Receptive fields (RFs) of cortical sensory neurons increase in size along consecutive processing stages. When multiple stimuli are present in a large visual RF, a neuron typically responds to an attended stimulus as if only that stimulus were present. However, the mechanism by which a neuron selectively responds to a subset of its inputs while discarding all others is unknown. Here, we show that neurons can switch between subsets of their afferent inputs by highly specific modulations of interareal gamma-band phase-coherence (PC). We measured local field potentials, single- and multi-unit activity in two male macaque monkeys ( Macaca mulatta ) performing an attention task. Two small stimuli were placed on a screen; the stimuli were driving separate local V1 populations, while both were driving the same local V4 population. In each trial, we cued one of the two stimuli to be attended. We found that gamma-band PC of the local V4 population with multiple subpopulations of its V1 input was differentially modulated. It was high with the input subpopulation representing the attended stimulus, while simultaneously it was very low between the same V4 population and the other input-providing subpopulation representing the irrelevant stimulus. These differential modulations, which depend on stimulus relevance, were also found in the locking of spikes from V4 neurons to the gamma-band oscillations of the V1 input subpopulations. This rapid, highly specific interareal locking provides neurons with a powerful dynamic routing mechanism to select and process only the currently relevant signals.

For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects. We performed experiments where observers detected contours of curvilinearly aligned edge configurations embedded into randomly oriented distractors. The key feature of our framework is to use a generative process for creating the contours, for which it is possible to derive a class of ideal detection models. This allowed us to compare human detection for contours with different statistical properties to the corresponding ideal detection models for the same stimuli. We then subjected the detection models to realistic constraints and required them to reproduce human decisions for every stimulus as well as possible. By independently varying the four model parameters, we identify a single detection model which quantitatively captures all correlations of human decision behaviour for more than 2000 stimuli from 42 contour ensembles with greatly varying statistical properties. This model reveals specific interactions between edges closely matching independent findings from physiology and psychophysics. These interactions imply a statistics of contours for which edge stimuli are indeed optimally integrated by the visual system, with the objective of inferring the presence of contours in cluttered scenes. The recurrent algorithm of our model makes testable predictions about the temporal dynamics of neuronal populations engaged in contour integration, and it suggests a strong directionality of the underlying functional anatomy.

Neurons receive a vast number of synaptic inputs at the same time. To successfully process a relevant piece of information, a neuron needs to select relevant, while suppressing irrelevant signals. In order to dynamically route a meaningful set of signals through processing pathways, neurons need to have a fast and flexible mechanism to change their effective connectivity. Here, we hypothesize that attention changes effective connectivity by modulating the synchrony between neuronal populations.We investigated whether attention dynamically changes effective connectivity between area V4 neurons and their V1 input. Single unit activity, multi unit activity and local field potentials (LFP) were recorded simultaneously from macaque monkeys’ areas V1 and V4. While maintaining their gaze on a fixation spot, the monkeys had to attend to one of two continuously morphing shapes and respond to reoccurrence of the initial sample shape in the attended stream. Size and position of the shapes were adjusted such that both fitted into a single V4 receptive field (RF) while covering two separate, non-overlapping V1 RFs.We found strong phase-coherence of gamma-band LFPs between the V1 populations responding to the attended stimulus and the V4 population. At the same time, V1 populations responding to the unattended stimulus showed only weak LFP phase-coherence with the V4 population. Furthermore, spike-triggered averages (STAs) of at least 40% of the recording pairs revealed that spikes recorded from area V4 were precisely timed with respect to the phase of the gamma oscillation in the upstream area V1. More importantly, almost all these cases showed an increased effect with attention. These results support the hypothesis that attention induces dynamic changes in the effective connectivity between two anatomically hard-wired neuronal populations by selectively modulating synchrony.

Coherent oscillations and synchronous activity are suggested to play an important role in selective processing and dynamic routing of information across the primary visual cortical areas.In this contribution we show that phase coherency between distant recording sites allows to distinguish almost perfectly between two attentional states in a behavioral task, thus giving strong quantitative support for a functional role of oscillatory neural dynamics.

Contour integration is believed to be an important step in human image processing and object recognition, and has been shown to be performed very efficiently by the visual system. However, its neuronal mechanisms are still not well understood. Most contour integration models propose lateral connections between distant orientation columns of similar orientation preference for establishing a so-called 'association field', which links colinearily aligned edge elements into a single contour. However, these models differ both in their dynamics and structure. In some models, afferent input from visual stimuli and lateral input are summed up, in other models these quantities are multiplied. In addition, one finds different assumptions on the range, geometry, and symmetry of the lateral connectivity. It is often assumed that long-range horizontal interactions in V1 serve as the neuronal substrate for the association field. Probabilistic models require unidirectional lateral interactions, linking orientation columns in only one direction, in order to get optimal contour detection performance. In contrast, experimental findings in monkeys rather suggest isotropic connections, spreading symmetrically into all directions from an orientation column. In order to analyze the range and symmetry of lateral interactions underlying contour integration in the human brain, we compared simulations of multiplicative and additive model dynamics with psychophysical contour detection data. For these investigations we used stimuli generated from association fields with different geometries. As expected, models detect contours exceedingly well when using the same association field for contour generation and contour detection. However, analyzing the correlations between human behavior and model prediction on a trial-by-trial basis showed that human behavior is reproduced best, when using the same association field for all contour geometries. Furthermore it turned out that a bidirectional association field reaching only to the nearest neighboring edge elements can not explain the correlations found among the responses of different subjects, while a single unidirectional association field can do so. However, when assuming connections up to the next to-nearest-neighbor elements, a bidirectional association field also explains the correlations between human subjects. The stimuli were designed such that the distance between two neighboring elements lies within the range of long-range connections found in V1. Hence our results allow two possible conclusions: If contour integration relies on horizontal interactions of the spatial range like in V1, a so far unknown unidirectional linking mechanism between neuronal columns is required. If such a unidirectional mechanism does not exist, our results suggest that contour integration is based on interactions on a larger spatial scale as found in higher cortical areas.

Contour integration is a dynamical process which links oriented, local elements into the percept of a coherent contour. Detailed psychophysical studies have described the basic properties of this process. In particular, studies on a contourdetection paradigm where subjects have to find a continuous path of Gabor elements embedded in a background of randomly orientated Gabor elements have demonstrated the impact of several stimulus parameters on contour integration. For example, element distance, alignment of the elements relative to the contour path, or curvature of the contour path itself influence the saliency of a contour [1].