Connectivity in the cortex is organized at multiple scales 1-5, suggesting that scale-dependent correlated activity is particularly important for understanding the behavior of sensory cortices and their function in stimulus encoding. Here, we analyze the scale-dependent structure of cortical interactions by using maximum entropy models 6-9 to characterize multiple-tetrode recordings from primary visual cortex of anesthetized monkeys (Macaca mulatta). We compare the properties of firing patterns among local clusters of neurons (<300 microns) with neurons separated by larger distances (600-2500 microns). We find that local firing patterns are distinctive: while multi-neuronal firing patterns at larger distances can be predicted by pairwise interactions, patterns within local clusters often show evidence of high-order correlations. Surprisingly, these local correlations are flexible and rapidly reorganized by visual input. While they modestly reduce the amount of information that a cluster conveys, they also modify the format of this information, creating sparser codes by increasing the periods of total quiescence, and concentrating information into briefer periods of common activity. These results imply a hierarchical organization of neuronal correlations: simple pairwise correlations link neurons over scales of tens to hundreds of minicolumns, but on the scale of a few minicolumns, ensembles of neurons form complex subnetworks whose moment-to-moment effective connectivity is dynamically reorganized by the stimulus.

The container relocation problem, where containers that are stored in bays are retrieved in a fixed sequence, is a crucial port operation. Existing approaches using branch and bound algorithms are only able to optimally solve small cases in a practical time frame. In this paper, we investigate iterative deepening A* algorithms (rather than branch and bound) using new lower bound measures and heuristics, and show that this approach is able to solve much larger instances of the problem in a time frame that is suitable for practical application. We also examine a more difficult variant of the problem that has been largely ignored in existing literature.Note to Practitioners-Container retrieval is an important operation in a container port. When a ship arrives, containers stored in the port yard are first retrieved by yard crane, loaded onto autoguided vehicles, transported to quay cranes, and loaded onto the ship by quay crane. Due to various operational constraints, e.g., maintenance of vessel balance and safety issues, the containers in a storage bay are retrieved one by one in a fixed sequence. When the next container to be retrieved is not at the top of its stack, all other containers above it must then be first relocated onto other stacks within the bay. The relocation of a container is a time-consuming operation that essentially dominates all other aspects of the problem, and therefore it is important that the retrieval plan minimizes the number of such relocations. This study proposes a method to generate a near-optimal retrieval plan for yard cranes. This often arises as a subproblem when devising an overall plan for port operations that maximizes throughput, which involves the coordination of multiple pieces of machinery. Our approach produces significantly better results than all existing approaches.Index Terms-Container relocation problem, container yard operation, iterative deepening A*.

Detection of motion is a crucial component of visual processing. To probe the computations underlying motion perception, we created a new class of non-Fourier motion stimuli, characterized by their third-and fourth-order spatiotemporal correlations. As with other non-Fourier stimuli, they lack second-order correlations, and therefore their motion cannot be detected by standard Fourier mechanisms. Additionally, these stimuli lack pairwise spatiotemporal correlation of edges or flickerVand thus, also cannot be detected by extraction of one of these features, followed by standard motion analysis. Nevertheless, many of these stimuli produced apparent motion in human observers. The pattern of responsesVi.e., which specific spatiotemporal correlations led to a percept of motionVwas highly consistent across subjects. For many of these stimuli, inverting the overall contrast of the stimulus reversed the direction of apparent motion. This "reverse-phi" phenomenon challenges existing models, including models that correlate low-level features and gradient models. Our findings indicate that current knowledge of the computations underlying motion processing is as yet incomplete, and that understanding how high-order spatiotemporal correlations lead to motion percepts will illuminate the computations underlying early motion processing.Keywords: motionV2D, computational modeling, isodipole texture Citation: Hu, Q., & Victor, J. D. (2010). A set of high-order spatiotemporal stimuli that elicit motion and reverse-phi percepts.