Natural scene statistics and the structure of orientation maps in the visual cortex

Optimizing the Representation of Orientation Preference Maps in Visual Cortex

The colorful representation of orientation preference maps in primary visual cortex has become iconic. However, the standard representation is misleading because it uses a color mapping to indicate orientations based on the HSV (hue, saturation, value) color space, for which important perceptual features such as brightness, and not just hue, vary among orientations. This means that some orientations stand out more than others, conveying a distorted visual impression. This is particularly problematic for visualizing subtle biases caused by slight overrepresentation of some orientations due to, for example, stripe rearing. We show that displaying orientation maps with a color mapping based on a slightly modified version of the HCL (hue, chroma, lightness) color space, so that primarily only hue varies between orientations, leads to a more balanced visual impression. This makes it easier to perceive the true structure of this seminal example of functional brain architecture.

Stripe-rearing changes multiple aspects of the structure of primary visual cortex

An important example of brain plasticity is the change in the structure of the orientation map in mammalian primary visual cortex in response to a visual environment consisting of stripes of one orientation. In principle there are many different ways in which the structure of a normal map could change to accommodate increased preference for one orientation. However, until now these changes have been characterised only by the relative sizes of the areas of primary visual cortex representing different orientations. Here we extend to the stripe-reared case a recently proposed Bayesian method for reconstructing orientation maps from intrinsic signal optical imaging data. We first formulated a suitable prior for the stripe-reared case, and developed an efficient method for maximising the marginal likelihood of the model in order to determine the optimal parameters. We then applied this to a set of orientation maps from normal and stripe-reared cats. This analysis revealed that several parameters of overall map structure, specifically the difference between wavelength, scaling and mean of the two vector components of maps, changed in response to stripe-rearing, which together give a more nuanced assessment of the effect of rearing condition on map structure than previous measures. Overall this work expands our understanding of the effects of the environment on brain structure.

Experience-dependent regulation of functional maps and synaptic protein expression in the cat visual cortex

Although the basis of our knowledge of experience-dependent plasticity comes from studies on carnivores and primates, studies examining the physiological and molecular mechanisms that underlie development and plasticity have increasingly employed mice. We have used several common rearing paradigms, such as dark-rearing and monocular deprivation (MD), to examine the timing of the physiological and molecular changes to altered experience in the cat primary visual cortex. Dark-rearing from birth or for 1 week starting at 4 weeks of age produced a similar reduction in the amplitude of responses measured through intrinsic signal imaging and a reduction in orientation selectivity. One week of visual experience following dark-rearing until 4 weeks of age yielded normal responses in both amplitude and orientation selectivity. The depression of deprived-eye responses was similar in magnitude after 2 and 7 days of MD. In contrast, non-deprived-eye responses almost doubled in magnitude after 7 days compared with 2 days of MD. These changes in the functional properties of primary visual cortex neurons were mirrored by specific changes in synaptic protein expression. Changes in proteins such as the NR2A and NR2B subunits of the N-methyl-D-aspartate receptor, postsynaptic density protein 95, alpha-CA(2+) /calmodulin-dependent protein kinase II (αCaMKII), and GABA(A) α1a indicated that the levels of sensory activity regulated mechanisms associated with both excitatory (NR2A and NR2B) and inhibitory (GABA(A) α1a) transmission so as to maintain response homeostasis. Additionally, we found that MD regulated the AMPA receptor glutamate (GluR1) subunit as well as signalling molecules (αCaMKII and synaptic Ras GTPase activating protein, SynGAP) downstream of N-methyl-D-aspartate receptors. Proteins in a common signalling pathway appeared to have similar developmental expression profiles that were broadly similar between cats and rodents.

Effects of different forms of monocular deprivation on primary visual cortex maps

Monocular deprivation (MD) by lid suture is one of the classic paradigms for the study of developmental plasticity in the cerebral cortex, and we have detailed knowledge of its anatomical and physiological consequences as well as underlying molecular and cellular mechanisms. However, the effects of other forms of manipulating visual input through one eye on the functional architecture of the primary visual cortex (V1) have not yet been examined directly. We compared MD by lid suture with the effects of daily monocular lens wear using either a frosted lens or a neutral density (ND) filter. We used optical imaging of intrinsic signals and visually evoked potentials (VEPs) to assess responses in V1 to monocular stimulation. We found that loss of stimulus contrast through monocular frosted lens wear resulted in marked takeover of cortical territory by the nondeprived eye (NDE) similar to that caused by classic MD, and in virtual absence of orientation-selective responses following stimulation of the deprived eye (DE). Furthermore, amplitudes of VEPs in response to gratings of a range of spatial frequencies were significantly reduced in the DE compared to the NDE. In contrast, differences in luminance between two eyes caused by an ND filter in front of one eye did not affect ocular dominance and orientation maps, and there was no significant difference in the amplitude of VEPs elicited through the two eyes. Our results are consistent with previous electrophysiological studies in demonstrating that binocular pattern information is necessary to maintain normal functional maps in both eyes, while reduced luminance in one eye has little effect on the overall functional architecture and visual responses in V1.

Link between orientation and retinotopic maps in primary visual cortex

Maps representing the preference of neurons for the location and orientation of a stimulus on the visual field are a hallmark of primary visual cortex. It is not yet known how these maps develop and what function they play in visual processing. One hypothesis postulates that orientation maps are initially seeded by the spatial interference of ON- and OFF-center retinal receptive field mosaics. Here we show that such a mechanism predicts a link between the layout of orientation preferences around singularities of different signs and the cardinal axes of the retinotopic map. Moreover, we confirm the predicted relationship holds in tree shrew primary visual cortex. These findings provide additional support for the notion that spatially structured input from the retina may provide a blueprint for the early development of cortical maps and receptive fields. More broadly, it raises the possibility that spatially structured input from the periphery may shape the organization of primary sensory cortex of other modalities as well.

Online learning and stimulus-driven responses of neurons in visual cortex

In understanding how visual scene is processed in visual cortex, it has been an intriguing problem for theoretical and experimental neuroscientists to examine the relationship between visual stimuli and the induced responses of visual cortex. In particular, it is less explored whether and how the collective responses of visual neurons are patterned to reflect the geometrical regularities. In this paper, through a computation model and statistical analysis, we show that the orientation preference maps induced from correlated visual stimuli exhibit geometrical regularities similar as observed in natural images.

Statistical structure of lateral connections in the primary visual cortex

BACKGROUND

The statistical structure of the visual world offers many useful clues for understanding how biological visual systems may understand natural scenes. One particularly important early process in visual object recognition is that of grouping together edges which belong to the same contour. The layout of edges in natural scenes have strong statistical structure. One such statistical property is that edges tend to lie on a common circle, and this 'co-circularity' can predict human performance at contour grouping. We therefore tested the hypothesis that long-range excitatory lateral connections in the primary visual cortex, which are believed to be involved in contour grouping, display a similar co-circular structure.

RESULTS

By analyzing data from tree shrews, where information on both lateral connectivity and the overall structure of the orientation map was available, we found a surprising diversity in the relevant statistical structure of the connections. In particular, the extent to which co-circularity was displayed varied significantly.

CONCLUSIONS

Overall, these data suggest the intriguing possibility that V1 may contain both co-circular and anti-cocircular connections.

Gaussian process methods for estimating cortical maps

A striking feature of cortical organization is that the encoding of many stimulus features, for example orientation or direction selectivity, is arranged into topographic maps. Functional imaging methods such as optical imaging of intrinsic signals, voltage sensitive dye imaging or functional magnetic resonance imaging are important tools for studying the structure of cortical maps. As functional imaging measurements are usually noisy, statistical processing of the data is necessary to extract maps from the imaging data. We here present a probabilistic model of functional imaging data based on Gaussian processes. In comparison to conventional approaches, our model yields superior estimates of cortical maps from smaller amounts of data. In addition, we obtain quantitative uncertainty estimates, i.e. error bars on properties of the estimated map. We use our probabilistic model to study the coding properties of the map and the role of noise-correlations by decoding the stimulus from single trials of an imaging experiment.