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灵长类动物皮层多感官整合水平上的分裂归一化神经特征。

A Neural Signature of Divisive Normalization at the Level of Multisensory Integration in Primate Cortex.

作者信息

Ohshiro Tomokazu, Angelaki Dora E, DeAngelis Gregory C

机构信息

Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14611, USA; Department of Physiology, Tohoku University School of Medicine, Sendai 980-8575, Japan.

Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA.

出版信息

Neuron. 2017 Jul 19;95(2):399-411.e8. doi: 10.1016/j.neuron.2017.06.043.

Abstract

Studies of multisensory integration by single neurons have traditionally emphasized empirical principles that describe nonlinear interactions between inputs from two sensory modalities. We previously proposed that many of these empirical principles could be explained by a divisive normalization mechanism operating in brain regions where multisensory integration occurs. This normalization model makes a critical diagnostic prediction: a non-preferred sensory input from one modality, which activates the neuron on its own, should suppress the response to a preferred input from another modality. We tested this prediction by recording from neurons in macaque area MSTd that integrate visual and vestibular cues regarding self-motion. We show that many MSTd neurons exhibit the diagnostic form of cross-modal suppression, whereas unisensory neurons in area MT do not. The normalization model also fits population responses better than a model based on subtractive inhibition. These findings provide strong support for a divisive normalization mechanism in multisensory integration.

摘要

传统上,对单神经元多感官整合的研究一直强调描述来自两种感官模态的输入之间非线性相互作用的经验性原理。我们之前提出,这些经验性原理中的许多都可以用在发生多感官整合的脑区中起作用的一种除法归一化机制来解释。这种归一化模型做出了一个关键的诊断性预测:来自一种模态的非偏好性感官输入,其自身能激活神经元,应该会抑制对来自另一种模态的偏好性输入的反应。我们通过记录猕猴颞叶内侧上颞区(MSTd)中整合关于自身运动的视觉和前庭线索的神经元活动来检验这一预测。我们发现,许多MSTd神经元表现出交叉模态抑制的诊断形式,而MT区的单感官神经元则没有。归一化模型对群体反应的拟合也优于基于减法抑制的模型。这些发现为多感官整合中的除法归一化机制提供了有力支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a83/5568912/f50d7b6226d3/nihms889586f1.jpg

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