this paper, we propose a Bayesian theory of hierarchical cortical computation based both on (a) the mathematical and computational ideas of computer vision and pattern the- ory and on (b) recent neurophysiological experimental evidence. We ,2 have proposed that Grenander's pattern theory 3 could potentially model the brain as a generafive model in such a way that feedback serves to disambiguate and 'explain away' the earlier representa- tion. The Helmholtz machine 4, 5 was an excellent step towards approximating this proposal, with feedback implementing priors. Its development, however, was rather limited, dealing only with binary images. Moreover, its feedback mechanisms were engaged only during the learning of the feedforward connections but not during perceptual inference, though the Gibbs sampling process for inference can potentially be interpreted as top-down feedback disambiguating low level representations? Rao and Ballard's predictive coding/Kalman filter model 6 did integrate generafive feedback in the perceptual inference process, but it was primarily a linear model and thus severely limited in practical utility. The data-driven Markov Chain Monte Carlo approach of Zhu and colleagues 7, 8 might be the most successful recent application of this proposal in solving real and difficult computer vision problems using generafive models, though its connection to the visual cortex has not been explored. Here, we bring in a powerful and widely applicable paradigm from artificial intelligence and computer vision to propose some new ideas about the algorithms of visual cortical process- ing and the nature of representations in the visual cortex. We will review some of our and others' neurophysiological experimental data to lend support to these ideas

A method of using functional magnetic resonance imaging (fMRI) to measure retinotopic organization within human cortex is described. The method is based on a visual stimulus that creates a traveling wave of neural activity within retinotopically organized visual areas. We measured the fMRI signal caused by this stimulus in visual cortex and represented the results on images of the #attened cortical sheet. We used the method to measure visual areas and to evaluate the spatial precision of fMRI. Specifically, we: 1) identified the borders between several retinotopically organized visual areas in the posterior occipital lobe, 2) measured the function relating cortical position to visual field eccentricity within area V1, 3) localized activity to within 1.1 mm of visual cortex, and 4) estimated the spatial resolution of the fMRI signal and found that signal falls to 60 percent at a spatial frequency of 1 cycle per 9 mm of visual cortex. This spatial resolution is consistent with a linespread w...

We investigated grapheme--colour synaesthesia and found that: (1) The induced colours led to perceptual grouping and pop-out, (2) a grapheme rendered invisible through `crowding' or lateral masking induced synaesthetic colours --- a form of blindsight --- and (3) peripherally presented graphemes did not induce colours even when they were clearly visible. Taken collectively, these and other experiments prove conclusively that synaesthesia is a genuine perceptual phenomenon, not an effect based on memory associations from childhood or on vague metaphorical speech. We identify different subtypes of number--colour synaesthesia and propose that they are caused by hyperconnectivity between colour and number areas at different stages in processing; lower synaesthetes may have cross-wiring (or cross-activation) within the fusiform gyrus, whereas higher synaesthetes may have cross-activation in the angular gyrus. This hyperconnectivity might be caused by a genetic mutation that causes defective pruning of connections between brain maps. The mutation may further be expressed selectively (due to transcription factors) in the fusiform or angular gyri, and this may explain the existence of different forms of synaesthesia. If expressed very diffusely, there may be extensive cross-wiring between brain regions that represent abstract concepts, which would explain the link between creativity, metaphor and synaesthesia (and the higher incidence of synaesthesia among artists and poets). Also, hyperconnectivity between the sensory cortex and amygdala would explain the heightened aversion synaesthetes experience when seeing numbers printed in the `wrong' colour. Lastly, kindling (induced hyperconnectivity in the temporal lobes of temporal lobe epilepsy [TLE] patients) may explain the purp...

Two new techniques for analyzing retinotopic maps— arrow diagrams and visual field sign maps—are demonstrated with a large electrophysiological mapping data set from owl monkey extrastriate visual cortex. An arrow diagram (vectors indicating receptive field centers placed at cortical coordinates) provides a more compact and understandable representation of retinotopy than does a standard receptive field chart (accompanied by a penetration map) or a double contour map (e.g., isoeccentricity and isopolar angle as a function of cortical x, y-coordinates). None of these three representational techniques, however, make separate areas easily visible, especially in data sets containing numerous areas with partial, distorted representations of the visual hemrfield. Therefore, we computed visual field sign maps (non-mirror-image vs mirror-image visual field representation) from the angle between the direction of the cortical gradient in receptive field eccentricity and the cortical gradient in receptive field angle for each small region of the cortex. Visual field sign is a local measure invariant to cortical map orientation and distortion but also to choice of receptive field coordinate system. To estimate the gradients, we first interpolated the eccentricity and polar angle data onto regular grids using a distance-weighted smoothing algorithm. The visual field sign technique provides a more objective method for using retinotopy to outline multiple visual areas. In order to relate these arrow and visual field sign maps accurately to architectonic features visualized in the stained, flattened cortex, we also developed a deformable template algorithm for warping the photograph-derived penetration map using the final observed location of a set of marking lesions.

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Copyright © 2007 by the American Physiological Society. Stereoscopic vision is characterized by greater visual acuity when a background feature serves as a reference. When a reference is present, the perceived depth of an object is predominantly dependent on this reference. Neural representations of stereoscopic depth are expected to have a relative frame of reference. The conversion of absolute disparity encoded in area V1 to relative disparity begins in area V2, although the information encoded in this area appears to be insufficient for stereopsis. This study examines whether relative disparity is encoded in a higher cortical area. We recorded the responses of V4 neurons from macaque monkeys to various combinations of the absolute disparities of two features, the center patch and surrounding annulus of a dynamic random-dot stereogram. We analyzed the effects of the disparity of the surrounding annulus on the tuning for the disparity of the center patch; the tuning curves of relative-disparity-selective neurons for disparities of the center patch should shift with changes in the disparity of the surrounding annulus. Most V4 tuning curves

h the new hemi# eld representation and further anterior in the ventral occipital lobe. Keywords: colour; retinotopy; visual cortex; cerebral achromatopsia; V4; V8 1. INTRODUCTION In his insightful review of the neurological literature on cerebral achromatopsia and colour anomia, Meadows (1974) argued that several cortical regions are essential for colour vision. Figure 1, adapted from Meadows' paper, shows three cortical regions he identi# ed: primary visual cortex, a region on the ventral surface and a region on the dorsal surface near the inferior parietal lobule. Damage to primary visual cortex impairs most forms of conscious vision. For this reason it is not usually counted as a cortical colour specialization. We think, however, that Meadows was correct to list primary visual cortex as an essential component of the colour system. In fact, we think it is useful to go further and remember that colour specialization begins within the retina. The presence of three types of cones,