Compressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks
نویسندگان
چکیده
منابع مشابه
Compressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks.
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 t...
متن کاملBRDF Reconstruction Using Compressive Sensing
Compressive sensing is a technique for efficiently acquiring and reconstructing the data. This technique takes advantage of sparseness or compressibility of the data, allowing the entire measured data to be recovered from relatively few measurements. Considering the fact that the BRDF data often can be highly sparse, we propose to employ the compressive sensing technique for an efficient recons...
متن کاملReconstruction Algorithms in Compressive Sensing: An Overview
The theory Compressive Sensing (CS) has provided a new acquisition strategy and recovery with good in the image processing area. This theory guarantees to recover a signal with high probability from a reduced sampling rate below the Nyquist-Shannon limit. The problem of recovering the original signal from the samples consists in solving an optimization problem. This article presents an overview...
متن کاملCompressive Sensing Reconstruction for Sparse 2D Data
In this paper we study the compressive sensing effects on 2D signals exhibiting sparsity in 2D DFT domain. A simple algorithm for reconstruction of randomly under-sampled data is proposed. It is based on the analytically determined threshold that precisely separates signal and non-signal components in the 2D DFT domain. The algorithm operates fast in a single iteration providing the accurate si...
متن کاملDiffusion Adaptation Framework for Compressive Sensing Reconstruction
Compressive sensing(CS) has drawn much attention in recent years due to its low sampling rate as well as high recovery accuracy. As an important procedure, reconstructing a sparse signal from few measurement data has been intensively studied. Many reconstruction algorithms have been proposed and shown good reconstruction performance. However, when dealing with large-scale sparse signal reconstr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Physical Review E
سال: 2016
ISSN: 2470-0045,2470-0053
DOI: 10.1103/physreve.93.060201