Efficient image processing via compressive sensing of integrate-and-fire neuronal network dynamics

Victor J. Barranca, Gregor Kovačič, Douglas Zhou, David Cai

Research output: Contribution to journalArticlepeer-review

Abstract

Integrate-and-fire (I&F) neuronal networks are ubiquitous in diverse image processing applications, including image segmentation and visual perception. While conventional I&F network image processing requires the number of nodes composing the network to be equal to the number of image pixels driving the network, we determine whether I&F dynamics can accurately transmit image information when there are significantly fewer nodes than network input-signal components. Although compressive sensing (CS) theory facilitates the recovery of images using very few samples through linear signal processing, it does not address whether similar signal recovery techniques facilitate reconstructions through measurement of the nonlinear dynamics of an I&F network. In this paper, we present a new framework for recovering sparse inputs of nonlinear neuronal networks via compressive sensing. By recovering both one-dimensional inputs and two-dimensional images, resembling natural stimuli, we demonstrate that input information can be well-preserved through nonlinear I&F network dynamics even when the number of network-output measurements is significantly smaller than the number of input-signal components. This work suggests an important extension of CS theory potentially useful in improving the processing of medical or natural images through I&F network dynamics and understanding the transmission of stimulus information across the visual system.

Original languageEnglish (US)
Pages (from-to)1313-1322
Number of pages10
JournalNeurocomputing
Volume171
DOIs
StatePublished - Jan 1 2016

Keywords

  • Compressive sensing
  • Neuronal networks
  • Nonlinear dynamics
  • Signal processing

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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