The potential importance of saturating and supersaturating contrast response functions in visual cortex.

The potential importance of saturating and supersaturating contrast response functions in visual cortex.
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DOI:
10.1167/7.6.13
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发表时间:
2007-04-30
期刊:
影响因子:
1.8
通讯作者:
Peirce, Jonathan W.
Peirce, Jonathan W.
中科院分区:
医学4区
文献类型:
--
作者:
Peirce, Jonathan W.

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大多数皮层视觉神经元对对比度的反应不是线性的。一般来说,它们对高对比度的刺激表现出饱和反应,这一特征通常以分裂的归一化函数为特征。这种非线性通常被认为在将神经元的动态响应范围集中在对比度空间的特定区域,优化对比度增益方面是有用的。一些神经元不仅饱和,而且过饱和;在高对比度下,神经元的反应降低而不是稳定。在对比度增益控制理论下,这些细胞似乎反映了对神经元提供过度抑制的非最佳归一化池。由于很少有过饱和的数据,本文探讨了这种神经元发生在猕猴视皮层的频率,考虑扩展的Naka-Rushton方程的能力,代表非单调函数。饱和度的增益控制理论的流行已经遮蔽了饱和度的附加计算功能,即,在检测某些特征的结合中。饱和非线性是选择性检测复合刺激的关键部分。在这个角色中,饱和对比度响应函数的存在可能被认为是必要的,而不仅仅是最佳的。
Most cortical visual neurons do not respond linearly with contrast. Generally, they show saturated responses to stimuli of high contrast, a feature often characterized by a divisive normalization function. This nonlinearity is generally thought to be useful in focusing the dynamic response range of the neuron on a particular region of contrast space, optimizing contrast gain. Some neurons not only saturate but also supersaturate; at high contrast, the response of the neuron decreases rather than plateaus. Under the contrast gain control theory, these cells would seem to reflect a nonoptimal normalization pool that provides excessive inhibition to the neurons. Since very few data on supersaturation are available, this article examines the frequency with which such neurons occur in macaque visual cortex by considering an extension of the Naka–Rushton equation with the capacity to represent nonmonotonic functions. The prevalence of gain-control theories for saturation has occluded an additional computational function for saturation, namely, in detecting the conjunction of certain features. A saturating nonlinearity is a critical part of the selective detection of compound stimuli over their components. In this role, the existence of saturating contrast response functions might be considered necessary rather than simply optimal.
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