The statistics of how natural images drive the responses of neurons.
The statistics of how natural images drive the responses of neurons.
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关于自然图像如何驱动神经元反应的统计数据。
DOI：
10.1167/19.13.4
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发表时间：
2019
影响因子：
1.8
通讯作者：
Burge,Johannes
中科院分区：
文献类型：
--
作者：
Iyer,Arvind;Burge,Johannes
为了模拟早期视觉系统中神经元的反应，至少需要三个基本成分：感受野、归一化项和编码噪声的规范。在这里，我们研究了当受到自然图像刺激时，感受野、归一化因子和编码噪声如何影响对模型神经元反应的驱动。我们表明，当这些分量被适当地建模时，自然刺激引起的响应驱动是高斯分布和尺度不变的，并且非常接近于最大化对自然图像区分的灵敏度(d‘)。我们讨论了可以解释这些响应统计的自然刺激的统计模型，并展示了一些常用的建模实践如何扭曲这些结果。最后，我们证明了归一化可以均衡不同刺激类型的神经反应的重要特性。具体地说，窄带(刺激和特征特定的)归一化导致模型神经元在受到自然刺激、1/f噪声刺激和白噪声刺激时产生高斯响应驱动统计。目前的工作提出了最佳做法的建议，并基于对自然刺激的反应统计数据奠定了基础，在此基础上建立了更复杂视觉任务的原则性模型。
To model the responses of neurons in the early visual system, at least three basic components are required: a receptive field, a normalization term, and a specification of encoding noise. Here, we examine how the receptive field, the normalization factor, and the encoding noise affect the drive to model-neuron responses when stimulated with natural images. We show that when these components are modeled appropriately, the response drives elicited by natural stimuli are Gaussian-distributed and scale invariant, and very nearly maximize the sensitivity (d′) for natural-image discrimination. We discuss the statistical models of natural stimuli that can account for these response statistics, and we show how some commonly used modeling practices may distort these results. Finally, we show that normalization can equalize important properties of neural response across different stimulus types. Specifically, narrowband (stimulus-and feature-specific) normalization causes model neurons to yield Gaussian response-drive statistics when stimulated with natural stimuli, 1/f noise stimuli, and white-noise stimuli. The current work makes recommendations for best practices and lays a foundation, grounded in the response statistics to natural stimuli, upon which to build principled models of more complex visual tasks.