Bidirectional, experience-dependent regulation of N-methyl-d-aspartate receptor subunit composition in the rat visual cortex during postnatal development

In the visual cortex, as elsewhere, N-methyl-d-aspartate receptors (NMDARs) play a critical role in triggering long-term, experience-dependent synaptic plasticity. Modifications of NMDAR subunit composition alter receptor function, and could have a large impact on the properties of synaptic plasticity. We have used immunoblot analysis to investigate the effects of age and visual experience on the expression of different NMDAR subunits in synaptoneurosomes prepared from rat visual cortices. NMDARs at birth are comprised of NR2B and NR1 subunits, and, over the first 5 postnatal weeks, there is a progressive inclusion of the NR2A subunit. Dark rearing from birth attenuates the developmental increase in NR2A. Levels of NR2A increase rapidly (in <2 hr) when dark-reared animals are exposed to light, and decrease gradually over the course of 3 to 4 days when animals are deprived of light. These data reveal that NMDAR subunit composition in the visual cortex is remarkably dynamic and bidirectionally regulated by sensory experience. We propose that NMDAR subunit regulation is a mechanism for experience-dependent modulation of synaptic plasticity in the visual cortex, and serves to maintain synaptic strength within an optimal dynamic range.

Surgically created neural pathways mediate visual pattern discrimination

Combined lesions of retinal targets and ascending auditory pathways can induce, in developing animals, permanent retinal projections to auditory thalamic nuclei and to visual thalamic nuclei that normally receive little direct retinal input. Neurons in the auditory cortex of such animals have visual response properties that resemble those of neurons in the primary visual cortex of normal animals. Therefore, we investigated the behavioral function of the surgically induced retino-thalamo-cortical pathways. We showed that both surgically induced pathways can mediate visually guided behaviors whose normal substrate, the pathway from the retina to the primary visual cortex via the primary thalamic visual nucleus, is missing.

Functional analysis of primary visual cortex (V1) in humans

Human area V1 offers an excellent opportunity to study, using functional MRI, a range of properties in a specific cortical visual area, whose borders are defined objectively and convergently by retinotopic criteria. The retinotopy in V1 (also known as primary visual cortex, striate cortex, or Brodmann’s area 17) was defined in each subject by using both stationary and phase-encoded polar coordinate stimuli. Data from V1 and neighboring retinotopic areas were displayed on flattened cortical maps. In additional tests we revealed the paired cortical representations of the monocular “blind spot.” We also activated area V1 preferentially (relative to other extrastriate areas) by presenting radial gratings alternating between 6% and 100% contrast. Finally, we showed evidence for orientation selectivity in V1 by measuring transient functional MRI increases produced at the change in response to gratings of differing orientations. By systematically varying the orientations presented, we were able to measure the bandwidth of the orientation “transients” (45°).

Image recognition: Visual grouping, recognition, and learning

Vision extracts useful information from images. Reconstructing the three-dimensional structure of our environment and recognizing the objects that populate it are among the most important functions of our visual system. Computer vision researchers study the computational principles of vision and aim at designing algorithms that reproduce these functions. Vision is difficult: the same scene may give rise to very different images depending on illumination and viewpoint. Typically, an astronomical number of hypotheses exist that in principle have to be analyzed to infer a correct scene description. Moreover, image information might be extracted at different levels of spatial and logical resolution dependent on the image processing task. Knowledge of the world allows the visual system to limit the amount of ambiguity and to greatly simplify visual computations. We discuss how simple properties of the world are captured by the Gestalt rules of grouping, how the visual system may learn and organize models of objects for recognition, and how one may control the complexity of the description that the visual system computes.

Selective attention to stimulus location modulates the steady-state visual evoked potential.

Steady-state visual evoked potentials (SSVEPs) were recorded from the scalp of human subjects who were cued to attend to a rapid sequence of alphanumeric characters presented to one visual half-field while ignoring a concurrent sequence of characters in the opposite half-field. These two-character sequences were each superimposed upon a small square background that was flickered at a rate of 8.6 Hz in one half-field and 12 Hz in the other half-field. The amplitude of the frequency-coded SSVEP elicited by either of the task-irrelevant flickering backgrounds was significantly enlarged when attention was focused upon the character sequence at the same location. This amplitude enhancement with attention was most prominent over occipital-temporal scalp areas of the right cerebral hemisphere regardless of the visual field of stimulation. These findings indicate that the SSVEP reflects an enhancement of neural responses to all stimuli that fall within the "spotlight" of spatial attention, whether or not the stimuli are task-relevant. Recordings of the SSVEP provide a new approach for studying the neural mechanisms and functional properties of selective attention to multi-element visual displays.

Computational models of cortical visual processing.

The visual responses of neurons in the cerebral cortex were first adequately characterized in the 1960s by D. H. Hubel and T. N. Wiesel [(1962) J. Physiol. (London) 160, 106-154; (1968) J. Physiol. (London) 195, 215-243] using qualitative analyses based on simple geometric visual targets. Over the past 30 years, it has become common to consider the properties of these neurons by attempting to make formal descriptions of these transformations they execute on the visual image. Most such models have their roots in linear-systems approaches pioneered in the retina by C. Enroth-Cugell and J. R. Robson [(1966) J. Physiol. (London) 187, 517-552], but it is clear that purely linear models of cortical neurons are inadequate. We present two related models: one designed to account for the responses of simple cells in primary visual cortex (V1) and one designed to account for the responses of pattern direction selective cells in MT (or V5), an extrastriate visual area thought to be involved in the analysis of visual motion. These models share a common structure that operates in the same way on different kinds of input, and instantiate the widely held view that computational strategies are similar throughout the cerebral cortex. Implementations of these models for Macintosh microcomputers are available and can be used to explore the models' properties.

Binocular visual surface perception.

Binocular disparity, the differential angular separation between pairs of image points in the two eyes, is the well-recognized basis for binocular distance perception. Without denying disparity's role in perceiving depth, we describe two perceptual phenomena, which indicate that a wider view of binocular vision is warranted. First, we show that disparity can play a critical role in two-dimensional perception by determining whether separate image fragments should be grouped as part of a single surface or segregated as parts of separate surfaces. Second, we show that stereoscopic vision is not limited to the registration and interpretation of binocular disparity but that it relies on half-occluded points, visible to one eye and not the other, to determine the layout and transparency of surfaces. Because these half-visible points are coded by neurons carrying eye-of-origin information, we suggest that the perception of these surface properties depends on neural activity available at visual cortical area V1.