A neural cocktail-party processor
Biological Cybernetics
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Image segmentation based on oscillatory correlation
Neural Computation
Adaptive resonance theory (ART)
The handbook of brain theory and neural networks
Synchrony and desynchrony in integrate-and-fire oscillators
Neural Computation
Fast synchronization of perceptual grouping in laminar visual cortical circuits
Neural Networks - 2004 Special issue Vision and brain
Cortical synchronization and perceptual framing
Journal of Cognitive Neuroscience
Pattern segmentation in associative memory
Neural Computation
Temporal segmentation in a neural dynamic system
Neural Computation
Scene analysis by integrating primitive segmentation andassociative memory
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Perfect image segmentation using pulse coupled neural networks
IEEE Transactions on Neural Networks
Object detection using pulse coupled neural networks
IEEE Transactions on Neural Networks
Motion segmentation based on motion/brightness integration and oscillatory correlation
IEEE Transactions on Neural Networks
Grouping synchronization in a pulse-coupled network of chaotic spiking oscillators
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
The time dimension for scene analysis
IEEE Transactions on Neural Networks
Adaptive Synchronization Between Two Different Chaotic Neural Networks With Time Delay
IEEE Transactions on Neural Networks
Unsupervised Segmentation With Dynamical Units
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A semantic model to study neural organization of language in bilingualism
Computational Intelligence and Neuroscience - Special issue on processing of brain signals by using hemodynamic and neuroelectromagnetic modalities
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Synchronization of neural activity in the gamma band is assumed to play a significant role not only in perceptual processing, but also in higher cognitive functions. Here, we propose a neural network of Wilson-Cowan oscillators to simulate recognition of abstract objects, each represented as a collection of four features. Features are ordered in topological maps of oscillators connected via excitatory lateral synapses, to implement a similarity principle. Experience on previous objects is stored in long-range synapses connecting the different topological maps, and trained via timing dependent Hebbian learning (previous knowledge principle). Finally, a downstream decision network detects the presence of a reliable object representation, when all features are oscillating in synchrony. Simulations performed giving various simultaneous objects to the network (from 1 to 4), with some missing and/or modified properties suggest that the network can reconstruct objects, and segment them from the other simultaneously present objects, even in case of deteriorated information, noise, and moderate correlation among the inputs (one common feature). The balance between sensitivity and specificity depends on the strength of the Hebbian learning. Achieving a correct reconstruction in all cases, however, requires ad hoc selection of the oscillation frequency. The model represents an attempt to investigate the interactions among topological maps, autoassociative memory, and gamma-band synchronization, for recognition of abstract objects.