Modeling studies have shown that recurrent interactions within neural networks are capable of self-sustaining non-uniform activity profiles. These patterns are thought to be the neural basis of working memory. However, the lack of robustness challenge this view as already small deviations from the assumed interaction symmetry destroy the attractor state. Here we analyze attractor states of a neural field model composed of bistable neurons. We show the existence of self-stabilized patterns that robustly represent the cue position in the presence of a substantial asymmetry in the connection profile. Using approximation techniques we derive an explicit expression for a threshold value describing the transition to a traveling activity wave.
A floorplan is a rectangle envelope subdivided by horizontal and vertical line segments into n nonoverlapping basic rectangles, having given dimensions and relative positioning (i.e. topology). This paper presents a formal algebraic specification ( i n SETS notation) appropriate for VLSI physical design layout, and capable of representing both the floorplan topology as well as the modules ' dimensions. The specification proposed allows a concise and rigorous representation of arbitrarily complex composite floorplans. This algebraic description unifies -under a rotation invariant single expression formalism -slicing and non-slicing generalized wheels jloorplans. A s needed b y specific floorplan algorithms, at supports either a topology dimensionless descripiton or the introduction of module dimensions.Finally, it allows a n eightfold reduction -over previous representations -of the total number offlooplan solutions considered in FP algorithms.
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