scholarly journals Detection of extremely weak NQR signals using stochastic resonance and neural network theories

2018 ◽  
Vol 142 ◽  
pp. 96-103 ◽  
Author(s):  
Weihang Shao ◽  
Jamie Barras ◽  
Panagiotis Kosmas
Keyword(s):  
Neural Network ◽  
Stochastic Resonance

Enhancing threshold neural network via suprathreshold stochastic resonance for pattern classification

2021 ◽  
pp. 127387
Author(s):  
Xiaojie Liu ◽  
Lingling Duan ◽  
Fabing Duan ◽  
François Chapeau-Blondeau ◽  
Derek Abbott
Keyword(s):  
Neural Network ◽  
Stochastic Resonance ◽  
Pattern Classification ◽  
Suprathreshold Stochastic Resonance

Stochastic resonance using noise generated by a neural network

1999 ◽  
Vol 59 (3) ◽  
pp. 3339-3343 ◽  
Author(s):  
G. Mato
Keyword(s):  
Neural Network ◽  
Stochastic Resonance

Harmonic stochastic resonance-enhanced signal detecting in NW small-world neural network

2010 ◽  
Vol 19 (11) ◽  
pp. 110515 ◽  
Author(s):  
Dao-Guang Wang ◽  
Xiao-Ming Liang ◽  
Jing Wang ◽  
Cheng-Fang Yang ◽  
Kai Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Stochastic Resonance ◽  
Small World

STOCHASTIC RESONANCE IN HINDMARSH–ROSE NEURAL NETWORK WITH SMALL-WORLD CONNECTIONS

2008 ◽  
Vol 22 (30) ◽  
pp. 5365-5373 ◽  
Author(s):  
RENHUAN YANG ◽  
AIGUO SONG
Keyword(s):  
Neural Network ◽  
Stochastic Resonance ◽  
Random Network ◽  
Small World ◽  
Resonance Curve ◽  
Resonance Phenomenon ◽  
Node Degree ◽  
Regular Network ◽  
Stochastic Resonance Phenomenon ◽  
Peak Value

We study stochastic resonance (SR) in Hindmarsh–Rose (HR) neural network with small-world (SW) connections driven by external periodic stimulus, focusing on the dependence of properties of SR on the network structure parameters. It is found that, the SW neural network enhances SR compared with single neuron. By turning coupling strength c, two categories of SR were gained. With the connection-rewiring probability p increasing, the resonance curve becomes more and more sharp and the peak value increases gradually and then reaches saturation. The SW network enhances the SR peak value compared with regular network and widens resonance in ascending range compared with random network. When decreasing node degree k, the resonance range is enlarged, and the signal noise ratio (SNR) curve becomes a two peak one from a classic single peak SR curve, and then the stochastic resonance phenomenon almost disappears.

STOCHASTIC RESONANCE IN COUPLED THRESHOLD ELEMENTS WITH INPUT SIGNALS SHIFTED IN PHASE

2000 ◽  
Vol 14 (08) ◽  
pp. 837-852
Author(s):  
A. KRAWIECKI ◽  
A. SUKIENNICKI ◽  
R. A. KOSIŃSKI
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stochastic Resonance ◽  
Response Function ◽  
Signal To Noise Ratio ◽  
Binary Response ◽  
Signal To Noise ◽  
Input And Output ◽  
Input Signals ◽  
Periodic Signals

Stochastic resonance in a system of two coupled threshold elements (neurons) forming a small artificial neural network is investigated. The elements have either antisymmetric or logistic (binary) response function and are driven by periodic signals and independent noises. Periodic signals at their inputs have equal amplitudes and frequencies but are shifted in phase. Depending on the response function and the phase shift, enhancement of stochastic resonance in individual elements, characterized by the output signal-to-noise ratio, and stochastic resonance with a spatiotemporal input signal, characterized by the correlation function between the input and output signals, are observed for proper coupling between elements.

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