V1 neurons can be tuned to far (uncrossed), zero or near (crossed) disparities, with a variety of tuning profiles [18,28,39]. Crucially, disparity tuning in V1 corresponds to absolute disparity tuning, but not relative disparity tuning. For example, when a stimulus with constant relative disparity between two parts of a stimulus (e.g. a disc surrounded by an annulus at different depth; figure 2a,c) is positioned at different distances in depth by adding various amounts of absolute disparity to all parts of the stimulus, V1 neurons ignore the constant relative disparity between the stimulus parts and respond based on the absolute disparity of the stimulus component that falls within their classical receptive field [43]. Thus, neurons in V1 are sensitive to absolute, but not relative disparity.
The disparity tuning characteristics of V1 neurons indicate that the initial disparity representation requires further disambiguation for it to be useful for global depth perception. Some of the transformations required to realize this goal are already performed at the next stage of the ventral pathway, area V2. For example, some V2 neurons are tuned to relative disparity, but in general the activity of V2 neurons reflects a mixture of absolute and relative disparity selectivities [52]. Interestingly, a model with a structure akin to the V1 disparity-energy model can generate relative disparity tuning curves similar to those observed in V2 [52]. This suggests that analogous computations, like those in the energy model (i.e. involving a sequence of linear and nonlinear operations), are repeated along the ventral pathway to produce neuronal tuning to increasingly complex 3D objects. Comparable designs have been proposed to explain the increasingly complex 2D-feature tuning in the ventral pathway [53].
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For example, V4 appears to be the first ventral-pathway area in which the stereo-correspondence problem has been partially solved: most disparity-selective V4 neurons are only weakly tuned to disparity in anti-correlated random-dot stereograms [41] (figure 2b, right panel). This finding indicates that V4 neurons are much less sensitive to the disparity of false matches within their receptive field, and respond mostly according to the global depth percept induced by the stimulus. Correspondingly, one study found that performance in a global stereopsis task, in which monkeys judged the relative depth of noisy random-dot stereograms, is impaired after bilateral removal of a part of posterior prestriate cortex that included area V4 [61].
One study offered a clue as to how V4's relative disparity information could lead to behaviorally relevant depth representations. Hinkle & Connor [62] showed that a significant proportion of V4 neurons are not only tuned to the orientation of bar-like stimuli in the fronto-parallel plane (i.e. the 2D image plane), but are also tuned to the orientation in depth of stimuli (defined by horizontal disparity or orientation disparity; [62]). This selectivity, for 3D orientation or slant, was often maintained across different positions in depth of the stimulus.
Disparity processing in the ventral visual pathway of macaques. The information represented by disparity-sensitive neurons in the ventral pathway becomes progressively more complex. Disparity processing originates in V1 where neurons signal absolute disparity. As early as V2, some neurons encode the relative disparity between a centre stimulus and a surrounding annulus (see pictogram below). Apart from relative disparities, neurons in V4 can be tuned to the orientation in depth of a planar stimulus. Neurons in STSv provide information about relative disparity (position in depth) or surface orientation, but also about which smoothly curved 3D shape is presented. Several regions in the ventral pathway may interact (dashed arrows) with regions outside the ventral pathway, such as MT, AIP or VLPFC, to facilitate depth perception. AIP, anterior intraparietal area; VLPFC, ventro-lateral prefrontal cortex.
Along the ventral pathway, the disparity information is combined with depth information from other cues (e.g. texture, shading), eventually producing reasonably cue-invariant 3D-shape representations in IT. One should bear in mind, however, that depth-cue convergence could occur in ventral-pathway regions prior to IT. This can happen in different ways [82]. First, an area might contain neurons that are tuned to depth such that each neuron uses a different depth cue. Second, neurons within an area may encode depth through various depth cues, but with a different tuning for each depth cue. For example, neurons in dorsal-pathway area MT can be selective to 3D-surface orientation (tilt) as defined by velocity, disparity and texture gradients, but their tilt preference across these different depth cues is not correlated [83]. Third, neurons within an area may have a similar depth tuning for different depth cues. This type of depth-cue convergence has been observed in IT (see above) and, to a certain degree, for 3D-orientation tuning in the caudal intraparietal area in the dorsal pathway [84,85]. Importantly, little is known about depth-cue convergence in ventral-pathway areas prior to IT. Findings from one fMRI study suggest that V4 may encode depth from texture or shading in addition to depth from disparity [86], but to our knowledge no study has examined simultaneous tuning for different depth cues at the single-neuron level in V4. Note, however, that depth-cue convergence may not be necessary to achieve a multi-cue depth representation. For instance, it is conceivable that the activity of different neurons (in different areas), each encoding depth using a different depth cue, is combined in a distributed code to represent depth in all its aspects. Hence, more studies are needed to elucidate where and how different depth cues are combined in the ventral pathway and if this convergence facilitates depth encoding.
So now we know, that all successful MIP-solvers are heuristics, tuned to solve more common problems faster and may fail spectacularly on other problems (again: No Free Lunch Theorem). This won't go away given above assumptions. Trying different solvers and tuning different parameters can help (exagerrated: different parameters = different solvers)!
If you're looking to tune in on time for tip off, here's what you need to know: Saturday's UConn vs. Gonzaga game starts at 8:49 p.m. ET over on TBS, and the best way to watch if you haven't got an old-school cable package is with Sling TV.
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Svelte 5 will be a rewrite of the Svelte compiler and runtime. Svelte 4 was mainly about setting the ground for these future improvements by adopting modern tooling and dropping support for some legacy versions of various technologies such as older bundlers. These changes will help us in a number of ways such as being able to more easily compare the Svelte 5 and Svelte 4 codebases and being able to run the existing tests against the new implementation. Svelte 5 will bring major new features and performance improvements to Svelte. The changes are still baking and not quite ready to share yet, but stay tuned!
The tuning properties of V1 neurons (what the neurons respond to) differ greatly over time. Early in time (40 ms and further) individual V1 neurons have strong tuning to a small set of stimuli. That is, the neuronal responses can discriminate small changes in visual orientations, spatial frequencies and colors (as in the optical system of a camera obscura, but projected onto retinal cells of the eye, which are clustered in density and fineness).[14] Each V1 neuron propagates a signal from a retinal cell, in continuation. Furthermore, individual V1 neurons in humans and other animals with binocular vision have ocular dominance, namely tuning to one of the two eyes. In V1, and primary sensory cortex in general, neurons with similar tuning properties tend to cluster together as cortical columns. David Hubel and Torsten Wiesel proposed the classic ice-cube organization model of cortical columns for two tuning properties: ocular dominance and orientation. However, this model cannot accommodate the color, spatial frequency and many other features to which neurons are tuned[citation needed]. The exact organization of all these cortical columns within V1 remains a hot topic of current research. The mathematical modeling of this function has been compared to Gabor transforms.[citation needed]
A theoretical explanation of the computational function of the simple cells in the primary visual cortex has been presented in.[18][19][20] It is described how receptive field shapes similar to those found by the biological receptive field measurements performed by DeAngelis et al.[21][22] can be derived as a consequence of structural properties of the environment in combination with internal consistency requirements to guarantee consistent image representations over multiple spatial and temporal scales. It is also described how the characteristic receptive field shapes, tuned to different scales, orientations and directions in image space, allow the visual system to compute invariant responses under natural image transformations at higher levels in the visual hierarchy.[23][19][20] be457b7860
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