A fundamental aspect of perception is to bind spatially separate sensory features, essential for object identification, segmentation of different objects, and figure/ground segregation. Theoretical considerations and neurophysiological findingspoint to the temporal correlation of feature detectors as a binding mechanism. In particular, it has been demonstrated that the cat visual cortex exhibits 40-60 H z stimulus-dependent oscillations, and synchronization exists in spatially remote columns (up to 7 m m ) which reflects global stimulus properties (Gray et al., 1989; Eckhom et al., 1988). What neural mechanisms underlie this global synchrony? Many neural models thus proposed end up relying on global connections, leading to the question of whether lateral connections alone can jwoduce remote synchronization. With a formulation diffwent from the frequently used phase model, w e find that locally coupled neural oscillators can indeed yield global synchrony. The model employs a previously suggested mechanism that the efficacy of the connections is allowed to change on a fast time scale. Based on the known connectivity of the visual cortex, the model outputs closely resemble the experimental findings. This model lays a computational foundation for Gestalt perceptual grouping.

Based on an interference theory of forgetting in short-term memory (STM), we model STM by a network of neural limits with mutual inhibition. Sequences are acquired by combining a Hebbian learning rule and a normalization rule with sequential system activation. As long as sequences are acquired, they can be recognized without being affected by speeds in presentation. The model of sequence reproduction consists of two reciprocally connected networks, one of which behaves as sequence recognizers. Reproduction of complex sequences is shown to be able to maintain interval lengths of sequence components. A mechanism of degree self-tuning based on a global inhibitor is proposed for the model to optimally learn required context lengths in order to disambiguate associations in complex sequence reproduction.

It has been found behaviorally that visual habituation in toads exhibits locus specificity and partial stimulus specificity. Dishabituation among different configurations of visual worm stimuli forms an ordered hierarchy. This paper presents a computational model of the toad visual system involved in pattern discrimination, including retina, tectum, and anterior thalamus. In the model w e propose that the toad discriminates visual objects based on temporal responses, and anterior thalamus has differing representations of different stimulus configurations. This theory is developed through a large scale neural simulation. With a minimum number of hypotheses, we demonstrate that anterior thalamus in response to different worm stimuli shows the same hierarchy as shown in the behavioral experiment. The successful simulation allows us to provide an explanation of neural mechanisms for visual pattern discrimination. This theory predicts that retinal R 2 cells play a primary role in the discrimination via tectal small pear cells (SP) while R 3 cells refine the feature analysis by inhibition. The simulation also demonstrates that the retinal response to the trailing edge of a stimulus is as crucial for pattern discrimination as to the leading edge. N e w dishabituation hierarchies are predicted by shrinking stimulus size and reversing stimulus-background contrast.