Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spatio-temporal Dynamics of Intrinsic Networks in Functional Magnetic
  Imaging Data Using Recurrent Neural Networks   We introduce a novel recurrent neural network (RNN) approach to account for
temporal dynamics and dependencies in brain networks observed via functional
magnetic resonance imaging (fMRI). Our approach directly parameterizes temporal
dynamics through recurrent connections, which can be used to formulate blind
source separation with a conditional (rather than marginal) independence
assumption, which we call RNN-ICA. This formulation enables us to visualize the
temporal dynamics of both first order (activity) and second order (directed
connectivity) information in brain networks that are widely studied in a static
sense, but not well-characterized dynamically. RNN-ICA predicts dynamics
directly from the recurrent states of the RNN in both task and resting state
fMRI. Our results show both task-related and group-differentiating directed
connectivity.

6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ