On the Physiology of Bistable Percepts

Binocular rivalry refers to the alternating perceptions experienced when two dissimilar patterns are stereoscopically viewed. To study the neural mechanism that underlies such competitive interactions, single cells were recorded in the visual areas V1, V2, and V4, while monkeys reported the perceived orientation of rivaling sinusoidal grating patterns. A number of neurons in all areas showed alternating periods of excitation and inhibition that correlated with the perceptual dominance and suppression of the cell"s preferred orientation. The remaining population of cells were not influenced by whether or not the optimal stimulus orientation was perceptually suppressed. Response modulation during rivalry was not correlated with cell attributes such as monocularity, binocularity, or disparity tuning. These results suggest that the awareness of a visual pattern during binocular rivalry arises through interactions between neurons at different levels of visual pathways, and that the site of suppression is unlikely to correspond to a particular visual area, as often hypothesized on the basis of psychophysical observations. The cell-types of modulating neurons and their overwhelming preponderance in higher rather than in early visual areas also suggests -- together with earlier psychophysical evidence -- the possibility of a common mechanism underlying rivalry as well as other bistable percepts, such as those experienced with ambiguous figures.

Statistical Models for Co-occurrence Data

Modeling and predicting co-occurrences of events is a fundamental problem of unsupervised learning. In this contribution we develop a statistical framework for analyzing co-occurrence data in a general setting where elementary observations are joint occurrences of pairs of abstract objects from two finite sets. The main challenge for statistical models in this context is to overcome the inherent data sparseness and to estimate the probabilities for pairs which were rarely observed or even unobserved in a given sample set. Moreover, it is often of considerable interest to extract grouping structure or to find a hierarchical data organization. A novel family of mixture models is proposed which explain the observed data by a finite number of shared aspects or clusters. This provides a common framework for statistical inference and structure discovery and also includes several recently proposed models as special cases. Adopting the maximum likelihood principle, EM algorithms are derived to fit the model parameters. We develop improved versions of EM which largely avoid overfitting problems and overcome the inherent locality of EM--based optimization. Among the broad variety of possible applications, e.g., in information retrieval, natural language processing, data mining, and computer vision, we have chosen document retrieval, the statistical analysis of noun/adjective co-occurrence and the unsupervised segmentation of textured images to test and evaluate the proposed algorithms.