Zhang Sheng, Abbey Craig K, Eckstein Miguel P
Vision & Image Understanding Laboratory, Department of Psychology, University of California, Santa Barbara, California 93106-9660, USA.
Vis Neurosci. 2009 Jan-Feb;26(1):93-108. doi: 10.1017/S0952523809090014. Epub 2009 Mar 12.
The neural mechanisms driving perception and saccades during search use information about the target but are also based on an inhibitory surround not present in the target luminance profile (e.g., Eckstein et al., 2007). Here, we ask whether these inhibitory surrounds might reflect a strategy that the brain has adapted to optimize the search for targets in natural scenes. To test this hypothesis, we sought to estimate the best linear template (behavioral receptive field), built from linear combinations of Gabor channels representing V1 simple cells in search for an additive Gaussian target embedded in natural images. Statistically nonstationary and non-Gaussian properties of natural scenes preclude calculation of the best linear template from analytic expressions and require an iterative optimization method such as a virtual evolution via a genetic algorithm. Evolved linear receptive fields built from linear combinations of Gabor functions include substantial inhibitory surround, larger than those found in humans performing target search in white noise. The inhibitory surrounds were robust to changes in the contrast of the signal, generalized to a larger calibrated natural image data set, and tasks in which the signal occluded other objects in the image. We show that channel nonlinearities can have strong effects on the observed linear behavioral receptive field but preserve the inhibitory surrounds. Together, the results suggest that the apparent suboptimality of inhibitory surrounds in human behavioral receptive fields when searching for a target in white noise might reflect a strategy to optimize detection of signals in natural scenes. Finally, we contend that optimized linear detection of spatially compact signals in natural images might be a new possible hypothesis, distinct from decorrelation of visual input and sparse representations (e.g., Graham et al., 2006), to explain the evolution of center-surround organization of receptive fields in early vision.
在搜索过程中驱动感知和扫视的神经机制利用了有关目标的信息，但也基于目标亮度轮廓中不存在的抑制性周边（例如，Eckstein等人，2007年）。在这里，我们要问这些抑制性周边是否可能反映了大脑为优化在自然场景中搜索目标而采用的一种策略。为了验证这一假设，我们试图估计最佳线性模板（行为感受野），它由代表V1简单细胞的Gabor通道的线性组合构建而成，用于在自然图像中搜索叠加的高斯目标。自然场景的统计非平稳性和非高斯特性使得无法从解析表达式计算最佳线性模板，需要一种迭代优化方法，如通过遗传算法进行虚拟进化。由Gabor函数的线性组合构建的进化线性感受野包括大量的抑制性周边，比在白噪声中执行目标搜索的人类中发现的抑制性周边更大。这些抑制性周边对信号对比度的变化具有鲁棒性，可推广到更大的校准自然图像数据集以及信号遮挡图像中其他物体的任务。我们表明，通道非线性对观察到的线性行为感受野可能有强烈影响，但保留了抑制性周边。总之，结果表明，在白噪声中搜索目标时人类行为感受野中抑制性周边的明显次优性可能反映了一种优化自然场景中信号检测的策略。最后，我们认为，在自然图像中对空间紧凑信号进行优化线性检测可能是一个新的可能假设，与视觉输入的去相关和稀疏表示（例如，Graham等人，2006年）不同，以解释早期视觉中感受野中心-周边组织的进化。
