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...s riddled with the aperture problem) in addition to aperture problem–free feedback from area MT. Based on physiology and psychophysics (in monkeys and humans, respectively), we hypothesize that the visual system has evolved this way because there are evolutionary benefits to have one visual area (V1) preserve local motion information while another (MT) processes global motion. Local motion may be useful for more than just estimating global motion and solving the aperture problem. For example, a local motion signal from V1 can be used in other early vision tasks, such as optic flow estimation (Beauchemin & Barron, 1995) and image segmentation (Stoner & Albright, 1993). By maintaining both a local and a global registration of motion, the system remains flexible to different types of visual and cognitive tasks without binding itself to a given scale. We note that if the input from MT were modulatory (multiplicative) onto V1, V1 would solve the aperture problem as soon as MT does, which is inconsistent with physiology. The fact that the aperture problem may be solved by fast interlaminar and interareal connections coupled with slower intralaminar and intra-areal sampling serves as a proof of concept for future ...

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...e onset of the moving stimulus, area MT already ‘‘solves’’ the aperture problem (responds to the pattern of motion) while V1 still largely responds to components of motion (Pack & Born, 2001; Pack et al., 2003). Historically, three broad classes of solutions have been proposed to explain how the aperture problem is solved: (a) intersection of constraints, (b) vector averaging of motion direction, and (c) feature tracking. The intersection of constraints method uses the normal components of velocity and predicts the perceived direction of motion from where those velocity-space lines intersect (Adelson & Movshon, 1982). In the vector averaging approach, the ambiguous line segments are perceived to move in a direction consistent with the average of the orthogonal components of the lines (Yo & Wilson, 1992). The unambiguous line ends are summed with varying weights together with ambiguous line segments to simulate the perception of global motion. In these models, cells are typically divided into two classes (terminator units, which can see the line ends, and contour units, which cannot). The aperture problem is solved by setting the weight applied to the terminator units to be larger than that applied to the ...

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...untering the view that early visual areas only process local information, a cell’s response to border ownership was shown to be largely independent of spatial extent and is represented at a single neuron level (Craft, Schuetze, Niebur, & von der Heydt, 2007). Zhou, Friedman, and von der Heydt (2000) showed that as early as V1 18% of the cells responded to border ownership. Moreover, different sized receptive fields in different visual areas suggest that some stimulus properties may be sampled in higher-order areas in parallel with processing at lower areas through fast interareal connections (Bullier, 2001; Girard, Hupeı, & Bullier, 2001). Together, these new pieces of evidence suggest that much of the processing that was previously suggested to occur intra-areally within the same layer of the visual cortex may instead be computed by fast, parallel, bidirectional, interareal and interlaminar connections at different spatial resolutions. In this paper, we explore whether a classic problem in visual motion integration—the aperture problem— can be solved with a simple model that samples the visual scene at different spatial and temporal scales in parallel. To frame what is meant by aperture probl...

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...eal and interlaminar connections between V1 and MT feed back information onto V1, and (c) the computation done in our model areas V1 and MT is essentially identical with the only difference being spatial sampling scales. Henceforth, we use the terms ‘‘spatial sampling scale’’ and ‘‘multiscale sampling’’ to mean the integration of information from neural populations with heterogeneous receptive field sizes wherein some populations have receptive fields as much as an order of magnitude larger than other populations. This type of heterogeneity is well documented in biology (Bolz & Gilbert, 1986; Albright & Desimone, 1987), but its usefulness is underexplored in modeling work. More recently, other models have suggested that multiscale sampling and feedback are the critical components to quickly and successfully solve the aperture problem in area MT (Bayerl & Neumann, 2004). However, it remains to be explained why V1 activity does not look more like MT activity (why it persists to show a component-like direction of motion). In the present work, we propose that this inconsistency with physiology can be addressed with a neural model that distinguishes what feedback layer 6 versus layer 4 of V1 receive from area MT...

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...l sampling scale’’ and ‘‘multiscale sampling’’ to mean the integration of information from neural populations with heterogeneous receptive field sizes wherein some populations have receptive fields as much as an order of magnitude larger than other populations. This type of heterogeneity is well documented in biology (Bolz & Gilbert, 1986; Albright & Desimone, 1987), but its usefulness is underexplored in modeling work. More recently, other models have suggested that multiscale sampling and feedback are the critical components to quickly and successfully solve the aperture problem in area MT (Bayerl & Neumann, 2004). However, it remains to be explained why V1 activity does not look more like MT activity (why it persists to show a component-like direction of motion). In the present work, we propose that this inconsistency with physiology can be addressed with a neural model that distinguishes what feedback layer 6 versus layer 4 of V1 receive from area MT. Moreover, while Bayerl and Neumann propose a modulatory, top-down connection fromMT to V1, our model suggests that the interareal connections must, in fact, be driving with the only gating or modulatory connections coming intraareally from V1 layer 6 to...

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...rection. We developed a neural model showing how the aperture problem can be solved using different spatial sampling scales between LGN, V1 layer 4, V1 layer 6, and area MT. Our results suggest that multiscale sampling, rather than feedback explicitly, is the key process that gives rise to end-stopped cells in V1 and enables area MT to solve the aperture problem without the need for calculating intersecting constraints or crafting intricate patterns of spatiotemporal receptive fields. Furthermore, the model explains why end-stopped cells no longer emerge in the absence of V1 layer 6 activity (Bolz & Gilbert, 1986), why V1 layer 4 cells are significantly more end-stopped than V1 layer 6 cells (Pack, Livingstone, Duffy, & Born, 2003), and how it is possible to have a solution to the aperture problem in area MT with no solution in V1 in the presence of driving feedback. In summary, while much research in the field focuses on how a laminar architecture can give rise to complicated spatiotemporal receptive fields to solve problems in the motion domain, we show that one can reframe motion integration as an emergent property of multiscale sampling achieved concurrently within lamina and across multiple visual...