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时标解耦揭示成年脊髓中稀疏的会聚 CPG 网络。

Decoupling of timescales reveals sparse convergent CPG network in the adult spinal cord.

机构信息

Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200, Copenhagen N, Denmark.

Neuroscience Institute, New York University, New York, NY, 10016, USA.

出版信息

Nat Commun. 2019 Jul 3;10(1):2937. doi: 10.1038/s41467-019-10822-9.

Abstract

During the generation of rhythmic movements, most spinal neurons receive an oscillatory synaptic drive. The neuronal architecture underlying this drive is unknown, and the corresponding network size and sparseness have not yet been addressed. If the input originates from a small central pattern generator (CPG) with dense divergent connectivity, it will induce correlated input to all receiving neurons, while sparse convergent wiring will induce a weak correlation, if any. Here, we use pairwise recordings of spinal neurons to measure synaptic correlations and thus infer the wiring architecture qualitatively. A strong correlation on a slow timescale implies functional relatedness and a common source, which will also cause correlation on fast timescale due to shared synaptic connections. However, we consistently find marginal coupling between slow and fast correlations regardless of neuronal identity. This suggests either sparse convergent connectivity or a CPG network with recurrent inhibition that actively decorrelates common input.

摘要

在产生有节奏的运动期间,大多数脊髓神经元接收振荡的突触驱动。这种驱动的神经元结构尚不清楚,相应的网络规模和稀疏性尚未得到解决。如果输入来自具有密集发散连接的小中央模式发生器 (CPG),它将诱导所有接收神经元的相关输入,而稀疏的会聚布线将诱导弱相关(如果有的话)。在这里,我们使用脊髓神经元的成对记录来测量突触相关性,从而定性地推断出布线结构。慢时标上的强相关性意味着功能相关性和共同来源,由于共享的突触连接,这也将导致快时标上的相关性。然而,无论神经元身份如何,我们始终发现慢和快相关性之间的边缘耦合。这表明稀疏的会聚连接或具有主动去相关共同输入的递归抑制的 CPG 网络。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8684/6610135/310837a6ea02/41467_2019_10822_Fig1_HTML.jpg

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