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从视觉数据中学习自我中心边界细胞的向量编码。

Learning the Vector Coding of Egocentric Boundary Cells from Visual Data.

机构信息

Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria 3010, Australia

Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Victoria 3010, Australia

出版信息

J Neurosci. 2023 Jul 12;43(28):5180-5190. doi: 10.1523/JNEUROSCI.1071-22.2023. Epub 2023 Jun 7.

Abstract

The use of spatial maps to navigate through the world requires a complex ongoing transformation of egocentric views of the environment into position within the allocentric map. Recent research has discovered neurons in retrosplenial cortex and other structures that could mediate the transformation from egocentric views to allocentric views. These egocentric boundary cells respond to the egocentric direction and distance of barriers relative to an animal's point of view. This egocentric coding based on the visual features of barriers would seem to require complex dynamics of cortical interactions. However, computational models presented here show that egocentric boundary cells can be generated with a remarkably simple synaptic learning rule that forms a sparse representation of visual input as an animal explores the environment. Simulation of this simple sparse synaptic modification generates a population of egocentric boundary cells with distributions of direction and distance coding that strikingly resemble those observed within the retrosplenial cortex. Furthermore, some egocentric boundary cells learnt by the model can still function in new environments without retraining. This provides a framework for understanding the properties of neuronal populations in the retrosplenial cortex that may be essential for interfacing egocentric sensory information with allocentric spatial maps of the world formed by neurons in downstream areas, including the grid cells in entorhinal cortex and place cells in the hippocampus. The computational model presented here demonstrates that the recently discovered egocentric boundary cells in retrosplenial cortex can be generated with a remarkably simple synaptic learning rule that forms a sparse representation of visual input as an animal explores the environment. Additionally, our model generates a population of egocentric boundary cells with distributions of direction and distance coding that strikingly resemble those observed within the retrosplenial cortex. This transformation between sensory input and egocentric representation in the navigational system could have implications for the way in which egocentric and allocentric representations interface in other brain areas.

摘要

使用空间地图在世界中导航需要将自我中心的环境视图不断转换为定位在以自我为中心的地图中。最近的研究发现,内嗅皮层和其他结构中的神经元可以介导从自我中心视图到以自我为中心的视图的转换。这些自我中心边界细胞响应相对于动物视点的障碍物的自我中心方向和距离。这种基于障碍物视觉特征的自我中心编码似乎需要皮质相互作用的复杂动态。然而,这里提出的计算模型表明,自我中心边界细胞可以用一种非常简单的突触学习规则生成,该规则作为动物探索环境时形成视觉输入的稀疏表示。这种简单稀疏突触修改的模拟会生成具有方向和距离编码分布的自我中心边界细胞群体,这些分布与内嗅皮层中观察到的分布非常相似。此外,模型学习的一些自我中心边界细胞仍然可以在没有重新训练的情况下在新环境中发挥作用。这为理解内嗅皮层中神经元群体的特性提供了一个框架,这些特性可能对于将自我中心感觉信息与下游区域(包括内嗅皮层中的网格细胞和海马体中的位置细胞)形成的以自我为中心的空间地图进行接口至关重要。本文提出的计算模型表明,内嗅皮层中最近发现的自我中心边界细胞可以用一种非常简单的突触学习规则生成,该规则作为动物探索环境时形成视觉输入的稀疏表示。此外,我们的模型生成了具有方向和距离编码分布的自我中心边界细胞群体,这些分布与内嗅皮层中观察到的分布非常相似。导航系统中感觉输入和自我中心表示之间的这种转换可能会影响其他大脑区域中自我中心和以自我为中心的表示之间的接口方式。

相似文献

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Learning the Vector Coding of Egocentric Boundary Cells from Visual Data.从视觉数据中学习自我中心边界细胞的向量编码。
J Neurosci. 2023 Jul 12;43(28):5180-5190. doi: 10.1523/JNEUROSCI.1071-22.2023. Epub 2023 Jun 7.

本文引用的文献

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Learning receptive field properties of complex cells in V1.学习 V1 中复杂细胞的感受野特性。
PLoS Comput Biol. 2021 Mar 2;17(3):e1007957. doi: 10.1371/journal.pcbi.1007957. eCollection 2021 Mar.
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Neuronal vector coding in spatial cognition.空间认知中的神经元向量编码。
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Egocentric boundary vector tuning of the retrosplenial cortex.自我中心边界向量调整后扣带回皮层。
Sci Adv. 2020 Feb 21;6(8):eaaz2322. doi: 10.1126/sciadv.aaz2322. eCollection 2020 Feb.
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A sense of space in postrhinal cortex.后眶皮层的空间感。
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Neural correlates of sparse coding and dimensionality reduction.神经稀疏编码和降维的关联。
PLoS Comput Biol. 2019 Jun 27;15(6):e1006908. doi: 10.1371/journal.pcbi.1006908. eCollection 2019 Jun.

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