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Roles of Early Vision for the Dynamics of Border-Ownership Selective Neurons

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6443))

Abstract

The border ownership (BO) that indicates which side of the contour owns the border plays a fundamental role in object perception[1]. The responses of BO-selective cells exhibit rapid transition when a stimulus is fliped along its classical receptive field so that the opposite BO is presented, while the transition is significantly slower when a clear BO is turned into an ambiguous edge such as when a square is enlarged extensively[2]. This phenomenon appears to be a crucial clue for understanding the neural mechanims underlying the credibility of BO. We hypothesize that dynamics of BO-selective cells and networks behind them play a crucial role in the credibility, and that the credibility is related to early visual areas as an appearance of a salient object evokes bottom-up attention. To investigate these hypotheses, we examined the dynamics of BO-selective cells with a computational model that include recurent pathways among V1, V2 and Posterior Parietal (PP) areas[3]. The model cells have been shown to reproduce effects of spatial attention. Simulations of the model exhibited distinct response time depending on the ambiguity of BO, indicating a crucial role of dynamics in the credibility. The recurrent network between PP and V1 appear to play a crucial role for the time course of BO-selective cells that governs simultaneously both credibility of BO and bottom-up attention.

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Wagatsuma, N., Sakai, K. (2010). Roles of Early Vision for the Dynamics of Border-Ownership Selective Neurons. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_13

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  • DOI: https://doi.org/10.1007/978-3-642-17537-4_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17536-7

  • Online ISBN: 978-3-642-17537-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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