Analysis of complex neural circuits with nonlinear multidimensional hidden state models
Author(s)
Altshuler, Alex; Sholes, Jacquelyn E. C.; Friedman, Alexander; Slocum, Joshua Foster; Tyulmankov, Danil; Gibb, Leif G.; Ruangwises, Suthee; Shi, Qinru; Toro Arana, Sebastian; Beck, Dirk W.; Graybiel, Ann M; ... Show more Show less
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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.
Date issued
2016-06Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences; McGovern Institute for Brain Research at MITJournal
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences (U.S.)
Citation
Friedman, Alexander et al. “Analysis of Complex Neural Circuits with Nonlinear Multidimensional Hidden State Models.” Proceedings of the National Academy of Sciences 113.23 (2016): 6538–6543. © 2016 National Academy of Sciences
Version: Final published version
ISSN
0027-8424
1091-6490