Network model of top-down influences on local gain and contextual interactions in visual cortex

Abstract

The visual system uses continuity as a cue for grouping oriented line segments that define object boundaries in complex visual scenes. Many studies support the idea that long-range intrinsic horizontal connections in early visual cortex contribute to this grouping. Top-down influences in primary visual cortex (V1) play an important role in the processes of contour integration and perceptual saliency, with contour-related responses being task dependent. This suggests an interaction between recurrent inputs to V1 and intrinsic connections within V1 that enables V1 neurons to respond differently under different conditions. We created a network model that simulates parametrically the control of local gain by hypothetical top-down modification of local recurrence. These local gain changes, as a consequence of network dynamics in our model, enable modulation of contextual interactions in a taskdependent manner. Our model displays contour-related facilitation of neuronal responses and differential foreground vs. background responses over the neuronal ensemble, accounting for the perceptual pop-out of salient contours. It quantitatively reproduces the results of single-unit recording experiments in V1, highlighting salient contours and replicating the time course of contextual influences. We show by means of phase-plane analysis that the model operates stably even in the presence of large inputs. Our model shows how a simple form of top-down modulation of the effective connectivity of intrinsic cortical connections among biophysically realistic neurons can account for some of the response changes seen in perceptual learning and task switching.long-range horizontal connections | feedback | reentrant | gating F or a visual system, one of the most important steps in object recognition is to distinguish what belongs to the object and what is part of the background. The borders of most objects consist of smooth edges that for the most part change direction slowly, with only occasional abrupt changes. Local contour elements can thus be integrated along the edge following the rule of "good continuation" discovered by the Gestalt school of psychologists (1).Studies in awake behaving monkeys show that the responses of single neurons in primary visual cortex (V1) to contours embedded in a complex background correlate closely with the animal's performance in detecting the contours (2). Moreover, the contour-related responses change with experience: before training on contour detection the V1 responses contain little information about the camouflaged contour, but after perceptual learning the responses increase in parallel with the animals' improvement in contour detection (3). The contour-related responses are subject to task-specific top-down influences, being strong when the animal performs the contour detection task, much weaker when it performs an irrelevant task, and entirely absent under anesthesia (3).Previous models have attempted to account for some of the mechanisms underlying contour integration in V1 (4-10), but they d...

Interactions between feedback and lateral connections in the primary visual cortex

“…S2). This result supports a neural network model involving gating of V1 lateral interactions through feedback modulation (11)(12)(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 selectively enhance object components and suppress interfering background (20)(21)(22).…”

“…Neurophysiological studies in monkeys have identified that the primary visual cortex (V1) makes a fundamental contribution to contour integration (3)(4)(5)(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)(8)(9)(10). Such intracortical circuitry in V1 has been implemented in many computational models to account for the successful process of contour integration (11)(12)(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)(15)(16)(17).…”

“…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 selectively enhance object components and suppress interfering background (20)(21)(22).…”

Top-down modulation of sensory cortex gates perceptual learning

“…It is important to recognize, however, that changes in bottomup stimulus encoding do not explain all forms of PL (6,56,71,72). For example, Gilbert and colleagues found that while perceptual training on an embedded contour detection task has little effect on basic response properties of monkey V1 neurons, it has a pronounced effect on how V1 neurons are modulated by stimulus context (6,56,71).…”