2018
DOI: 10.7554/elife.37349
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Sparse recurrent excitatory connectivity in the microcircuit of the adult mouse and human cortex

Abstract: Generating a comprehensive description of cortical networks requires a large-scale, systematic approach. To that end, we have begun a pipeline project using multipatch electrophysiology, supplemented with two-photon optogenetics, to characterize connectivity and synaptic signaling between classes of neurons in adult mouse primary visual cortex (V1) and human cortex. We focus on producing results detailed enough for the generation of computational models and enabling comparison with future studies. Here, we rep… Show more

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Cited by 174 publications
(205 citation statements)
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References 73 publications
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“…The bottom rows of Figures 3B-E show the dependence of the mean positive (red lines) and negative (blue lines) synaptic weights, respectively onto a target neuron k 1 from all neurons a fixed distance away, measured in terms of receptive field size. Using the cortical magnification of 30 deg/mm (Garrett et al, 2014;Zhuang et al, 2017), the standard deviation of a Gaussian fit (Figures 3B,D, black dashed line, also see Methods) can be converted to σ lr = 155 µm and σ s = 87 µm, respectively, qualitatively similar to the measured distances (Levy and Reyes, 2012) of 114 µm extrapolated from multi-patch recordings in mouse auditory cortex ( Figure 3A bottom panel, see also Methods) and reported dependence in mouse visual cortex (Seeman et al, 2018). Both the lowrank and sparse inhibitory connections have a somewhat larger spatial extent than the excitatory connections (σ lr ≈ 155µm ≈ σ s ), which could be verified experimentally.…”
Section: Orientation and Distance Dependence Of Connectionssupporting
confidence: 54%
“…The bottom rows of Figures 3B-E show the dependence of the mean positive (red lines) and negative (blue lines) synaptic weights, respectively onto a target neuron k 1 from all neurons a fixed distance away, measured in terms of receptive field size. Using the cortical magnification of 30 deg/mm (Garrett et al, 2014;Zhuang et al, 2017), the standard deviation of a Gaussian fit (Figures 3B,D, black dashed line, also see Methods) can be converted to σ lr = 155 µm and σ s = 87 µm, respectively, qualitatively similar to the measured distances (Levy and Reyes, 2012) of 114 µm extrapolated from multi-patch recordings in mouse auditory cortex ( Figure 3A bottom panel, see also Methods) and reported dependence in mouse visual cortex (Seeman et al, 2018). Both the lowrank and sparse inhibitory connections have a somewhat larger spatial extent than the excitatory connections (σ lr ≈ 155µm ≈ σ s ), which could be verified experimentally.…”
Section: Orientation and Distance Dependence Of Connectionssupporting
confidence: 54%
“…This structural heterogeneity was confirmed by a recent structural/functional investigation about synaptic connections in the human TLN using paired recordings (Seeman et al 2018). This study demonstrated that synaptic connections in the TLN are indeed highly reliable and strong as indicated by large excitatory postsynaptic potential (EPSP) amplitudes when compared to mouse neocortex, but also show layer-specific differences and in modulating short-term plasticity.…”
Section: Fig 3: Innervation Pattern Of Synaptic Boutons In Differentsupporting
confidence: 61%
“…In addition, the size and time course of action potential evoked Ca 2+ influx Sakmann 1996, 1998;Bischofberger and Jonas 2002), the occupancy of the putative Ca 2+ sensor driving vesicle fusion (Bollmann et al 2000;Schneggenburger and Neher 2000), the equilibration of intracellular Ca 2+ with the endogenous Ca 2+ buffer, and the eventual Ca 2+ -clearance (Helmchen et al 1997) can be accurately measured. Furthermore, the latency, size and time course of evoked quantal and multiquantal EPSCs (Borst and Sakmann 1996;Silver et al 2003;Molnar et al 2016;Holderith et al 2016;Seeman et al 2018;Rollenhagen et al 2018;Vaden et al 2019; reviewed by Neher 2015; Chamberland and Toth 2016) can be determined. However, there are still steps in the signal cascades that at present can only be simulated (Yamada and Zucker 1992;Bertram et al 1999;Meinrenken et al 2002Meinrenken et al , 2003Freche et al 2011).…”
Section: Historical Backgroundmentioning
confidence: 99%
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“…For example, by selectively expressing ChR2 in a specific population of neurons, one can study the connectivity from those ChR2-expressing neurons onto other non-ChR2 expressing neurons with ease and speed (Adesnik and Scanziani, 2010;Adesnik et al, 2012). In this experimental design, optogenetic stimulation replaces the electrical stimulation in paired whole-cell clamp recordings and greatly increases the yield and the chance of detecting connectivity, since multiple presynaptic neurons can be activated simultaneously by the lightevoked current (Seeman et al, 2018) and the spatiotemporal pattern of optogenetic stimulation can be flexibly readjusted (Adesnik and Scanziani, 2010;Adesnik et al, 2012). Notably, when this optogenetic method of local circuit analysis was compared to traditional pairwise patch clamp methods, both gave rise to similar connection probabilities, validating the utility of optogenetics in the study of local circuit connectivity (Seeman et al, 2018).…”
Section: Local Connectivitymentioning
confidence: 99%