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

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 task-dependent 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.

Perceptual learning and adult cortical plasticity.

The visual cortex retains the capacity for experience-dependent changes, or plasticity, of cortical function and cortical circuitry, throughout life. These changes constitute the mechanism of perceptual learning in normal visual experience and in recovery of function after CNS damage. Such plasticity can be seen at multiple stages in the visual pathway, including primary visual cortex. The manifestation of the functional changes associated with perceptual learning involve both long term modification of cortical circuits during the course of learning, and short term dynamics in the functional properties of cortical neurons. These dynamics are subject to top-down influences of attention, expectation and perceptual task. As a consequence, each cortical area is an adaptive processor, altering its function in accordance to immediate perceptual demands.

High-resolution fMRI: overcoming the signal-to-noise problem.

Increasing the spatial resolution in functional Magnetic Resonance Imaging (fMRI) inherently lowers the signal-to-noise ratio (SNR). In order to still detect functionally significant activations in high-resolution images, spatial smoothing of the data is required. However, conventional non-adaptive smoothing comes with a reduced effective resolution, foiling the benefit of the higher acquisition resolution. We show how our recently proposed structural adaptive smoothing procedure for functional MRI data can improve signal detection of high-resolution fMRI experiments regardless of the lower SNR. The procedure is evaluated on human visual and sensory-motor mapping experiments. In these applications, the higher resolution could be fully utilized and high-resolution experiments were outperforming normal resolution experiments by means of both statistical significance and information content.

Learning to link visual contours.

In complex visual scenes, linking related contour elements is important for object recognition. This process, thought to be stimulus driven and hard wired, has substrates in primary visual cortex (V1). Here, however, we find contour integration in V1 to depend strongly on perceptual learning and top-down influences that are specific to contour detection. In naive monkeys, the information about contours embedded in complex backgrounds is absent in V1 neuronal responses and is independent of the locus of spatial attention. Training animals to find embedded contours induces strong contour-related responses specific to the trained retinotopic region. These responses are most robust when animals perform the contour detection task but disappear under anesthesia. Our findings suggest that top-down influences dynamically adapt neural circuits according to specific perceptual tasks. This may serve as a general neuronal mechanism of perceptual learning and reflect top-down mediated changes in cortical states.

Contour saliency in primary visual cortex.

Contour integration is an important intermediate stage of object recognition, in which line segments belonging to an object boundary are perceptually linked and segmented from complex backgrounds. Contextual influences observed in primary visual cortex (V1) suggest the involvement of V1 in contour integration. Here, we provide direct evidence that, in monkeys performing a contour detection task, there was a close correlation between the responses of V1 neurons and the perceptual saliency of contours. Receiver operating characteristic analysis showed that single neuronal responses encode the presence or absence of a contour as reliably as the animal's behavioral responses. We also show that the same visual contours elicited significantly weaker neuronal responses when they were not detected in the detection task, or when they were unattended. Our results demonstrate that contextual interactions in V1 play a pivotal role in contour integration and saliency.

Perceptual learning and top-down influences in primary visual cortex.

Neuronal responses at early stages in visual cortical processing, including those in primary visual cortex (V1), are subject to the influences of visual context, experience and attention. Here we show that for monkeys trained in a shape discrimination task, V1 neurons took on novel functional properties related to the attributes of the trained shapes. Furthermore, these properties depended on the perceptual task being performed; neurons responded very differently to an identical visual stimulus under different visual discrimination tasks. These top-down influences were seen from the very beginning and throughout the entire time course of the neural responses. Information theoretic analysis showed that neurons carried more information about a stimulus attribute when the animals were performing a task related to that attribute. Our findings suggest that the output from V1 reflects both sensory and behavioral context.