TY - JOUR
T1 - Compressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks
AU - Barranca, Victor J.
AU - Zhou, Douglas
AU - Cai, David
N1 - Funding Information:
This work was supported by N.Y.U. Abu Dhabi Institute Grant No. G1301 (V.J.B., D.Z., D.C.), by Grant No. NSFC-91230202, by the Shanghai Rising-Star Program Grant No. 15QA1402600 (D.Z.), Shanghai Grants No. 14JC1403800, No. 15JC1400104, and No. NSFC-31571071, the SJTU-UM Collaborative Research Program (D.C., D.Z.), and by NSF Grant No. DMS-1009575 (D.C.).
Publisher Copyright:
© 2016 American Physical Society.
PY - 2016/6/16
Y1 - 2016/6/16
N2 - Utilizing the sparsity ubiquitous in real-world network connectivity, we develop a theoretical framework for efficiently reconstructing sparse feed-forward connections in a pulse-coupled nonlinear network through its output activities. Using only a small ensemble of random inputs, we solve this inverse problem through the compressive sensing theory based on a hidden linear structure intrinsic to the nonlinear network dynamics. The accuracy of the reconstruction is further verified by the fact that complex inputs can be well recovered using the reconstructed connectivity. We expect this Rapid Communication provides a new perspective for understanding the structure-function relationship as well as compressive sensing principle in nonlinear network dynamics.
AB - Utilizing the sparsity ubiquitous in real-world network connectivity, we develop a theoretical framework for efficiently reconstructing sparse feed-forward connections in a pulse-coupled nonlinear network through its output activities. Using only a small ensemble of random inputs, we solve this inverse problem through the compressive sensing theory based on a hidden linear structure intrinsic to the nonlinear network dynamics. The accuracy of the reconstruction is further verified by the fact that complex inputs can be well recovered using the reconstructed connectivity. We expect this Rapid Communication provides a new perspective for understanding the structure-function relationship as well as compressive sensing principle in nonlinear network dynamics.
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U2 - 10.1103/PhysRevE.93.060201
DO - 10.1103/PhysRevE.93.060201
M3 - Article
AN - SCOPUS:84975230693
VL - 93
JO - Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
JF - Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
SN - 1063-651X
IS - 6
M1 - 060201
ER -