Chaotic synchronization in 2D lattice for scene segmentation
Neurocomputing
Spatio-temporal Dynamics during Perceptual Processing in an Oscillatory Neural Network
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
A network of integrate and fire neurons for visual selection
Neurocomputing
An oscillatory correlation model of object-based attention
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Automatic road extraction from satellite imagery using LEGION networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Chaotic phase synchronization for visual selection
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Single vs. population cell coding: gaze movement control in target search
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Strategic planning in the game of Go using coupled non-linear oscillators
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Learning internal representation of visual context in a neural coding network
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Expert Systems with Applications: An International Journal
Optical Memory and Neural Networks
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A fundamental issue in neural computation is the binding problem, which refers to how sensory elements in a scene organize into perceived objects, or percepts. The issue of binding is hotly debated in recent years in neuroscience and related communities. Much of the debate, however, gives little attention to computational considerations. This review intends to elucidate the computational issues that bear directly on the binding issue. The review starts with two problems considered by Rosenblatt to be the most challenging to the development of perceptron theory more than 40 years ago, and argues that the main challenge is the figure-ground separation problem, which is intrinsically related to the binding problem. The theme of the review is that the time dimension is essential for systematically attacking Rosenblatt's challenge. The temporal correlation theory as well as its special form-oscillatory correlation theory-is discussed as an adequate representation theory to address the binding problem. Recent advances in understanding oscillatory dynamics are reviewed, and these advances have overcome key computational obstacles for the development of the oscillatory correlation theory. We survey a variety of studies that address the scene analysis problem. The results of these studies have substantially advanced the capability of neural networks for figure-ground separation. A number of issues regarding oscillatory correlation are considered and clarified. Finally, the time dimension is argued to be necessary for versatile computing.