Granger causality-based synaptic weights estimation for analyzing neuronal networks

DOI10.1007/s10827-015-0550-zzbMath1382.92086OpenAlexW2138674902WikidataQ41243365 ScholiaQ41243365MaRDI QIDQ1704884

Pei-Chiang Shao, Meng-Li Tsai, Chen-Tung Yen, Jian-Jia Huang, Chien-Chang Yen, Wei-Chang Shann

Publication date: 13 March 2018

Published in: Journal of Computational Neuroscience (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10827-015-0550-z

zbMATH Keywords

neuronal networksvector autoregressive modelGranger causality analysissynaptic weights estimation

Mathematics Subject Classification ID

Neural biology (92C20) Neural networks for/in biological studies, artificial life and related topics (92B20) Systems biology, networks (92C42)

Related Items (1)

Evaluation of connectivity estimates using spiking neuronal network models

Uses Software

• GCCA
• MVGC

Cites Work

• Effects of spike sorting error on the Granger causality index
• Partial directed coherence: a new concept in neural structure determination
• Autoregressive models for gene regulatory network inference: sparsity, stability and causality issues
• Pooled spike trains of correlated presynaptic inputs as realizations of cluster point processes
• Distinguishing Causal Interactions in Neural Populations
• Introduction to Time Series Modeling
• Decoding Poisson Spike Trains by Gaussian Filtering
• A Method for Selecting the Bin Size of a Time Histogram
• Probing Changes in Neural Interaction During Adaptation
• Investigating Causal Relations by Econometric Models and Cross-spectral Methods
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