3 Estimation ofVAR Coefficients
3.4 Granger Causality and Multi-step Causality.
3.4.1 Granger Causalitylnference Between the 2-partitioned
Variate Sets
3.4.2 Granger Causality Between a Pair of Variate Sets
3.4.3 Testing Multi-step Granger Causality Between a Pair of
Variate Sets
3.5 Identification Algorithm of the MSGCM
4 Synthetic Spike NeuraI Networks and Their Dynamical Network
Behaviors
4.1 Spike Neural Networks
4.2 Typical Network Behaviors
4.3 Synchronized Bursting Behavior and Feedback Mechanism
4.4 Feedback Motifs ofNetworks
4.5 Dynamical Characteristics of Network Motifs
5 Application of Feedback Loop Identification Methods to
Synthetic Spike Neural Networks
6 Feedback Loop Identifications for Biological Cultured Neural
Networks
7 Summary
Appendix
Bibliography
