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我们知道早期视觉系统的功能是什么吗?

Do we know what the early visual system does?

作者信息

Carandini Matteo, Demb Jonathan B, Mante Valerio, Tolhurst David J, Dan Yang, Olshausen Bruno A, Gallant Jack L, Rust Nicole C

机构信息

Smith-Kettlewell Eye Research Institute, San Francisco, California 94115, USA.

出版信息

J Neurosci. 2005 Nov 16;25(46):10577-97. doi: 10.1523/JNEUROSCI.3726-05.2005.

Abstract

We can claim that we know what the visual system does once we can predict neural responses to arbitrary stimuli, including those seen in nature. In the early visual system, models based on one or more linear receptive fields hold promise to achieve this goal as long as the models include nonlinear mechanisms that control responsiveness, based on stimulus context and history, and take into account the nonlinearity of spike generation. These linear and nonlinear mechanisms might be the only essential determinants of the response, or alternatively, there may be additional fundamental determinants yet to be identified. Research is progressing with the goals of defining a single "standard model" for each stage of the visual pathway and testing the predictive power of these models on the responses to movies of natural scenes. These predictive models represent, at a given stage of the visual pathway, a compact description of visual computation. They would be an invaluable guide for understanding the underlying biophysical and anatomical mechanisms and relating neural responses to visual perception.

摘要

一旦我们能够预测对任意刺激(包括自然界中所见的刺激)的神经反应,我们就可以宣称自己了解视觉系统的功能。在早期视觉系统中,基于一个或多个线性感受野的模型有望实现这一目标,只要这些模型包括基于刺激背景和历史来控制反应性的非线性机制,并考虑到动作电位产生的非线性。这些线性和非线性机制可能是反应的唯一关键决定因素,或者,可能还有其他尚未确定的基本决定因素。研究正在朝着为视觉通路的每个阶段定义单一“标准模型”以及测试这些模型对自然场景电影反应的预测能力的目标推进。这些预测模型在视觉通路的给定阶段代表了视觉计算的简洁描述。它们将是理解潜在生物物理和解剖机制以及将神经反应与视觉感知联系起来的宝贵指南。

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本文引用的文献

1
The contrast sensitivity of retinal ganglion cells of the cat.
J Physiol. 1966 Dec;187(3):517-52. doi: 10.1113/jphysiol.1966.sp008107.
2
Predicting neuronal responses during natural vision.
Network. 2005 Jun-Sep;16(2-3):239-60. doi: 10.1080/09548980500464030.
3
The suppressive field of neurons in lateral geniculate nucleus.
J Neurosci. 2005 Nov 23;25(47):10844-56. doi: 10.1523/JNEUROSCI.3562-05.2005.
4
Independence of luminance and contrast in natural scenes and in the early visual system.
Nat Neurosci. 2005 Dec;8(12):1690-7. doi: 10.1038/nn1556. Epub 2005 Nov 13.
5
Cortical sensitivity to visual features in natural scenes.
PLoS Biol. 2005 Oct;3(10):e342. doi: 10.1371/journal.pbio.0030342. Epub 2005 Sep 27.
6
Adaptive stimulus optimization for auditory cortical neurons.
J Neurophysiol. 2005 Dec;94(6):4051-67. doi: 10.1152/jn.00046.2005. Epub 2005 Aug 31.
8
Higher-order thalamic relays burst more than first-order relays.
Proc Natl Acad Sci U S A. 2005 Aug 23;102(34):12236-41. doi: 10.1073/pnas.0502843102. Epub 2005 Aug 11.
9
Dynamic predictive coding by the retina.
Nature. 2005 Jul 7;436(7047):71-7. doi: 10.1038/nature03689.
10
Intracortical origins of interocular suppression in the visual cortex.
J Neurosci. 2005 Jul 6;25(27):6394-400. doi: 10.1523/JNEUROSCI.0862-05.2005.

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