The traditional view on visual processing emphasizes a hierarchy: local line segments are first linked into global contours, which in turn are assembled into more complex forms. Distinct from this bottom-up viewpoint, here we provide evidence for a theoretical framework whereby objects and their parts are processed almost concurrently in a bidirectional cortico-cortical loop. By simultaneous recordings from V1 and V4 in awake monkeys, we found that information about global contours in a cluttered background emerged initially in V4, started ∼40 ms later in V1, and continued to develop in parallel in both areas. Detailed analysis of neuronal response properties implicated contour integration to emerge from both bottom-up and reentrant processes. Our results point to an incremental integration mechanism: feedforward assembling accompanied by feedback disambiguating to define and enhance the global contours and to suppress background noise. The consequence is a parallel accumulation of contour information over multiple cortical areas.

Perceptual learning substantially improves visual discrimination and detection ability, which has been associated with visual cortical plasticity. However, little is known about the dynamic changes in neuronal response properties over the course of training. Using chronically implanted multielectrode arrays, we were able to capture day-by-day spatiotemporal dynamics of neurons in the primary visual cortex (V1) of monkeys trained to detect camouflaged visual contours. We found progressive strengthening and accelerating in both facilitation of neurons encoding the contour elements and suppression of neurons responding to the background components. The enhancement of this figure-ground contrast in V1 was closely correlated with improved behavioral performance on a daily basis. Decoding accuracy of a simple linear classifier based on V1 population responses also paralleled the animal's behavioral changes. Our results indicate that perceptual learning shapes the V1 population code to allow a more efficient readout of task-relevant information.

Perceptual grouping of line segments into object contours has been thought to be mediated, in part, by long-range horizontal connectivity intrinsic to the primary visual cortex (V1), with a contribution by top-down feedback projections. To dissect the contributions of intraareal and interareal connections during contour integration, we applied conditional Granger causality analysis to assess directional influences among neural signals simultaneously recorded from visual cortical areas V1 and V4 of monkeys performing a contour detection task. Our results showed that discounting the influences from V4 markedly reduced V1 lateral interactions, indicating dependence on feedback signals of the effective connectivity within V1. On the other hand, the feedback influences were reciprocally dependent on V1 lateral interactions because the modulation strengths from V4 to V1 were greatly reduced after discounting the influences from other V1 neurons. Our findings suggest that feedback and lateral connections closely interact to mediate image grouping and segmentation.perceptual grouping | contour integration | Granger causality | horizontal connection | feedback connection A key step in the visual system's analysis of object shape is to group line segments into global contours and segregate these contours from background features. This process is critical to identifying object boundaries in complex visual scenes, and thus particularly important for performing shape discrimination; image segmentation; and, ultimately, object recognition.Contour integration follows the Gestalt principle of good continuation (1). The underlying neural underpinnings have been characterized as an association field (2), which links contour elements that are part of smooth contours. Neurophysiological studies in monkeys have identified that the primary visual cortex (V1) makes a fundamental contribution to contour integration (3-6), and anatomical studies have shown that the topology of horizontal connections in V1 is well suited for mediating interactions between neurons with a similar orientation preference (7-10). Such intracortical circuitry in V1 has been implemented in many computational models to account for the successful process of contour integration (11-13). Although many lines of converging evidence suggest that V1 is intimately involved in contour integration, circuitbased models have to take into account the findings that contour grouping is more than a bottom-up or hard-wired process, but that it is strongly dependent on top-down feedback influences (5, 14-17). Surface segmentation, another important intermediate stage in processing of visual images, is also mediated by interactions between feedforward and feedback connections (18).We have proposed a model whereby cortical feedback contributes to the effective connectivity of horizontal connections within V1 (13,19). A possible role of higher cortical areas in this process is to disambiguate local image components by creating a template that is fed back to V1, which then can select...