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Neural Additive Vector Autoregression Models for Causal Discovery in Time Series Data [article]

Bart Bussmann, Jannes Nys, Steven Latré
2020 arXiv   pre-print
We train deep neural networks that extract the (additive) Granger causal influences from the time evolution in multi-variate time series.  ...  We propose Neural Additive Vector Autoregression (NAVAR) models, a neural approach to causal structure learning that can discover nonlinear relationships.  ...  We model these nonlinear functions using neural networks. In comparison to other works using Granger causality for causal discovery in time series, our work differs in the following ways: 1.  ... 
arXiv:2010.09429v1 fatcat:awm6mc365vghbo4sfkj3pjujqy

Surrogate-based test for Granger causality

T. Gautama, M.M. Van Hulle
2003 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)  
The proposed approach uses the surrogate data method, and implements the self-and crossprediction systems as feedforward neural networks.  ...  A novel approach for testing the presence of Granger causality between two time series is proposed.  ...  Self-Prediction The self-prediction system is implemented as a feedforward neural network with a single hidden layer of three neurons.  ... 
doi:10.1109/nnsp.2003.1318079 dblp:conf/nnsp/GautamaH03 fatcat:dqjudakh5jbfzf4evqe4ikcoiu

Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning [article]

Wenjie Shi, Gao Huang, Shiji Song, Cheng Wu
2021 arXiv   pre-print
TSCI model is applicable to recurrent agents and can be used to discover causal features with high efficiency once trained.  ...  To address this problem, we present a temporal-spatial causal interpretation (TSCI) model to understand the agent's long-term behavior, which is essential for sequential decision-making.  ...  non-temporal variant of Granger causality for model interpretation • G.  ... 
arXiv:2112.03020v1 fatcat:qdypudi6djcanelgijzh3ifkqq

Analysing connectivity with Granger causality and dynamic causal modelling

Karl Friston, Rosalyn Moran, Anil K Seth
2013 Current Opinion in Neurobiology  
We focus on detecting and estimating directed connectivity in neuronal networks using Granger causality (GC) and dynamic causal modelling (DCM).  ...  GC and DCM have distinct and complementary ambitions that are usefully considered in relation to the detection of functional connectivity and the identification of models of effective connectivity.  ...  that could explain spectral differences between healthy human Figure 1 1 Figure 1 Granger causality (GC) is first applied to fMRI time-series using the method of 'Granger causality mapping' (GCM) from  ... 
doi:10.1016/j.conb.2012.11.010 pmid:23265964 pmcid:PMC3925802 fatcat:nwo47mva5ndo7meusc7snjj2m4

Causal interactions in resting-state networks predict perceived loneliness

Yin Tian, Li Yang, Sifan Chen, Daqing Guo, Zechao Ding, Kin Yip Tam, Dezhong Yao, Xi-Nian Zuo
2017 PLoS ONE  
Using conditional granger causal analysis of resting-state fMRI data, we revealed that the weaker causal flow from DAN to VAN is related to higher loneliness scores, and the decreased causal flow from  ...  Our results clearly support the hypothesis that there is a connection between loneliness and neural networks.  ...  Granger causality and statistical analysis Granger causal interactions between neural networks were calculated for lonely and non-lonely groups and displayed graphically with respect to the mean group  ... 
doi:10.1371/journal.pone.0177443 pmid:28545125 pmcid:PMC5436685 fatcat:rwdil4y3nnftzgqc4llfrx5d7e

Learning interaction rules from multi-animal trajectories via augmented behavioral models [article]

Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu (+1 others)
2021 arXiv   pre-print
We adopt an approach for augmenting incomplete multi-agent behavioral models described by time-varying dynamical systems with neural networks.  ...  In this paper, we propose a new framework for learning Granger causality from multi-animal trajectories via augmented theory-based behavioral models with interpretable data-driven models.  ...  For obtaining flies data, we would like to thank Ryota Nishimura at Nagoya University.  ... 
arXiv:2107.05326v3 fatcat:yxj3vbgmivc4jggw5cersb2mnm

Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone

Felipe Gerhard, Tilman Kispersky, Gabrielle J. Gutierrez, Eve Marder, Mark Kramer, Uri Eden, Olaf Sporns
2013 PLoS Computational Biology  
In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities  ...  Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics.  ...  Acknowledgments The authors thank Anatoly Rinberg for supplying the CsCl data and Kyle Lepage for useful comments on the manuscript. Author Contributions  ... 
doi:10.1371/journal.pcbi.1003138 pmid:23874181 pmcid:PMC3708849 fatcat:qhxdyobjszhorjbxd4s465jwoe

Dissociated Emergent-Response System and Fine-Processing System in Human Neural Network and a Heuristic Neural Architecture for Autonomous Humanoid Robots

Xiaodan Yan
2010 Computational Intelligence and Neuroscience  
Correlation and Granger causality analyses were utilized to reveal the functional connectivity patterns.  ...  The current study investigated the functional connectivity of the primary sensory system with resting state fMRI and applied such knowledge into the design of the neural architecture of autonomous humanoid  ...  Donald Wilson at the New York University for their encouragement on the current study, and Helen Smith for her help with English proof, as well as the anonymous reviewers whose insightful and constructive  ... 
doi:10.1155/2010/314932 pmid:21331371 pmcid:PMC3038559 fatcat:imyq7mgwtvh6bpfiljq36yvd5u

New Levels of Language Processing Complexity and Organization Revealed by Granger Causation

David W. Gow, David N. Caplan
2012 Frontiers in Psychology  
Premised on the observation that causes both precede and uniquely predict their effects, this approach provides an intuitive, model-free means of identifying directed causal interactions in the brain.  ...  Granger causation analysis of high spatiotemporal resolution reconstructions of brain activation offers a new window on the dynamic interactions between brain areas that support language processing.  ...  We would like to thank Conrad Nied and Reid Vancelette for their assistance in preparing the manuscript.  ... 
doi:10.3389/fpsyg.2012.00506 pmid:23293611 pmcid:PMC3536267 fatcat:vyanyt3fdjbutp5z7meikeflg4

Top-Down Network Effective Connectivity in Abstinent Substance Dependent Individuals

Michael F. Regner, Naomi Saenz, Keeran Maharajh, Dorothy J. Yamamoto, Brianne Mohl, Korey Wylie, Jason Tregellas, Jody Tanabe, Emmanuel Andreas Stamatakis
2016 PLoS ONE  
The number, direction, and strength of connections between NOI were analyzed with Granger Causality. Within-group thresholds were p<0.005 using a bootstrap permutation.  ...  Results Compared to controls, SDI showed significantly greater Granger causal connectivity from right executive control network (RECN) to dorsal default mode network (dDMN) and from dDMN to basal ganglia  ...  Wen and colleagues [60] demonstrated that fMRI-based Granger causality is a monotonic function of neural Granger causality.  ... 
doi:10.1371/journal.pone.0164818 pmid:27776135 pmcid:PMC5077096 fatcat:7e7jkxf4z5favhgtixqjto3hya

Detection of object motion during self-motion: psychophysics and neuronal substrate

F. Calabro, L.-M. Vaina
2011 Journal of Vision  
Sporns, Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 2010. 52(3): p. 1059-69. 5. Seth, A.K., A MATLAB toolbox for Granger causal connectivity analysis.  ...  Brain Networks To determine how ROIs interact in this psychophysical task, we computed connectivity among functionally defined areas using multivariate Granger causality.  ... 
doi:10.1167/11.11.722 fatcat:u76nvbd5s5borduqvytg4ynyfi

Identifying Neural Drivers with Functional MRI: An Electrophysiological Validation

Olivier David, Isabelle Guillemain, Sandrine Saillet, Sebastien Reyt, Colin Deransart, Christoph Segebarth, Antoine Depaulis, Pedro Valdes-Sosa
2008 PLoS Biology  
a measure of Granger causality and Dynamic Causal Modelling that relates synaptic activity to fMRI. fMRI connectivity was compared to directed functional coupling estimated from iEEG using asymmetry in  ...  As such, it has important implications for future studies on brain connectivity using functional neuroimaging.  ...  Acknowledgments We are very grateful to Karl Friston for improvements suggested on an early draft of this manuscript. We thank Guerbet Research for providing us with Sinerem. Author contributions.  ... 
doi:10.1371/journal.pbio.0060315 pmid:19108604 pmcid:PMC2605917 fatcat:vdkdknb3bjeezpucvekh3nbjni

A procedure to increase the power of Granger-causal analysis through temporal smoothing

E. Spencer, L.-E. Martinet, E.N. Eskandar, C.J. Chu, E.D. Kolaczyk, S.S. Cash, U.T. Eden, M.A. Kramer
2018 Journal of Neuroscience Methods  
Highlights • Modification of multivariate Granger causality for conditional inference on large network data • Model with interpretable parameters for signals with extended, smooth history dependencies  ...  Figure 2: Illustration of model coefficients for network simulations. Plots of example functions used for history dependence in the nine-node simulations.  ...  In conclusion, the spline-Granger method provides a flexible and useful tool for network inference of large models.  ... 
doi:10.1016/j.jneumeth.2018.07.010 pmid:30031776 pmcid:PMC6200653 fatcat:clzus7t5p5birmu7cgtbgljadi

Confounding Effects of Phase Delays on Causality Estimation

Vasily A. Vakorin, Bratislav Mišić, Olga Krakovska, Gleb Bezgin, Anthony R. McIntosh, Pedro Antonio Valdes-Sosa
2013 PLoS ONE  
To demonstrate this, we used a prototypical model of coupled non-linear systems, and compared three typical pipelines of inferring Granger causality, as established in the literature.  ...  Specifically, we compared the performance of the spectral and information-theoretic Granger pipelines as well as standard Granger causality in their relations to the observed phase differences for frequencies  ...  Acknowledgments We thank Maria Tassopoulos and Tanya Brown for their assistance in preparing this manuscript. Author Contributions  ... 
doi:10.1371/journal.pone.0053588 pmid:23349720 pmcid:PMC3549927 fatcat:7cz5akgor5ehzdcwsehvlkiwv4

CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods [article]

Wei Zhang, Thomas Kobber Panum, Somesh Jha, Prasad Chalasani, and David Page
2020 arXiv   pre-print
Existing work suffers from either limited model flexibility or poor model explainability and thus fails to uncover Granger causality across a wide variety of event sequences with diverse event interdependency  ...  The key idea of CAUSE is to first implicitly capture the underlying event interdependency by fitting a neural point process, and then extract from the process a Granger causality statistic using an axiomatic  ...  for obtaining Granger causality from multi-type event sequences using information captured by a highly predictive NPP model.  ... 
arXiv:2002.07906v1 fatcat:rb7f4bidmzhtdmad3bekhbkkk4
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