Analysis of complex neural circuits with nonlinear multidimensional hidden state models.
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ABSTRACT:
SUBMITTER: Friedman A
PROVIDER: S-EPMC4988606 | biostudies-literature | 2016 Jun
REPOSITORIES: biostudies-literature
Publications
Analysis of complex neural circuits with nonlinear multidimensional hidden state models.
Friedman Alexander A Slocum Alanna F AF Tyulmankov Danil D Gibb Leif G LG Altshuler Alex A Ruangwises Suthee S Shi Qinru Q Toro Arana Sebastian E SE Beck Dirk W DW Sholes Jacquelyn E C JE Graybiel Ann M AM
Proceedings of the National Academy of Sciences of the United States of America 20160524 23
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) appr ...[more]
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