The behavior of a neural network model for binocular rivalry is explored through the development of an analogy between it and an electronic astable multivibrator circuit. The model incorporates reciprocal feedback inhibition between signals from the left and the right eyes prior to binocular convergence. The strength of inhibitory coupling determines whether the system undergoes rivalrous oscillations or remains in stable fusion: strong coupling leads to oscillations, weak coupling to fusion. This implies that correlation between spatial patterns presented to the two eyes can affect the strength of binocular inhibition. Finally, computer simulations are presented which show that a reciprocal inhibition model can reproduce the stochastic behavior of rivalry. The model described is a counterexample to claims that reciprocal inhibition models as a class cannot exhibit many of the experimentally observed properties of rivalry.

Ventral and dorsal visual pathways perform fundamentally different functions. The former is involved in object recognition, whereas the latter carries out spatial localization of stimuli and visual guidance of motor actions. Despite the association of the dorsal pathway with spatial vision, recent studies have reported shape selectivity in the dorsal stream. We compared shape encoding in anterior inferotemporal cortex (AIT), a high-level ventral area, with that in lateral intraparietal cortex (LIP), a high-level dorsal area, during a fixation task. We found shape selectivities of individual neurons to be greater in anterior inferotemporal cortex than in lateral intraparietal cortex. At the neural population level, responses to different shapes were more dissimilar in AIT than LIP. Both observations suggest a greater capacity in AIT for making finer shape distinctions. Multivariate analyses of AIT data grouped together similar shapes based on neural population responses, whereas such grouping was indistinct in LIP. Thus in a first comparison of shape response properties in late stages of the two visual pathways, we report that AIT exhibits greater capability than LIP for both object discrimination and generalization. These differences in the two visual pathways provide the first neurophysiological evidence that shape encoding in the dorsal pathway is distinct from and not a mere duplication of that formed in the ventral pathway. In addition to shape selectivity, we observed stimulus-driven cognitive effects in both areas. Stimulus repetition suppression in LIP was similar to the well-known repetition suppression in AIT and may be associated with the "inhibition of return" memory effect observed during reflexive attention.

We have characterized selectivity and sparseness in anterior inferotemporal cortex, using a large data set. Responses were collected from 674 monkey inferotemporal cells, each stimulated by 806 object photographs. This 806 × 674 matrix was examined in two ways: columnwise, looking at responses of a single neuron to all images (single-neuron selectivity), and rowwise, looking at the responses of all neurons caused by a single image (population sparseness). Selectivity and sparseness were measured as kurtosis of probability distributions. Population sparseness exceeded single-neuron selectivity, with specific values dependent on the size of the data sample. This difference was principally caused by inclusion, within the population, of neurons with a variety of dynamic ranges (standard deviations of responses over all images). Statistics of large responses were examined by quantifying how quickly the upper tail of the probability distribution decreased (tail heaviness). This analysis demonstrated that population responses had heavier tails than single-neuron responses, consistent with the difference between sparseness and selectivity measurements. Population responses with spontaneous activity subtracted had the heaviest tails, following a power law. The very light tails of single-neuron responses indicate that the critical feature for each neuron is simple enough to have a high probability of occurring within a limited stimulus set. Heavy tails of population responses indicate that there are a large number of different critical features to which different neurons are tuned. These results are inconsistent with some structural models of object recognition that posit that objects are decomposed into a small number of standard features.