Corey M. Ziemba

A functional and perceptual signature of the second visual area in primates

There is no generally accepted account of the function of the second visual cortical area (V2), partly because no simple response properties robustly distinguish V2 neurons from those in primary visual cortex (V1). We constructed synthetic stimuli replicating the higher-order statistical dependencies found in natural texture images and used them to stimulate macaque V1 and V2 neurons. Most V2 cells responded more vigorously to these textures than to control stimuli lacking naturalistic structure; V1 cells did not. Functional magnetic resonance imaging (fMRI) measurements in humans revealed differences between V1 and V2 that paralleled the neuronal measurements. The ability of human observers to detect naturalistic structure in different types of texture was well predicted by the strength of neuronal and fMRI responses in V2 but not in V1. Together, these results reveal a particular functional role for V2 in the representation of natural image structure.

Population representation of visual information in areas V1 and V2 of amblyopic macaques

Amblyopia is a developmental disorder resulting in poor vision in one eye. The mechanism by which input to the affected eye is prevented from reaching the level of awareness remains poorly understood. We recorded simultaneously from large populations of neurons in the supragranular layers of areas V1 and V2 in 6 macaques that were made amblyopic by rearing with artificial strabismus or anisometropia, and 1 normally reared control. In agreement with previous reports, we found that cortical neuronal signals driven through the amblyopic eyes were reduced, and that cortical neurons were on average more strongly driven by the non-amblyopic than by the amblyopic eyes. We analyzed multiunit recordings using standard population decoding methods, and found that visual signals from the amblyopic eye, while weakened, were not degraded enough to explain the behavioral deficits. Thus additional losses must arise in downstream processing. We tested the idea that under monocular viewing conditions, only signals from neurons dominated by - rather than driven by - the open eye might be used. This reduces the proportion of neuronal signals available from the amblyopic eye, and amplifies the interocular difference observed at the level of single neurons. We conclude that amblyopia might arise in part from degradation in the neuronal signals from the amblyopic eye, and in part from a reduction in the number of signals processed by downstream areas.

Selectivity and tolerance for visual texture in macaque V2

Significance The brain generates increasingly complex representations of the visual world to recognize objects, to form new memories, and to organize visual behavior. Relatively simple signals in the retina are transformed through a cascade of neural computations into highly complex responses in visual cortical areas deep in the temporal lobe. The representations of visual signals in areas that lie in the middle of this cascade remain poorly understood, yet they are critical to understanding how the cascade operates. Here, we demonstrate changes in the representation of visual information from area V1 to V2, and show how these changes extract and represent information about the local statistical features of visual images. As information propagates along the ventral visual hierarchy, neuronal responses become both more specific for particular image features and more tolerant of image transformations that preserve those features. Here, we present evidence that neurons in area V2 are selective for local statistics that occur in natural visual textures, and tolerant of manipulations that preserve these statistics. Texture stimuli were generated by sampling from a statistical model, with parameters chosen to match the parameters of a set of visually distinct natural texture images. Stimuli generated with the same statistics are perceptually similar to each other despite differences, arising from the sampling process, in the precise spatial location of features. We assessed the accuracy with which these textures could be classified based on the responses of V1 and V2 neurons recorded individually in anesthetized macaque monkeys. We also assessed the accuracy with which particular samples could be identified, relative to other statistically matched samples. For populations of up to 100 cells, V1 neurons supported better performance in the sample identification task, whereas V2 neurons exhibited better performance in texture classification. Relative to V1, the responses of V2 show greater selectivity and tolerance for the representation of texture statistics.

Unwrapping the Ventral Stream

It seems immediately obvious that the apple in my hand is the same apple I just picked up from the table. How can my brain recognize an object despite substantial variation in its location, size, and context? In a series of cortical areas known as the ventral stream, the brain performs sophisticated

Representing “stuff” in visual cortex

Despite decades of study, we do not understand the fundamental processes by which our brain encodes and represents incoming visual information and uses it to guide perception and action. A wealth of evidence suggests that visual recognition is mediated by a series of areas in primate cortex known as the ventral stream, including V1 (primary visual cortex), V2, and V4 (Fig. 1A) (1). The earliest stages are to some extent understood; Hubel and Wiesel famously discovered, for example, that neurons in V1 respond selectively to the orientation and direction of a moving edge (2). However, a vast gulf remains between coding for a simple edge and representing the full richness of our visual world. David Hubel himself observed in 2012 that we still “have almost no examples of neural structures in which we know the difference between the information coming in and what is going out—what the structure is for. We have some idea of the answer for the retina, the lateral geniculate body, and the primary visual cortex, but that’s about it” (3). In PNAS, Okazawa et al. (4) make significant headway in this quest by uncovering and characterizing a unique form of neural selectivity in area V4.

Slow gain fluctuations limit benefits of temporal integration in visual cortex

Sensory neurons represent stimulus information with sequences of action potentials that differ across repeated measurements. This variability limits the information that can be extracted from momentary observations of a neurons response. It is often assumed that integrating responses over time mitigates this limitation. However, temporal response correlations can reduce the benefits of temporal integration. We examined responses of individual orientation-selective neurons in the primary visual cortex of two macaque monkeys performing an orientation-discrimination task. The signal-to-noise ratio of temporally integrated responses increased for durations up to a few hundred milliseconds but saturated for longer durations. This was true even when cells exhibited little or no adaptation in their response levels. These observations are well explained by a statistical response model in which spikes arise from a Poisson process whose stimulus-dependent rate is modulated by slow, stimulus-independent fluctuations in gain. The response variability arising from the Poisson process is reduced by temporal integration, but the slow modulatory nature of variability due to gain fluctuations is not. Slow gain fluctuations therefore impose a fundamental limit on the benefits of temporal integration.

Contextual modulation of sensitivity to naturalistic image structure in macaque V2

The stimulus selectivity of neurons in V1 is well known, as is the finding that their responses can be affected by visual input to areas outside of the classical receptive field. Less well understood are the ways selectivity is modified as signals propagate to visual areas beyond V1, such as V2. We recently proposed a role for V2 neurons in representing the higher order statistical dependencies found in images of naturally occurring visual texture. V2 neurons, but not V1 neurons, respond more vigorously to “naturalistic” images that contain these dependencies than to “noise” images that lack them. In this work, we examine the dependency of these effects on stimulus size. For most V2 neurons, the preference for naturalistic over noise stimuli was modest when presented in small patches and gradually strengthened with increasing size, suggesting that the mechanisms responsible for this enhanced sensitivity operate over regions of the visual field that are larger than the classical receptive field. Indeed, we found that surround suppression was stronger for noise than for naturalistic stimuli and that the preference for large naturalistic stimuli developed over a delayed time course consistent with lateral or feedback connections. These findings are compatible with a spatially broad facilitatory mechanism that is absent in V1 and suggest that a distinct role for the receptive field surround emerges in V2 along with sensitivity for more complex image structure. NEW & NOTEWORTHY The responses of neurons in visual cortex are often affected by visual input delivered to regions of the visual field outside of the conventionally defined receptive field, but the significance of such contextual modulations are not well understood outside of area V1. We studied the importance of regions beyond the receptive field in establishing a novel form of selectivity for the statistical dependencies contained in natural visual textures that first emerges in area V2.

Receptive field properties of V1 and V2 neurons in amblyopic macaque monkeys revealed with local spectral reverse correlation

Amblyopia is a visual disorder associated with a disruption of conjugate binocular vision early in life, resulting in decreased visual performance in one eye. Previous studies have explored the neurophysiological basis of this disorder using single-unit recordings, but the results incompletely explain the perceptual deficits. New advances in recording techniques now allow us to simultaneously sample multiple sites over large areas of visual cortex. Introduction