Identifying Chaotic FitzHugh–Nagumo Neurons Using Compressive Sensing
نویسندگان
چکیده
منابع مشابه
Identifying Chaotic FitzHugh-Nagumo Neurons Using Compressive Sensing
We develop a completely data-driven approach to reconstructing coupled neuronal networks that contain a small subset of chaotic neurons. Such chaotic elements can be the result of parameter shift in their individual dynamical systems and may lead to abnormal functions of the network. To accurately identify the chaotic neurons may thus be necessary and important, for example, applying appropriat...
متن کاملBeamforming using compressive sensing.
Compressive sensing (CS) is compared with conventional beamforming using horizontal beamforming of at-sea, towed-array data. They are compared qualitatively using bearing time records and quantitatively using signal-to-interference ratio. Qualitatively, CS exhibits lower levels of background interference than conventional beamforming. Furthermore, bearing time records show increasing, but toler...
متن کاملCompressive Sensing by Colpitts Chaotic Oscillator for Image Sensors
Compressive sensing uses simultaneous sensing and compression to provide an efficient image acquisition technique and it has been demonstrated in optical and electrical image sensors. To guarantee exact recovery from sparse measurements, specific sensing matrix, which satisfies the Restricted Isometry Property (RIP), should be well chosen. Toeplitz-structured chaotic sensing matrix constructed ...
متن کاملSound Localization using Compressive Sensing
In a sensor network with remote sensor devices, it is important to have a method that can accurately localize a sound event with a small amount of data transmitted from the sensors. In this paper, we propose a novel method for localization of a sound source using compressive sensing. Instead of sampling a large amount of data at the Nyquist sampling rate in time domain, the acoustic sensors tak...
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 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: Entropy
سال: 2014
ISSN: 1099-4300
DOI: 10.3390/e16073889