Rich cell-type-specific network topology in neocortical microcircuitry (vol 20, pg 1004, 2017)

被引:81
作者
Gal, Eyal
London, Michael
Globerson, Amir
Ramaswamy, Srikanth
Reimann, Michael W.
Muller, Eilif
Markram, Henry
Segev, Idan
机构
[1] Edmond and Lily Safra Center for Brain Sciences, Hebrew University, Jerusalem
[2] Department of Neurobiology, Hebrew University, Jerusalem
[3] Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv
[4] Sagol School of Neuroscience, Tel Aviv University, Tel Aviv
[5] Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Biotech Campus, Geneva
基金
欧盟地平线“2020”;
关键词
D O I
10.1038/nn.4576
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Uncovering structural regularities and architectural topologies of cortical circuitry is vital for understanding neural computations. Recently, an experimentally constrained algorithm generated a dense network reconstruction of a similar to 0.3-mm(3) volume from juvenile rat somatosensory neocortex, comprising similar to 31,000 cells and similar to 36 million synapses. Using this reconstruction, we found a small-world topology with an average of 2.5 synapses separating any two cells and multiple cell-type-specific wiring features. Amounts of excitatory and inhibitory innervations varied across cells, yet pyramidal neurons maintained relatively constant excitation/inhibition ratios. The circuit contained highly connected hub neurons belonging to a small subset of cell types and forming an interconnected cell-type-specific rich club. Certain three-neuron motifs were overrepresented, matching recent experimental results. Cell-type-specific network properties were even more striking when synaptic strength and sign were considered in generating a functional topology. Our systematic approach enables interpretation of microconnectomics 'big data' and provides several experimentally testable predictions.
引用
收藏
页码:1004 / +
页数:1
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[1]   Rich cell-type-specific network topology in neocortical microcircuitry (vol 20, pg 1004, 2017) [J].
Gal, Eyal ;
London, Michael ;
Globerson, Amir ;
Ramaswamy, Srikanth ;
Reimann, Michael W. ;
Muller, Eilif ;
Markram, Henry ;
Segev, Idan .
NATURE NEUROSCIENCE, 2017, 20 (07) :1004-+