1. Object vision is largely invariant to changes of retinal images of objects in size and position. To reveal neuronal mechanisms of this invariance, we recorded activities from single cells in the anterior part of the inferotemporal cortex (anterior IT), determined the critical features for the activation of individual cells, and examined the effects of changes in stimulus size and position on the responses. 2. Twenty-one percent of the anterior IT cells studied here responded to ranges of size > 4 octaves, whereas 43% responded to size ranges < 2 octaves. The optimal stimulus size, measured by the distance between the outer edges along the longest axis of the stimulus, ranged from 1.7 to 30 degrees. 3. The selectivity for shape was mostly preserved over the entire range of effective size and over the receptive field, whereas some subtle but statistically significant changes were observed in one half of the cells studied here. 4. The size-specific responses observed in 43% of the cells are consistent with recent psychophysical data that suggest that images of objects are stored in a size-specific manner in the long-term memory. Both size-dependent and -independent processing of images may occur in anterior IT.

At early stages of the mammalian visual cortex, neurons with similar stimulus selectivities are vertically arrayed through the thickness of the cortical sheet and clustered in patches or bands across the surface. This organization, referred to as a 'column', has been found with respect to one-dimensional stimulus parameters such as orientation of stimulus contours, eye dominance of visual inputs, and direction of stimulus motion. It is unclear, however, whether information with extremely high dimensions, such as visual shape, is organized in a similar columnar fashion or in a different manner in the brain. Here we report that the anterior inferotemporal area of the monkey cortex, the final station of the visual cortical stream crucial for object recognition, consists of columns, each containing cells responsive to similar visual features of objects.

Visual area V4 is a midtier cortical area in the ventral visual pathway. It is crucial for visual object recognition and has been a focus of many studies on visual attention. However, there is no unifying view of V4’s role in visual processing. Neither is there an understanding of how its role in feature processing interfaces with its role in visual attention. This review captures our current knowledge of V4, largely derived from electrophysiological and imaging studies in the macaque monkey. Based on recent discovery of functionally specific domains in V4, we propose that the unifying function of V4 circuitry is to enable selective extraction of specific functional domain-based networks, whether it be by bottom-up specification of object features or by top-down attentionally driven selection.

It has been empirically established that the cerebral cortical areas defined by Brodmann one hundred years ago solely on the basis of cellular organization are closely correlated to their function, such as sensation, association, and motion. Cytoarchitectonically distinct cortical areas have different densities and types of neurons. Thus, signaling patterns may also vary among cytoarchitectonically unique cortical areas. To examine how neuronal signaling patterns are related to innate cortical functions, we detected intrinsic features of cortical firing by devising a metric that efficiently isolates non-Poisson irregular characteristics, independent of spike rate fluctuations that are caused extrinsically by ever-changing behavioral conditions. Using the new metric, we analyzed spike trains from over 1,000 neurons in 15 cortical areas sampled by eight independent neurophysiological laboratories. Analysis of firing-pattern dissimilarities across cortical areas revealed a gradient of firing regularity that corresponded closely to the functional category of the cortical area; neuronal spiking patterns are regular in motor areas, random in the visual areas, and bursty in the prefrontal area. Thus, signaling patterns may play an important role in function-specific cerebral cortical computation.

A fundamental task of the visual system is to infer depth by using binocular disparity. To encode binocular disparity, the visual cortex performs two distinct computations: one detects matched patterns in paired images (matching computation); the other constructs the cross-correlation between the images (correlation computation). How the two computations are used in stereoscopic perception is unclear. We dissociated their contributions in near/far discrimination by varying the magnitude of the disparity across separate sessions. For small disparity (0.03°), subjects performed at chance level to a binocularly opposite-contrast (anti-correlated) random-dot stereogram (RDS) but improved their performance with the proportion of contrast-matched (correlated) dots. For large disparity (0.48°), the direction of perceived depth reversed with an anti-correlated RDS relative to that for a correlated one. Neither reversed nor normal depth was perceived when anti-correlation was applied to half of the dots. We explain the decision process as a weighted average of the two computations, with the relative weight of the correlation computation increasing with the disparity magnitude. We conclude that matching computation dominates fine depth perception, while both computations contribute to coarser depth perception. Thus, stereoscopic depth perception recruits different computations depending on the disparity magnitude.