Image segmentation based on oscillatory correlation
Neural Computation
Synchrony and desynchrony in integrate-and-fire oscillators
Neural Computation
Segmentation of MRI trabecular-bone images using network of synchronised oscillators
Machine Graphics & Vision International Journal
MICRONEURO '99 Proceedings of the 7th International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems
A VLSI Implementation of a Neuromorphic Network for Scene Segmentation
MICRONEURO '99 Proceedings of the 7th International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems
Building semantic perceptron net for topic spotting
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Analysis of Microscopic Mast Cell Images Based on Network of Synchronised Oscillators
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Analog CMOS Circuits Implementing Neural Segmentation Model Based on Symmetric STDP Learning
Neural Information Processing
Action Recognition Using a Bio-Inspired Feedforward Spiking Network
International Journal of Computer Vision
Selective attention model with spiking elements
Neural Networks
Selective Attention Improves Learning
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Partial synchronization of neural activity and information processing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Real-time robot path planning based on a modified pulse-coupled neural network model
IEEE Transactions on Neural Networks
Competition through selective inhibitory synchrony
Neural Computation
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A novel class of locally excitatory, globally inhibitory oscillator networks (LEGION) is proposed and investigated. The model of each oscillator corresponds to a standard relaxation oscillator with two time scales. In the network, an oscillator jumping up to its active phase rapidly recruits the oscillators stimulated by the same pattern, while preventing other oscillators from jumping up. Computer simulations demonstrate that the network rapidly achieves both synchronization within blocks of oscillators that are stimulated by connected regions and desynchronization between different blocks. This model lays a physical foundation for the oscillatory correlation theory of feature binding and may provide an effective computational framework for scene segmentation and figure/ground segregation in real time