Compressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks

Victor J. Barranca, Douglas Zhou, David Cai

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number060201
JournalPhysical Review E
Issue number6
StatePublished - Jun 16 2016

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics


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