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恒河猴MT区反应归一化的对比度依赖性

Contrast dependence of response normalization in area MT of the rhesus macaque.

作者信息

Heuer Hilary W, Britten Kenneth H

机构信息

Center for Neuroscience and Section of Neurobiology, Physiology, and Behavior, University of California, Davis, California 95616.

出版信息

J Neurophysiol. 2002 Dec;88(6):3398-408. doi: 10.1152/jn.00255.2002.

DOI:10.1152/jn.00255.2002
PMID:12466456
Abstract

Contrast normalization is a process whereby responses of neurons are scaled according to the total amount of contrast in a region of the image nearby the receptive field of a neuron. This process allows neurons to code for informative scene or object attributes in a manner unaffected by changes in illumination. Evidence for normalization is seen in striate and extrastriate cortex from experiments where multiple stimuli are presented with a single receptive field (RF). Neuronal responses in such experiments are smaller than that predicted by linear summation, revealing the presence of normalization. While the presence of normalization is often clear, its mechanism is less so. To study the mechanism of normalization, we measured the interaction between pairs of brief local stimuli (spatial Gabor functions) within the RFs of cells in the middle temporal (MT or V5) area of monkeys and varied both the location and contrast of the stimuli. We found response summed approximately linearly when contrast was low but rapidly became normalized as stimulus contrast increased. The rapid transition to effective normalization at low contrasts suggested cooperativity in the normalization, and a model embodying such a cooperative step provided a good account of our data.

摘要

对比度归一化是一个过程,通过该过程,神经元的反应会根据神经元感受野附近图像区域中的总对比度进行缩放。这个过程使神经元能够以不受光照变化影响的方式对信息丰富的场景或物体属性进行编码。在纹状皮层和纹外皮层中,通过在单个感受野(RF)呈现多个刺激的实验可以看到归一化的证据。在这类实验中,神经元反应比线性求和预测的要小,这揭示了归一化的存在。虽然归一化的存在通常很明显,但其机制却不那么清晰。为了研究归一化的机制,我们测量了猴子颞中区(MT或V5区)细胞感受野内成对的短暂局部刺激(空间Gabor函数)之间的相互作用,并改变了刺激的位置和对比度。我们发现,当对比度较低时,反应近似线性相加,但随着刺激对比度增加,反应迅速归一化。在低对比度下迅速过渡到有效的归一化表明归一化过程中存在协同作用,一个体现这种协同步骤的模型很好地解释了我们的数据。

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