Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex

被引:148
作者
Billeh, Yazan N. [1 ]
Cai, Binghuang [1 ]
Gratiy, Sergey L. [1 ]
Dai, Kael [1 ]
Iyer, Ramakrishnan [1 ]
Gouwens, Nathan W. [1 ]
Abbasi-Asl, Reza [1 ,2 ]
Jia, Xiaoxuan [1 ]
Siegle, Joshua H. [1 ]
Olsen, Shawn R. [1 ]
Koch, Christof [1 ]
Mihalas, Stefan [1 ]
Arkhipov, Anton [1 ]
机构
[1] Allen Inst Brain Sci, Seattle, WA 98109 USA
[2] Univ Calif San Francisco, Dept Neurol, Weill Inst Neurosci, San Francisco, CA USA
关键词
LOCAL-FIELD POTENTIALS; INHIBITORY NEURONS; LAYER; 4; CANONICAL MICROCIRCUIT; GABAERGIC INTERNEURONS; CORTICAL MICROCIRCUITS; SYNAPTIC CONNECTIVITY; RESPONSE PROPERTIES; SPATIAL SUMMATION; RECEPTIVE-FIELDS;
D O I
10.1016/j.neuron.2020.01.040
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Structural rules underlying functional properties of cortical circuits are poorly understood. To explore these rules systematically, we integrated information from extensive literature curation and large-scale experimental surveys into a data-driven, biologically realistic simulation of the awake mouse primary visual cortex. The model was constructed at two levels of granularity, using either biophysically detailed or point neurons. Both variants have identical network connectivity and were compared to each other and to experimental recordings of visual-driven neural activity. While tuning these networks to recapitulate experimental data, we identified rules governing cell-class-specific connectivity and synaptic strengths. These structural constraints constitute hypotheses that can be tested experimentally. Despite their distinct single-cell abstraction, both spatially extended and point models perform similarly at the level of firing rate distributions for the questions we investigated. All data and models are freely available as a resource for the community.
引用
收藏
页码:388 / +
页数:34
相关论文
共 117 条
[1]   A neural circuit for spatial summation in visual cortex [J].
Adesnik, Hillel ;
Bruns, William ;
Taniguchi, Hiroki ;
Huang, Z. Josh ;
Scanziani, Massimo .
NATURE, 2012, 490 (7419) :226-231
[2]   An efficient analytical reduction of detailed nonlinear neuron models [J].
Amsalem, Oren ;
Eyal, Guy ;
Rogozinski, Noa ;
Gevaert, Michael ;
Kumbhar, Pramod ;
Schuermann, Felix ;
Segev, Idan .
NATURE COMMUNICATIONS, 2020, 11 (01)
[3]   The Human Brain Project: Creating a European Research Infrastructure to Decode the Human Brain [J].
Amunts, Katrin ;
Ebell, Christoph ;
Muller, Jeff ;
Telefont, Martin ;
Knoll, Alois ;
Lippert, Thomas .
NEURON, 2016, 92 (03) :574-581
[4]  
[Anonymous], 2018, BIORXIV
[5]  
Antolik J., 2023, BIORXIV, DOI [10.1101/416156, DOI 10.1101/416156]
[6]   The Temporal Tuning of the Drosophila Motion Detectors Is Determined by the Dynamics of Their Input Elements [J].
Arenz, Alexander ;
Drews, Michael S. ;
Richter, Florian G. ;
Ammer, Georg ;
Borst, Alexander .
CURRENT BIOLOGY, 2017, 27 (07) :929-944
[7]   Visual physiology of the layer 4 cortical circuit in silico [J].
Arkhipov, Anton ;
Gouwens, Nathan W. ;
Billeh, Yazan N. ;
Gratiy, Sergey ;
Iyer, Ramakrishnan ;
Wei, Ziqiang ;
Xu, Zihao ;
Abbasi-Asl, Reza ;
Berg, Jim ;
Buice, Michael ;
Cain, Nicholas ;
da Costa, Nuno ;
de Vries, Saskia ;
Denman, Daniel ;
Durand, Severine ;
Feng, David ;
Jarsky, Tim ;
Lecoq, Jerome ;
Lee, Brian ;
Li, Lu ;
Mihalas, Stefan ;
Ocker, Gabriel K. ;
Olsen, Shawn R. ;
Reid, R. Clay ;
Soler-Llavina, Gilberto ;
Sorensen, Staci A. ;
Wang, Quanxin ;
Waters, Jack ;
Scanziani, Massimo ;
Koch, Christof .
PLOS COMPUTATIONAL BIOLOGY, 2018, 14 (11)
[8]   Two dynamically distinct inhibitory networks in layer 4 of the neocortex [J].
Beierlein, M ;
Gibson, JR ;
Connors, BW .
JOURNAL OF NEUROPHYSIOLOGY, 2003, 90 (05) :2987-3000
[9]   Short-term dynamics of thalamocortical and intracortical synapses onto layer 6 neurons in neocortex [J].
Beierlein, M ;
Connors, BW .
JOURNAL OF NEUROPHYSIOLOGY, 2002, 88 (04) :1924-1932
[10]  
Billeh YN., 2019, bioRxiv, P826701, DOI [10.1101/826701, DOI 10.1101/826701]