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Neural encoding of large-scale three-dimensional space—properties and constraints

机译:大型三维空间的神经编码-属性和约束

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摘要

How the brain represents represent large-scale, navigable space has been the topic of intensive investigation for several decades, resulting in the discovery that neurons in a complex network of cortical and subcortical brain regions co-operatively encode distance, direction, place, movement etc. using a variety of different sensory inputs. However, such studies have mainly been conducted in simple laboratory settings in which animals explore small, two-dimensional (i.e., flat) arenas. The real world, by contrast, is complex and three dimensional with hills, valleys, tunnels, branches, and—for species that can swim or fly—large volumetric spaces. Adding an additional dimension to space adds coding challenges, a primary reason for which is that several basic geometric properties are different in three dimensions. This article will explore the consequences of these challenges for the establishment of a functional three-dimensional metric map of space, one of which is that the brains of some species might have evolved to reduce the dimensionality of the representational space and thus sidestep some of these problems.
机译:数十年来,大脑如何代表大范围的可导航空间一直是深入研究的主题,结果发现,皮层和皮层下大脑区域的复杂网络中的神经元可以协同编码距离,方向,位置,运动等。使用各种不同的感觉输入。但是,这类研究主要是在简单的实验室环境中进行的,在这种环境中,动物探索了二维的小型(即平坦)的竞技场。相比之下,现实世界是复杂的三维空间,有丘陵,山谷,隧道,树枝和(对于那些会游泳或飞翔的物种而言)大型的空间空间。在空间上增加额外的维度会增加编码挑战,其主要原因是几个基本几何属性在三个维度上都不同。本文将探讨这些挑战对建立功能性三维空间度量图的后果,其中之一是某些物种的大脑可能已经进化以减少代表性空间的维数,从而回避了其中一些挑战问题。

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