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

Victor J. Barranca, Doug Zhou, David Cai

Research output: Contribution to journalArticle

Abstract

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 - Statistical, Nonlinear, and Soft Matter Physics
Volume93
Issue number6
DOIs
StatePublished - Jun 16 2016

Fingerprint

Compressive Sensing
Network Dynamics
Feedforward
Nonlinear Dynamics
Connectivity
Network Connectivity
Structure-function
pulses
Sparsity
Inverse Problem
Ensemble
Output
communication
output
Communication
Relationships
Framework

ASJC Scopus subject areas

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

Cite this

Compressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks. / Barranca, Victor J.; Zhou, Doug; Cai, David.

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 93, No. 6, 060201, 16.06.2016.

Research output: Contribution to journalArticle

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