Coupled oscillator networks with frustration

2016 ◽  
pp. 163-181
Author(s):  
Yoko Uwate Uwate ◽  
Yoshifumi Nishio Nishio
Keyword(s):  
Coupled Oscillator ◽  
Oscillator Networks

Efficient recovery of dynamic behavior in coupled oscillator networks

2013 ◽  
Vol 88 (3) ◽  
Author(s):  
Kai Morino ◽  
Gouhei Tanaka ◽  
Kazuyuki Aihara
Keyword(s):  
Dynamic Behavior ◽  
Coupled Oscillator ◽  
Oscillator Networks ◽  
Efficient Recovery

Threshold Color Sensing Using Coupled Oscillator Networks

2019 ◽  
Author(s):  
Conor S. Pyles ◽  
Nikhil Bajaj ◽  
Jeffrey F. Rhoads ◽  
Dana Weinstein ◽  
D. Dane Quinn
Keyword(s):  
Coupled Oscillator ◽  
Oscillator Networks

scholarly journals Model reconstruction from temporal data for coupled oscillator networks

2019 ◽  
Vol 29 (10) ◽  
pp. 103116 ◽  
Author(s):  
Mark J. Panaggio ◽  
Maria-Veronica Ciocanel ◽  
Lauren Lazarus ◽  
Chad M. Topaz ◽  
Bin Xu
Keyword(s):  
Temporal Data ◽  
Model Reconstruction ◽  
Coupled Oscillator ◽  
Oscillator Networks

scholarly journals Partial synchronization in diffusively time-delay coupled oscillator networks

2012 ◽  
Vol 22 (4) ◽  
pp. 043144 ◽  
Author(s):  
Erik Steur ◽  
Toshiki Oguchi ◽  
Cees van Leeuwen ◽  
Henk Nijmeijer
Keyword(s):  
Time Delay ◽  
Coupled Oscillator ◽  
Partial Synchronization ◽  
Oscillator Networks

scholarly journals Isochronous dynamics in pulse coupled oscillator networks with delay

2017 ◽  
Vol 27 (5) ◽  
pp. 053103 ◽  
Author(s):  
Pan Li ◽  
Wei Lin ◽  
Konstantinos Efstathiou
Keyword(s):  
Coupled Oscillator ◽  
Oscillator Networks

scholarly journals Computing with Arrays of Coupled Oscillators: An Application to Preattentive Texture Discrimination

1990 ◽  
Vol 2 (4) ◽  
pp. 458-471 ◽  
Author(s):  
Pierre Baldi ◽  
Ronny Meir
Keyword(s):  
Long Range ◽  
Cortical Neurons ◽  
Coupled Oscillators ◽  
Dynamic Evolution ◽  
Coupled Oscillator ◽  
Texture Discrimination ◽  
Oscillator Networks ◽  
Neural Computations ◽  
Different Populations ◽  
Experimental Findings

Recent experimental findings (Gray et al. 1989; Eckhorn et al. 1988) seem to indicate that rapid oscillations and phase-lockings of different populations of cortical neurons play an important role in neural computations. In particular, global stimulus properties could be reflected in the correlated firing of spatially distant cells. Here we describe how simple coupled oscillator networks can be used to model the data and to investigate whether useful tasks can be performed by oscillator architectures. A specific demonstration is given for the problem of preattentive texture discrimination. Texture images are convolved with different sets of Gabor filters feeding into several corresponding arrays of coupled oscillators. After a brief transient, the dynamic evolution in the arrays leads to a separation of the textures by a phase labeling mechanism. The importance of noise and of long range connections is briefly discussed.

scholarly journals Perceptual grouping through competition in coupled oscillator networks

2014 ◽  
Vol 141 ◽  
pp. 76-83 ◽  
Author(s):  
Martin Meier ◽  
Robert Haschke ◽  
Helge J. Ritter
Keyword(s):  
Perceptual Grouping ◽  
Coupled Oscillator ◽  
Oscillator Networks
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