This Feature Article is part of a series identified by the Editorial Board as reporting findings of exceptional significance.Edited by Terrence J. Sejnowski, Salk Institute for Biological Studies, La Jolla, CA, and approved August 8, 2014 (received for review February 22, 2014) Cognitive functions rely on the coordinated activity of neurons in many brain regions, but the interactions between cortical areas are not yet well understood. Here we investigated whether lowfrequency (α) and high-frequency (γ) oscillations characterize different directions of information flow in monkey visual cortex. We recorded from all layers of the primary visual cortex (V1) and found that γ-waves are initiated in input layer 4 and propagate to the deep and superficial layers of cortex, whereas α-waves propagate in the opposite direction. Simultaneous recordings from V1 and downstream area V4 confirmed that γ-and α-waves propagate in the feedforward and feedback direction, respectively. Microstimulation in V1 elicited γ-oscillations in V4, whereas microstimulation in V4 elicited α-oscillations in V1, thus providing causal evidence for the opposite propagation of these rhythms. Furthermore, blocking NMDA receptors, thought to be involved in feedback processing, suppressed α while boosting γ. These results provide new insights into the relation between brain rhythms and cognition.neuronal synchronization | attention | perceptual organization | phase coherence | Granger causality A reas of the visual cortex are arranged hierarchically, with low-level areas representing simple features and higher areas representing the more complex aspects of the visual world (1, 2). Neurons in many visual areas are coactive during the perception of a visual stimulus and it is difficult to disentangle the influences of lower areas onto higher areas from the effects that go in the opposite direction (3). Studies of visual cognition could benefit enormously from markers of cortical activity that distinguish between feedforward and feedback effects. One such putative marker is cortical oscillatory activity, because oscillations of different frequencies have been proposed to propagate either in feedforward or in the feedback direction (4, 5), but experimental evidence for this view is sparse (6).Low-frequency rhythms, like the α-rhythm-which is particularly pronounced in the visual cortex-have been proposed to characterize spontaneous activity (7,8) as the α-rhythm increases when the subject closes the eyes (9). More recent observations have also implicated α-oscillations in the active suppression of irrelevant, unattended information (10, 11). In contrast, the high-frequency γ-rhythm increases if visual stimuli are presented, and in particular if they are task-relevant (12, 13). One influential hypothesis has been that γ-oscillations play a role in feature binding (14), but later studies cast doubt on this proposal (15,16). A more recent hypothesis holds that γ-oscillations facilitate the communication between cortical areas (17), but both evidence in fa...

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The present results reveal unique contributions of the different cortical layers to the formation of a visual percept. This new blueprint of laminar processing may generalize to other tasks and to other areas of the cerebral cortex, where the layers are likely to have roles similar to those in area V1.

In many robotic applications the need to rep resent and reason about spatial relationships is of great importance. However, our knowledge of par ticular spatial relationships is inherently uncertain. The most used method for handling the uncertainty is to "pre-engineer" the problem away, by structur ing the working environment and using specially suited high-precision equipment. In some advanced robotic research domains, however, such as auto matic task planning, off-line robot programming, 267 and autonomous vehicle operation, prior structur ing will not be possible, because of dynamically changing environments, or because of the demand for greater reasoning flexibility. Spatial reasoning is further complicated because relationships are often not described explicitly, but are given by uncertain relative information. This is particularly true when many different frames of reference are used, produc ing a network of uncertain relationships. Rather than treat spatial uncertainty as a side issue in geo metrical reasoning, we believe it must be an intrin sic part of spatial representations. In this paper, we describe a representation for spatial informa tion, called the stochastic map, and associated pro cedures for building it, reading information from it, and revising it incrementally as new information is obtained. The map always contains the best esti mates of relationships among objects in the map, and their uncertainties. The procedures provide a general solution to the problem of estimating un certain relative spatial relationships. The estimates are probabilistic in nature, an advance over the pre vious, very conservative, worst-case approaches to the problem. Finally, the procedures are developed in the context of state-estimation and filtering the ory, which provides a solid basis for numerous ex tensions.