通过上下文反馈调制消除视觉运动的歧义。

Disambiguating visual motion through contextual feedback modulation.

作者信息

Bayerl Pierre, Neumann Heiko

机构信息

Department of Neural Information Processing, University of Ulm, D-89069 Ulm, Germany.

出版信息

Neural Comput. 2004 Oct;16(10):2041-66. doi: 10.1162/0899766041732404.

Abstract

Motion of an extended boundary can be measured locally by neurons only orthogonal to its orientation (aperture problem) while this ambiguity is resolved for localized image features, such as corners or nonocclusion junctions. The integration of local motion signals sampled along the outline of a moving form reveals the object velocity. We propose a new model of V1-MT feedforward and feedback processing in which localized V1 motion signals are integrated along the feedforward path by model MT cells. Top-down feedback from MT cells in turn emphasizes model V1 motion activities of matching velocity by excitatory modulation and thus realizes an attentional gating mechanism. The model dynamics implement a guided filling-in process to disambiguate motion signals through biased on-center, off-surround competition. Our model makes predictions concerning the time course of cells in area MT and V1 and the disambiguation process of activity patterns in these areas and serves as a means to link physiological mechanisms with perceptual behavior. We further demonstrate that our model also successfully processes natural image sequences.

摘要

扩展边界的运动只能由与其方向正交的神经元进行局部测量(孔径问题),而对于局部图像特征(如角点或非遮挡连接点),这种模糊性则得以解决。沿着移动形状的轮廓采样的局部运动信号的整合揭示了物体的速度。我们提出了一种新的V1-MT前馈和反馈处理模型,其中局部V1运动信号在模型MT细胞的前馈路径上进行整合。MT细胞的自上而下反馈反过来通过兴奋性调制强调匹配速度的模型V1运动活动,从而实现一种注意力门控机制。模型动力学通过偏向于中心兴奋、周边抑制的竞争实现一个引导性的填充过程,以消除运动信号的模糊性。我们的模型对MT区和V1区细胞的时间进程以及这些区域活动模式的消除模糊过程进行了预测,并作为将生理机制与感知行为联系起来的一种手段。我们进一步证明,我们的模型也能成功处理自然图像序列。

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