Pattern recognition with figure-ground separation by generation of coherent oscillations

Abstract For elucidating the computation theory underlying flexible recognition of visual patterns by an oscillating neural network of the brain, a model with two dynamical centers was presented and the capability of the model was demonstrated with computer experiments. One of the two dynamical centers, the center for figure organization, organizes figures from elementary visual signals by self-organizing coherent dynamics of neural oscillators, being separated from background represented by incoherent dynamics. The other center, the center for symbol formation, provides symbolic constraints to the dynamics of the former center to define the boundaries of the figures according to memory. Synchronized oscillations also emerge in the latter center by neural oscillators encoding elementary symbols constituting memory items. The linkers connecting the two centers are dynamically gated to generate correspondence between the symbol and the figure. Information circulation emerges between the two centers due to synchronization through linkers. It enables the pattern recognition in the presence of background independent of size, position, and some deformation.

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