Analysis of complex neural circuits with nonlinear multidimensional hidden state models

Alexander Friedman, Joshua F Slocum, Danil Tyulmankov, Leif G Gibb, Suthee Ruangwises, Qinru Shi, Alexander Altshuler, Sebastian E Toro Arana, Dirk W Beck, Jacquelyn EC Sholes

Research output: Contribution to journalArticlepeer-review


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
Issue number23
StatePublished - 2016
Externally publishedYes


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

ASJC Scopus subject areas

  • General


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