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Nan Zhang, Rudiger von der Heydt, Fangtu Qiu; Studying the neural mechanisms of visual context integration in border ownership assignment. Journal of Vision 2007;7(9):905. doi: 10.1167/7.9.905.
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© ARVO (1962-2015); The Authors (2016-present)
Neural signals in area V2 carry information about the assignment of edges to figures (border ownership) indicating mechanisms that integrate image context information far beyond the classical receptive fields (CRF). To elucidate the spatial integration structure of these mechanisms we devised a nonlinear reverse correlation method. As in previous studies of border ownership coding (Zhou et al., J Neurosci 20:6594, 2000), one edge of a rectangular figure was placed in the receptive field under study, and the border ownership signal was defined as the difference between the responses to a given local edge when this edge was part of a figure on one side of the receptive field or the other. We then presented the same displays with additively superimposed dynamic binary noise (e.g., 16 by 15 pixels, figure occupying 5 by 5) and computed the spike-triggered sum (STS) of the noise for either location of figure. We expected that each noise pixel would enhance or reduce the visibility of the figure according on its location and contrast. Because border ownership modulation consists in an enhancement of responses for one figure location, or a reduction for the opposite location, we expected that the difference between the STSs for the two figure locations would reveal the features of the figure-ground display that contribute to border ownership modulation. The CRF would cancel in the difference. We estimated the responses of hypothetical filters from the differential STS for each neuron. In a few cells we found significant filter responses, revealing critical features of the figure, such as corners or edges outside the CRF, as well as combinations of features. As yet, we did not find a positive correlation between the filter responses and the border ownership modulation index of the cells.
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