Analysis of complex neural circuits with nonlinear multidimensional hidden state models

Alexander Friedman, Joshua F. Slocum, Danil Tyulmankov, Leif G. Gi, Alex Altshuler, Suthee Ruangwises, Qinru Shi, Sebastian E.Toro Arana, Dirk W. Beck, Jacquelyn E.C. Sholes, Ann M. Graybiel

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear interactions remains an unmet challenge. We present a nonlinear multidimensional hidden state (NMHS) approach that achieves interaction strength analysis and decoding of networks with nonlinear interactions by including latent state variables for each node in the network. We compare NMHS to Granger causality in analyzing neural circuit recordings and simulations, improvised music, and sociodemographic data. We conclude that NMHS significantly extends the scope of analyses of multidimensional, nonlinear networks, notably in coping with the complexity of the brain.

Original languageEnglish
Pages (from-to)6538-6543
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume113
Issue number23
DOIs
StatePublished - 7 Jun 2016
Externally publishedYes

Bibliographical note

Funding Information:
We thank Daniel Gibson and Yasuo Kubota for their help in many aspects of this work. This work was supported by National Institutes of Health Grant R01 MH060379, the Defense Advanced Research Project Agency and US Army Research Office Grant W911NF-10-1-0059, and the Saks Kavanaugh Foundation.

Funding

We thank Daniel Gibson and Yasuo Kubota for their help in many aspects of this work. This work was supported by National Institutes of Health Grant R01 MH060379, the Defense Advanced Research Project Agency and US Army Research Office Grant W911NF-10-1-0059, and the Saks Kavanaugh Foundation.

FundersFunder number
Saks Kavanaugh Foundation
National Institutes of Health
National Institute of Mental HealthR01MH060379
Army Research OfficeW911NF-10-1-0059
Defense Advanced Research Projects Agency

    Keywords

    • Causal analysis
    • Decoding
    • Functional connectivity
    • Hidden Markov models
    • Machine learning

    Fingerprint

    Dive into the research topics of 'Analysis of complex neural circuits with nonlinear multidimensional hidden state models'. Together they form a unique fingerprint.

    Cite this