Coding of the contrasts in natural images by visual cortex (V1) neurons: a Bayesian approach
Coding of the contrasts in natural images by visual cortex (V1) neurons: a Bayesian approach
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DOI：
10.1364/josaa.20.001253
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发表时间：
2003-07-01
影响因子：
1.9
通讯作者：
Tolhurst, DJ
中科院分区：
文献类型：
--
作者：
Chirimuuta, M;Clatworthy, PL;Tolhurst, DJ
单个V1神经元仅在有限的刺激对比范围内动态响应，但我们可以在广泛的范围内区分对比。不同的V1神经元覆盖对比度范围的不同部分，它们提供的信息必须以某种方式汇集在一起。我们描述了一个概率池模型，该模型表明，像猫和猴子V1这样具有对比反应的神经元群体，将最准确地编码在自然场景中实际发现的范围内的对比。池化方程类似于贝叶斯方程；然而，在推理中明确包含先验概率只会略微提高编码精度。(C) 2003年美国光学学会。
Individual V1 neurons respond dynamically over only limited ranges of stimulus contrasts, yet we can discriminate contrasts over a wide range. Different V1 neurons cover different parts of the contrast range, and the information they provide must be pooled somehow. We describe a probabilistic pooling model that shows that populations of neurons with contrast responses like those in cat and monkey V1 would most accurately code contrasts in the range actually found in natural scenes. The pooling equation is similar to Bayes's equation; however, explicit inclusion of prior probabilities in the inference increases coding accuracy only slightly. (C) 2003 Optical Society of America.