The Annals of Applied Statistics

Adjusted regularization in latent graphical models: Application to multiple-neuron spike count data

Giuseppe Vinci, Valérie Ventura, Matthew A. Smith, and Robert E. Kass

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Abstract

A major challenge in contemporary neuroscience is to analyze data from large numbers of neurons recorded simultaneously across many experimental replications (trials), where the data are counts of neural firing events, and one of the basic problems is to characterize the dependence structure among such multivariate counts. Methods of estimating high-dimensional covariation based on $\ell_{1}$-regularization are most appropriate when there are a small number of relatively large partial correlations, but in neural data there are often large numbers of relatively small partial correlations. Furthermore, the variation across trials is often confounded by Poisson-like variation within trials. To overcome these problems we introduce a comprehensive methodology that imbeds a Gaussian graphical model into a hierarchical structure: the counts are assumed Poisson, conditionally on latent variables that follow a Gaussian graphical model, and the graphical model parameters, in turn, are assumed to depend on physiologically-motivated covariates, which can greatly improve correct detection of interactions (nonzero partial correlations). We develop a Bayesian approach to fitting this covariate-adjusted generalized graphical model and we demonstrate its success in simulation studies. We then apply it to data from an experiment on visual attention, where we assess functional interactions between neurons recorded from two brain areas.

Article information

Source
Ann. Appl. Stat., Volume 12, Number 2 (2018), 1068-1095.

Dates
Received: December 2017
Revised: June 2018
First available in Project Euclid: 28 July 2018

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1532743486

Digital Object Identifier
doi:10.1214/18-AOAS1190

Keywords
Bayesian inference Gaussian graphical models Gaussian scale mixture high dimensionality lasso latent variable models macaque prefrontal cortex macaque visual cortex Poisson-lognormal sparsity spike-counts

Citation

Vinci, Giuseppe; Ventura, Valérie; Smith, Matthew A.; Kass, Robert E. Adjusted regularization in latent graphical models: Application to multiple-neuron spike count data. Ann. Appl. Stat. 12 (2018), no. 2, 1068--1095. doi:10.1214/18-AOAS1190. https://projecteuclid.org/euclid.aoas/1532743486


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Supplemental materials

  • Supplement to “Adjusted regularization in latent graphical models: Application to multiple-neuron spike count data.”. Appendix containing: Appendix A additional details about adjusted regularization and simulations, Appendix B lemmas with proofs, Appendix C algorithms, and Appendix D additional data analyses.