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MT神经元中双眼运动整合模型

A Model of Binocular Motion Integration in MT Neurons.

作者信息

Baker Pamela M, Bair Wyeth

机构信息

Department of Biological Structure, Washington National Primate Research Center, University of Washington, Seattle, Washington 98195

Department of Biological Structure, Washington National Primate Research Center, University of Washington, Seattle, Washington 98195.

出版信息

J Neurosci. 2016 Jun 15;36(24):6563-82. doi: 10.1523/JNEUROSCI.3213-15.2016.

Abstract

UNLABELLED

Primate cortical area MT plays a central role in visual motion perception, but models of this area have largely overlooked the binocular integration of motion signals. Recent electrophysiological studies tested binocular integration in MT and found surprisingly that MT neurons lose their hallmark "pattern motion" selectivity when stimuli are presented dichoptically and that many neurons are selective for motion-in-depth (MID). By unifying these novel observations with insights from monocular, frontoparallel motion studies concurrently in a binocular MT motion model, we generated clear, testable predictions about the circuitry and mechanisms underlying visual motion processing. We built binocular models in which signals from left- and right-eye streams could be integrated at various stages from V1 to MT, attempting to create the simplest plausible circuits that accounted for the physiological range of pattern motion selectivity, that explained changes across this range for dichoptic stimulus presentation, and that spanned the spectrum of MID selectivity observed in MT. Our successful models predict that motion-opponent suppression is the key mechanism to account for the striking loss of pattern motion sensitivity with dichoptic plaids, that opponent suppression precedes binocular integration, and that opponent suppression will be stronger in inputs to pattern cells than to component cells. We also found an unexpected connection between circuits for pattern motion selectivity and MID selectivity, suggesting that these two separately studied phenomena could be related. These results also hold in models that include binocular disparity computations, providing a platform for future exploration of binocular response properties in MT.

SIGNIFICANCE STATEMENT

The neural pathways underlying our sense of visual motion are among the most studied and well-understood parts of the primate cerebral cortex. Nevertheless, our understanding is incomplete because electrophysiological research has focused mainly on motion in the 2D frontoparallel plane, even though real-world motion often occurs in three dimensions, involving a change in distance from the viewer. Recent studies have revealed a specialization for sensing 3D motion in area MT, the cortical area most tightly linked to the processing and perception of visual motion. Our study provides the first model to explain how 3D motion sensitivity can arise in MT neurons and predicts how essential features of 2D motion integration may relate to 3D motion processing.

摘要

未标注

灵长类动物的视皮层MT区在视觉运动感知中起核心作用,但该区域的模型在很大程度上忽略了运动信号的双眼整合。最近的电生理研究对视皮层MT区的双眼整合进行了测试,令人惊讶地发现,当以双眼分视方式呈现刺激时,MT神经元失去了其标志性的“模式运动”选择性,并且许多神经元对深度运动(MID)具有选择性。通过在双眼MT运动模型中同时将这些新观察结果与单眼、额面平行运动研究的见解相结合,我们对视觉运动处理的电路和机制产生了清晰、可测试的预测。我们构建了双眼模型,其中来自左眼和右眼信息流的信号可以在从V1到MT的各个阶段进行整合,试图创建最简单合理的电路,该电路能够解释模式运动选择性的生理范围,解释双眼分视刺激呈现时该范围内的变化,并涵盖在MT区观察到的深度运动选择性的范围。我们成功的模型预测,运动拮抗抑制是解释双眼分视格子图案时模式运动敏感性显著丧失的关键机制,拮抗抑制先于双眼整合,并且在模式细胞的输入中,拮抗抑制将比在成分细胞中更强。我们还发现了模式运动选择性和深度运动选择性电路之间的意外联系,表明这两个分别研究的现象可能相关。这些结果在包括双眼视差计算的模型中也成立,为未来探索视皮层MT区的双眼反应特性提供了一个平台。

意义声明

我们视觉运动感知的神经通路是灵长类动物大脑皮层中研究最多、理解最透彻的部分之一。然而,我们的理解并不完整,因为电生理研究主要集中在二维额面平行平面中的运动,尽管现实世界中的运动通常发生在三维空间中,涉及与观察者距离的变化。最近的研究揭示了视皮层MT区在感知三维运动方面的特化,MT区是与视觉运动处理和感知联系最紧密的皮层区域。我们的研究提供了第一个模型,解释了MT神经元如何产生三维运动敏感性,并预测了二维运动整合的基本特征可能如何与三维运动处理相关。

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本文引用的文献

1
Area MT encodes three-dimensional motion.体感区 MT 编码三维运动。
J Neurosci. 2014 Nov 19;34(47):15522-33. doi: 10.1523/JNEUROSCI.1081-14.2014.
2
Neural representation of motion-in-depth in area MT.MT 区中深度运动的神经表示。
J Neurosci. 2014 Nov 19;34(47):15508-21. doi: 10.1523/JNEUROSCI.1072-14.2014.
3
Neural population models for perception of motion in depth.用于深度运动感知的神经群体模型。
Vision Res. 2014 Aug;101:11-31. doi: 10.1016/j.visres.2014.04.014. Epub 2014 May 9.
10
The role of V1 surround suppression in MT motion integration.V1 周边抑制在 MT 运动整合中的作用。
J Neurophysiol. 2010 Jun;103(6):3123-38. doi: 10.1152/jn.00654.2009. Epub 2010 Mar 24.

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