Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI.

ABSTRACT: A major tenet in theoretical neuroscience is that cognitive and behavioral processes are ultimately implemented in terms of the neural system dynamics. Accordingly, a major aim for the analysis of neurophysiological measurements should lie in the identification of the computational dynamics underlying task processing. Here we advance a state space model (SSM) based on generative piecewise-linear recurrent neural networks (PLRNN) to assess dynamics from neuroimaging data. In contrast to many other nonlinear time series models which have been proposed for reconstructing latent dynamics, our model is easily interpretable in neural terms, amenable to systematic dynamical systems analysis of the resulting set of equations, and can straightforwardly be transformed into an equivalent continuous-t

SUBMITTER: Koppe G 

PROVIDER: S-EPMC6719895 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature