Stability and intermittency in large-scale coupled oscillator models for perceptual segmentation
Journal of Mathematical Psychology
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)
Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)
Object localisation using laterally connected "What" and "Where" associator networks
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
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Spike synchronisation and de-synchronisation are important for feature binding and separation at various levels in the visual system. We present a model of complex valued neuron activations which are synchronised using lateral couplings. The firing rates of the model neurons correspond to a complex number’s absolute value and obey conventional attractor network relaxation dynamics, while the firing phases correspond to a complex number’s angle and follow the dynamics of a logistic map. During relaxation, we show that features with strong couplings are grouped by firing in the same phase and are separated in phase from features that are coupled weakly or by negative weights. In an example, we apply the model to the level of a hidden representation of an image, thereby segmenting it on an abstract level. We imply that this process can facilitate unsupervised learning of objects in cluttered background.