商品簡介
Despite these advantages，traditional feedback identification theories often suffer from the opinion that they usually address two-variate time-series data and are inappropriate for large-scale networks because of their practical and theoretical limitations.Data acquisition is difficult or connective entanglements are fearing，which might hinder their applications to very large datasets， as occur more and more frequently nowadays. Now this is not the case，as many new experimental techniques， for example，real-time PCR， immunofluorescence，microarray，multi-electrode array and EEG;can now provide such time-series data in a cost efficient manner. Also a multi-variate time-series analysis theory has undergone a great development.The new theoretical contribution much helps to find the feedback loops in large-scale networks.
作者簡介
名人/編輯推薦
目次
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
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
書摘/試閱
Figure 4-2 (a) The kernel εij(t) describing the response of xi(t)caused by a pre-synaptic spike at t = 0. △ax= 50 ms, τs = 3.5 ms, and rm =8ms. (b) The function φ(t) reflecting refractoriness after a spike emitted at t=0 . η=0.1 and τ=40ms. (c) The kernel ei(t) representing the dynamics from the local current stimulation to the membrane voltage of a neuron. The time constant is identical to τm in (a). (d) The membrane voltage xi(t) firing at time tki when it reaches a threshold voltage r/=0.1. After firing, it is reset by the function ψi(t) and then re-accumulated by the pre-synaptic spike inputs wijεij(t-tkj).
Figure 5-9 Applying the MSGCM to the network only with one unidirectional excitatory connection. (a) The procedures of determining Popt. The VAR models are fitted until p = 20. The minimum values of HQ is found at (7, -2.735,4). (b) All estimated parameters of the chosen VAR(7) model. The first two points are the estimated values of mean μ. The following 28 points are the estimated vectors α. The horizontal dash lines indicate 3std confidence interval. The values out of this region are considered to be significant.
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