猴子前顶内区在视觉和运动过程中的混合选择性

IF 6.7 2区 医学 Q1 NEUROSCIENCES
Monica Maranesi, Marco Lanzilotto, Edoardo Arcuri, Luca Bonini
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引用次数: 0

摘要

经典研究表明,顶叶内前区(AIP)通过各种神经元类别(如典型神经元、运动神经元和镜像神经元)对特定信息(如物体和自己与他人的行为)进行编码。然而,这些研究通常只关注单一变量,因此还不清楚不同的 AIP 神经元组是编码单一信息还是多种信息,也不清楚多模态编码是如何出现的。在这里,我们对猴子 AIP 神经元在各种任务和条件下进行了长期记录,这些任务和条件通常在单独的实验中使用。大多数细胞对观察到的物体、执行的动作和观察到的动作表现出混合选择性,当这些信息来自猴子的周身工作空间时,选择性会增强。与传统观点不同的是,我们的研究结果表明,AIP 中的多模态编码产生于单个神经元对各种信息的部分混合选择性,这些信息与针对物理对象和其他主体的计划行动相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mixed selectivity in monkey anterior intraparietal area during visual and motor processes

Classical studies suggest that the anterior intraparietal area (AIP) contributes to the encoding of specific information such as objects and actions of self and others, through a variety of neuronal classes, such as canonical, motor and mirror neurons. However, these studies typically focused on a single variable, leaving it unclear whether distinct sets of AIP neurons encode a single or multiple sources of information and how multimodal coding emerges. Here, we chronically recorded monkey AIP neurons in a variety of tasks and conditions classically employed in separate experiments. Most cells exhibited mixed selectivity for observed objects, executed actions, and observed actions, enhanced when this information came from the monkey’s peripersonal working space. In contrast with the classical view, our findings indicate that multimodal coding emerges in AIP from partially-mixed selectivity of individual neurons for a variety of information relevant for planning actions directed to both physical objects and other subjects.

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来源期刊
Progress in Neurobiology
Progress in Neurobiology 医学-神经科学
CiteScore
12.80
自引率
1.50%
发文量
107
审稿时长
33 days
期刊介绍: Progress in Neurobiology is an international journal that publishes groundbreaking original research, comprehensive review articles and opinion pieces written by leading researchers. The journal welcomes contributions from the broad field of neuroscience that apply neurophysiological, biochemical, pharmacological, molecular biological, anatomical, computational and behavioral analyses to problems of molecular, cellular, developmental, systems, and clinical neuroscience.
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