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.
['DeLiang Wang']
The time dimension for scene analysis