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分布式电路的缩放原理。

Scaling Principles of Distributed Circuits.

机构信息

Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Kavli Institute for Brain and Mind, University of California, San Diego, CA 92093, USA.

出版信息

Curr Biol. 2019 Aug 5;29(15):2533-2540.e7. doi: 10.1016/j.cub.2019.06.046. Epub 2019 Jul 18.

Abstract

Identifying shared quantitative features of a neural circuit across species is important for 3 reasons. Often expressed in the form of power laws and called scaling relationships [1, 2], they reveal organizational principles of circuits, make insights gleaned from model systems widely applicable, and explain circuit performance and function, e.g., visual circuits [3, 4]. The visual circuit is topographic [5, 6], wherein retinal neurons target and activate predictable spatial loci in primary visual cortex. The brain, however, contains many circuits, where neuronal targets and activity are unpredictable and distributed throughout the circuit, e.g., olfactory circuits, in which glomeruli (or mitral cells) in the olfactory bulb synapse with neurons distributed throughout the piriform cortex [7-10]. It is unknown whether such circuits, which we term distributed circuits, are scalable. To determine whether distributed circuits scale, we obtained quantitative descriptions of the olfactory bulb and piriform cortex in six mammals using stereology techniques and light microscopy. Two conserved features provide evidence of scalability. First, the number of piriform neurons n and bulb glomeruli g scale as n∼g. Second, the average number of synapses between a bulb glomerulus and piriform neuron is invariant at one. Using theory and modeling, we show that these two features preserve the discriminatory ability and precision of odor information across the olfactory circuit. As both abilities depend on circuit size, manipulating size provides evolution with a way to adapt a species to its niche without designing developmental programs de novo. These principles might apply to other distributed circuits like the hippocampus.

摘要

确定跨物种神经回路的共享定量特征很重要,原因有三。这些特征通常以幂律的形式表示,称为缩放关系[1,2],它们揭示了回路的组织原则,使从模型系统中获得的见解得到广泛应用,并解释了回路的性能和功能,例如视觉回路[3,4]。视觉回路具有拓扑结构[5,6],其中视网膜神经元以初级视觉皮层中的可预测空间位置为靶点并激活这些位置。然而,大脑包含许多神经元靶点和活动不可预测且分布在整个回路中的回路,例如嗅觉回路,其中嗅球中的神经节(或僧帽细胞)与分布在梨状皮层中的神经元形成突触[7-10]。我们尚不清楚这些我们称之为分布式回路的回路是否具有可扩展性。为了确定分布式回路是否可扩展,我们使用体视学技术和光学显微镜对六种哺乳动物的嗅球和梨状皮层进行了定量描述。两个保守特征为可扩展性提供了证据。首先,梨状皮层神经元 n 和嗅球神经节 g 的数量呈 n∼g 关系。其次,嗅球神经节和梨状皮层神经元之间的平均突触数为 1。我们利用理论和模型表明,这两个特征在整个嗅觉回路中保持了气味信息的辨别能力和精度不变。由于这两种能力都取决于回路的大小,因此操纵大小为物种适应其生态位提供了一种方法,而无需从头设计发育程序。这些原则可能适用于其他分布式回路,如海马体。

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