87,971 research outputs found
How does the Cerebral Cortex Work? Learning, Attention, and Grouping by the Laminar Circuits of Visual Cortex
The organization of neocortex into layers is one of its most salient anatomical features. These layers include circuits that form functional columns in cortical maps. A major unsolved problem concerns how bottom-up, top-down, and horizontal interactions are organized within cortical layers to generate adaptive behaviors. This article models how these interactions help visual co1tex to realize: (I) the binding process whereby cortex groups distributed data into coherent object representations; (2) the attentional process whereby cortex selectively processes important events; and (3) the developmental and learning processes whereby cortex shapes its circuits to match environmental constraints. New computational ideas about feedback systems suggest how neocortex develops and learns in a stable way, and why top-down attention requires converging bottom-up inputs to fully activate cortical cells, whereas perceptual groupings do not.Defense Advanced Research Projects Agency; National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657
Modal and transient dynamics of jet flows
International audienceThe linear stability dynamics of incompressible and compressible isothermal jets are investigated by means of their optimal initial perturbations and of their temporal eigenmodes. The transient growth analysis of optimal perturbations is robust and allows physical interpretation of the salient instability mechanisms. In contrast, the modal representation appears to be inadequate, as neither the computed eigenvalue spectrum nor the eigenmode shapes allow a characterization of the flow dynamics in these settings. More surprisingly, numerical issues also prevent the reconstruction of the dynamics from a basis of computed eigenmodes. An investigation of simple model problems reveals inherent problems of this modal approach in the context of a stable convection-dominated configuration. In particular, eigenmodes may exhibit an exponential growth in the streamwise direction even in regions where the flow is locally stable
A Computational Model of the Short-Cut Rule for 2D Shape Decomposition
We propose a new 2D shape decomposition method based on the short-cut rule.
The short-cut rule originates from cognition research, and states that the
human visual system prefers to partition an object into parts using the
shortest possible cuts. We propose and implement a computational model for the
short-cut rule and apply it to the problem of shape decomposition. The model we
proposed generates a set of cut hypotheses passing through the points on the
silhouette which represent the negative minima of curvature. We then show that
most part-cut hypotheses can be eliminated by analysis of local properties of
each. Finally, the remaining hypotheses are evaluated in ascending length
order, which guarantees that of any pair of conflicting cuts only the shortest
will be accepted. We demonstrate that, compared with state-of-the-art shape
decomposition methods, the proposed approach achieves decomposition results
which better correspond to human intuition as revealed in psychological
experiments.Comment: 11 page
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