Department of Complexity Science and Engineering, The University of Tokyo, Kashiwa, Chiba, Japan.
PLoS One. 2010 Mar 25;5(3):e9704. doi: 10.1371/journal.pone.0009704.
The power law provides an efficient description of amplitude spectra of natural scenes. Psychophysical studies have shown that the forms of the amplitude spectra are clearly related to human visual performance, indicating that the statistical parameters in natural scenes are represented in the nervous system. However, the underlying neuronal computation that accounts for the perception of the natural image statistics has not been thoroughly studied. We propose a theoretical framework for neuronal encoding and decoding of the image statistics, hypothesizing the elicited population activities of spatial-frequency selective neurons observed in the early visual cortex. The model predicts that frequency-tuned neurons have asymmetric tuning curves as functions of the amplitude spectra falloffs. To investigate the ability of this neural population to encode the statistical parameters of the input images, we analyze the Fisher information of the stochastic population code, relating it to the psychophysically measured human ability to discriminate natural image statistics. The nature of discrimination thresholds suggested by the computational model is consistent with experimental data from previous studies. Of particular interest, a reported qualitative disparity between performance in fovea and parafovea can be explained based on the distributional difference over preferred frequencies of neurons in the current model. The threshold shows a peak at a small falloff parameter when the neuronal preferred spatial frequencies are narrowly distributed, whereas the threshold peak vanishes for a neural population with a more broadly distributed frequency preference. These results demonstrate that the distributional property of neuronal stimulus preference can play a crucial role in linking microscopic neurophysiological phenomena and macroscopic human behaviors.
幂律为自然场景的幅度谱提供了有效的描述。心理物理学研究表明，幅度谱的形式与人的视觉表现明显相关，这表明自然场景中的统计参数在神经系统中得到了表示。然而，尚未对解释自然图像统计感知的潜在神经元计算进行深入研究。我们提出了一个用于图像统计的神经元编码和解码的理论框架，假设了在早期视觉皮层中观察到的空间频率选择性神经元的诱发群体活动。该模型预测，频率调谐神经元的调谐曲线作为幅度谱下降的函数具有不对称性。为了研究该神经群体对输入图像统计参数的编码能力，我们分析了随机群体代码的 Fisher 信息，将其与心理物理学测量的人类区分自然图像统计的能力相关联。该计算模型所建议的辨别阈值的性质与先前研究的实验数据一致。特别值得注意的是，基于当前模型中神经元最佳频率的分布差异，可以解释报告的中央凹和旁中心区之间性能的定性差异。当神经元的最佳空间频率分布较窄时，阈值在较小的下降参数处出现峰值，而对于具有更广泛频率偏好的神经元群体，阈值峰值则消失。这些结果表明，神经元刺激偏好的分布特性在将微观神经生理学现象与宏观人类行为联系起来方面起着至关重要的作用。
