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

NEUROCOMPUTING

卷:171

Article

Barranca, Victor J.^1,2,3,4  Kovacic, Gregor⁵  Zhou, Douglas^6,7  Cai, David^1,2,3,6,7 

[1] NYU, Courant Inst Math Sci, New York, NY 10012 USA

[2] NYU, Ctr Neural Sci, New York, NY 10012 USA

[3] New York Univ Abu Dhabi, NYUAD Inst, Abu Dhabi, U Arab Emirates

[4] Swarthmore Coll, Dept Math & Stat, Swarthmore, PA 19081 USA

[5] Rensselaer Polytech Inst, Dept Math Sci, Troy, NY 12180 USA

[6] Shanghai Jiao Tong Univ, Dept Math, MOE LSC, Shanghai 200240, Peoples R China

[7] Shanghai Jiao Tong Univ, Inst Nat Sci, Shanghai 200240, Peoples R China

关键词: Compressive sensing;    Neuronal networks;    Signal processing;    Nonlinear dynamics;   

DOI  :  10.1016/j.neucom.2015.07.067

来源: Elsevier

【 摘 要 】

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. (C) 2015 Elsevier B.V. All rights reserved.

【 授权许可】

Free