Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method.

• Publication year

  2010

• Venue

  Computational Intelligence and Neuroscience

• Publication date

  2010-04-29

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1155/2010/752428PMID 20454705PMCID 2862319
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• Generating Spike Trains with Specified Correlation Coefficients

  2009cited by this paper
• Generation of Spike Trains with Controlled Auto- and Cross-Correlation Functions

  2009influential reference
• Correlation-distortion based identification of Linear-Nonlinear-Poisson models

  2009cited by this paper
• Quantifying Statistical Interdependence by Message Passing on Graphs—Part I: One-Dimensional Point Processes

  2009cited by this paper
• Analyzing multiple spike trains with nonparametric granger causality

  2009cited by this paper
• Detecting synfire chain activity using massively parallel spike train recording.

  2008cited by this paper
• Correlations and synchrony in threshold neuron models.

  2008cited by this paper
• Spatio-temporal correlations and visual signalling in a complete neuronal population

  2008cited by this paper
• Patterned Optical Activation of Retinal Ganglion Cells

  2007cited by this paper
• In vivo calcium imaging of neural network function.

  2007cited by this paper
• Comparison of different cortical connectivity estimators for high‐resolution EEG recordings

  2007cited by this paper
• Polychronization: Computation with Spikes

  2006cited by this paper
• Linear and nonlinear causality between signals: methods, examples and neurophysiological applications

  2006cited by this paper
• Granger Causality: Basic Theory and Application to Neuroscience

  2006influential reference
• Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity

  2005cited by this paper
• Spike train metrics.

  2005cited by this paper
• A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.

  2005cited by this paper
• Nonlinear multivariate analysis of neurophysiological signals.

  2005cited by this paper
• Rapid neurotransmitter uncaging in spatially defined patterns

  2005cited by this paper
• Superlinear Population Encoding of Dynamic Hand Trajectory in Primary Motor Cortex

  2004cited by this paper
• Multiple neural spike train data analysis: state-of-the-art and future challenges

  2004cited by this paper
• ModelDB: A Database to Support Computational Neuroscience

  2004cited by this paper
• Simple model of spiking neurons

  2003cited by this paper
• Amplitude and frequency dependence of spike timing: implications for dynamic regulation.

  2003cited by this paper
• Probing Changes in Neural Interaction During Adaptation

  2003cited by this paper
• Analysis of higher-order neuronal interactions based on conditional inference

  2003cited by this paper
• A new correlation-based measure of spike timing reliability

  2003cited by this paper
• Maximum likelihood identification of neural point process systems

  2002cited by this paper
• Information-Geometric Measure for Neural Spikes

  2002cited by this paper
• A Novel Spike Distance

  2001cited by this paper
• Thalamic bursting in rats during different awake behavioral states

  2001cited by this paper
• Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance

  2001cited by this paper
• Neural Coding: Higher-Order Temporal Patterns in the Neurostatistics of Cell Assemblies

  2000cited by this paper
• Using partial directed coherence to describe neuronal ensemble interactions.

  1999cited by this paper
• Metric-space analysis of spike trains: theory, algorithms and application

  1998cited by this paper
• Nerve Cell Spike Train Data Analysis: A Progression of Technique

  1992cited by this paper
• Identification of connectivity in neural networks.

  1990cited by this paper
• Maximum likelihood identification of neural point process systems

  1988cited by this paper
• Maximum likelihood estimations in a nonlinear self-exciting point process model

  1986cited by this paper
• Maximum likelihood estimations in a nonlinear self-exciting point process model.

  1986cited by this paper
• A new statistical method for identifying interconnections between neuronal network elements

  1985cited by this paper
• Signal transmission in random spike trains with applications to the statocyst neurons of the lobster

  1970cited by this paper
• Investigating causal relations by econometric models and cross-spectral methods

  1969cited by this paper
• On the fitting of multivariate autoregressions, and the approximate canonical factorization of a spectral density matrix

  1963cited by this paper

• Intracranial directed connectivity links subregions of the prefrontal cortex to major depression

  2025cites this paper
• Intracranial Directed Connectivity Links Subregions of the Prefrontal Cortex to Major Depression

  2024cites this paper
• Causality relations and causality direction of shallots price changes in east java province, Indonesia

  2024cites this paper
• Modeling multiscale causal interactions between spiking and field potential signals during behavior

  2022cites this paper
• Granger Causality for Compressively Sensed Sparse Signals

  2022cites this paper
• Granger Causal Inference from Spiking Observations via Latent Variable Modeling

  2022cites this paper
• Subbotin graphical models for extreme value dependencies with applications to functional neuronal connectivity

  2021cites this paper
• Fuzzy-based Description of Computational Complexity of Central Nervous Systems

  2020cites this paper
• An EEG channel selection method for motor imagery based brain-computer interface and neurofeedback using Granger causality

  2020cites this paper
• Estimating Multiscale Direct Causality Graphs in Neural Spike-Field Networks

  2019cites this paper
• Spike-Field Granger Causality for Hybrid Neural Data Analysis.

  2019cites this paper
• A Review on EEG-Based Automatic Sleepiness Detection Systems for Driver

  2018cites this paper
• Granger Causality to Reveal Functional Connectivity in the Mouse Basal Ganglia-Thalamocortical Circuit

  2018cites this paper
• Exact Partial Information Decompositions for Gaussian Systems Based on Dependency Constraints

  2018cites this paper
• Designing Patient-Specific Optimal Neurostimulation Patterns for Seizure Suppression

  2018cites this paper
• A Copula-Based Granger Causality Measure for the Analysis of Neural Spike Train Data

  2018cites this paper
• Extracting neuronal functional network dynamics via adaptive Granger causality analysis

  2018cites this paper
• Data-driven perception of neuron point process with unknown unknowns

  2018cites this paper
• Lasso Guarantees for Time Series Estimation under Subgaussian Tails and -Mixing

  2017cites this paper
• 神經元網路訊息的因果分析;Causal Connectivity Analysis for Identifying the Information Flow of Neuronal Networks

  2017cites this paper
• Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains.

  2017cites this paper
• On neural encoding: its estimation, application, and development

  2017cites this paper
• Causality of Spike Trains Based on Entropy

  2016cites this paper
• Using multivariate cross correlations, Granger causality and graphical models to quantify spatiotemporal synchronization and causality between pest populations

  2016cites this paper
• Granger causality-based synaptic weights estimation for analyzing neuronal networks

  2015cites this paper
• Copula Regression Analysis of Simultaneously Recorded Frontal Eye Field and Inferotemporal Spiking Activity during Object-Based Working Memory

  2015cites this paper
• Discovering Functional Neuronal Connectivity from Serial Patterns in Spike Train Data

  2014cites this paper
• Discovering Neuronal Connectivity from Serial Patterns in Spike Train Data

  2014cites this paper
• EEG Spatial Decoding and Classification with Logit Shrinkage Regularized Directed Information Assessment (L-SODA)

  2014influential citation
• Granger causality analysis reveals distinct spatio-temporal connectivity patterns in motor and perceptual visuo-spatial working memory

  2014cites this paper
• Pattern identification of movement related states in biosignals

  2013cites this paper
• Methods toward improved lower extremity rehabilitation

  2012cites this paper
• Multimodal Video Indexing and Retrieval Using Directed Information

  2012cites this paper
• nSTAT: open-source neural spike train analysis toolbox for Matlab.

  2012cites this paper
• A neural correlate to learning decision and control using functional synaptic efficacy

  2012cites this paper
• Characterization of the causality between spike trains with permutation conditional mutual information.

  2011cites this paper
• Inference About Functional Connectivity From Multiple Neural Spike Trains

  2011cites this paper
• A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity

  2011cites this paper
• Correlation-Based Analysis and Generation of Multiple Spike Trains Using Hawkes Models with an Exogenous Input

  2010influential citation
• Correlation-based approach to analysis of spiking networks

  2010cites this paper
• Generating Spike Trains with Specified Correlation Coefficients

  2009cites this paper
• Generation of Spike Trains with Controlled Auto- and Cross-Correlation Functions

  2009cites this paper
• Quantifying Statistical Interdependence by Message Passing on Graphs—Part I: One-Dimensional Point Processes

  2009cites this paper