Local connectivity and synaptic dynamics in mouse and human neocortex

Campagnola, Luke; Seeman, Stephanie C.; Chartrand, Thomas; Kim, Lisa; Hoggarth, Alex; Gamlin, Clare; Ito, Setsuro; Trinh, Jessica; Davoudian, Pasha A; Radaelli, Cristina; Kim, Mean-Hwan; Hage, Travis A.; Braun, Thomas; Alfiler, Lauren; Andrade, Júlia; Bohn, Phillip; Dalley, Rachel; Henry, Alex M.; Kebede, Sara; Mukora, Alice; Sandman, David; Williams, Grace; Larsen, Rachael; Teeter, Corinne; Daigle, Tanya L.; Berry, Kyla; Dotson, Nadia; Enstrom, Rachel; Gorham, Melissa; Hupp, Madie; Lee, Samuel Dingman; Ngo, Kiet; Nicovich, Philip R.; Potekhina, Lydia; Ransford, Shea; Gary, Amanda; Goldy, Jeff; McMillen, Delissa; Pham, Trangthanh; Tieu, Michael; Siverts, La’ Akea; Walker, Miranda; Farrell, Colin; Schroedter, Martin; Slaughterbeck, Cliff; Cobbs, Charles; Ellenbogen, Richard G.; Gwinn, Ryder P.; Keene, C. Dirk; Ko, Andrew L.; Ojemann, Jeffrey G.; Silbergeld, Daniel L.; Carey, Daniel; Casper, Tamara; Crichton, Kirsten; Clark, Michael; Dee, Nick; Ellingwood, Lauren; Gloe, Jessica; Kroll, Matthew; Šulc, Josef; Tung, Herman; Wadhwani, Katherine; Brouner, Krissy; Egdorf, Tom; Maxwell, Michelle; McGraw, Mary; Pom, Alice; Ruiz, Augustin; Bomben, Jasmine; Feng, David; Hejazinia, Nika; Shu, Shi; Szafer, Aaron; Wakeman, Wayne; Phillips, John W.; Bernard, Amy; Esposito, Luke; D’Orazi, Florence D.; Sunkin, Susan M.; Smith, Kimberly A.; Tasic, Bosiljka; Arkhipov, Anton; Sorensen, Staci A.; Lein, Ed; Koch, Christof; Murphy, Gabe J.; Zeng, Hongkui; Jarsky, Tim

doi:10.1126/science.abj5861

Cited by 200 publications

(281 citation statements)

References 90 publications

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“…A fact that plays a crucial role in our study is the diversity and heterogeneity of EPSP amplitudes from E to PV cells. Intracellular in vitro recordings reveal that in layer 2/3, the EPSP amplitudes from E to PV cells are much stronger and more diverse than the connections between E cells [2, 14, 21]. Moreover, highly feature-tuned PV cells have smaller dendritic fields, but broadly tuned PV cells have longer dendrites with larger dendritic fields [29].…”

Section: Discussionmentioning

confidence: 99%

“…We reconcile these diverse experimental findings by showing in a model that the emergence of co-tuned PV neurons is a network property, and considering only pairwise correlations between PV and E cells may not identify their tuning. To achieve this, we consider two facts and one hypothesis about layer 2/3 neurons in mouse V1: (1) shared input from layer 4 drives feature tuning and stimulus selectivity in excitatory subnetworks [19,20], (2) there is a large amount of heterogeneity in the excitatory postsynaptic potentials (EPSP) that impinge on PV cells [2,14,21], (3) we assume that the homeostatic regulation of postsynaptic firing rate which has been reported for GABAergic cells in the literature [22][23][24][25] governs the synaptic dynamics of the connections from PV to E cells. These considerations result in the emergence of co-tuned PV and pyramidal subnetworks, with a small portion of PV cells strongly tuned to Pyramidal cells.…”

Section: Introductionmentioning

confidence: 99%

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Tuned inhibitory firing rate and connection weights as emergent network properties

Lagzi

Fairhall

2022

Preprint

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Excitatory cortical neurons show clear tuning to stimulus features, but the tuning properties of inhibitory neurons are ambiguous and have been the subject of a long debate. While inhibitory neurons have been considered to be largely untuned [1–4], recent studies show that some parvalbumin expressing (PV) neurons do show feature selectivity and participate in co-tuned subnetworks with pyramidal cells in which PV cells show high response similarity to the excitatory (E) neurons [5, 6]. Given shared input from layer 4 that drives feature tuning in excitatory subnetworks, we demonstrate that homeostatic regulation of postsynaptic firing rate governing the synaptic dynamics of the connections from PV to E cells, in combination with heterogeneity in the excitatory postsynaptic potentials (EPSP) that impinge on PV cells, results in the self-organization of PV subnetworks. We reconcile different experimental findings by showing that feature tuning of PV cells is an emerging network property that may be driven by synaptic heterogeneity, and can be inferred using population-level measures, while pairwise individual-level measures may fail to reveal inhibitory tuning. We show that such co-tuning can enhance network stability at the cost of response salience.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tuned inhibitory firing rate and connection weights as emergent network properties

Lagzi

Fairhall

2022

Preprint

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“…Separate subsets of IPSCs are selectively recruited by activation of interneurons of different subtypes. However, in the rodent neocortex, presynaptic interneurons of different subtype elicit in postsynaptic pyramidal cells GABA A R-IPSCs with similar decay kinetics (Xiang et al, 2002; Ali and Thomson, 2008; Galarreta et al, 2008; Donato et al, 2021; Campagnola et al, 2022). Likewise, different subtypes of presynaptic interneurons elicit GABA A R-IPSCs with similar decay kinetics in L3 PNs from monkey DLPFC (Gonzalez-Burgos et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

“…However, in the rodent neocortex, presynaptic interneurons of different subtype elicit in postsynaptic pyramidal cells GABAAR-IPSCs with similar decay kinetics (Xiang et al, 2002;Ali and Thomson, 2008;Galarreta et al, 2008;Donato et al, 2021;Campagnola et al, 2022).…”

Section: Gabaar-mediated Ipscs Show Similar Properties In Dlpfc and P...mentioning

confidence: 99%

Mechanisms regulating the frequency of inhibition-based gamma oscillations in primate prefrontal and parietal cortices

González‐Burgos

Miyamae

Reddy

et al. 2022

Preprint

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In primates, the dorsolateral prefrontal (DLPFC) and posterior parietal (PPC) cortices are critical nodes in the network mediating cognitive functions including attention and working memory. Notably, during working memory tasks, gamma oscillations, usually prominent in layer 3 (L3), are induced in both DLPFC and PPC but exhibit higher frequency in DLPFC. These oscillation frequency differences might be crucial for working memory function, but the mechanisms producing different oscillation frequencies in monkey DLPFC and PPC remain poorly understood.To investigate the basis of the DLPFC-PPC differences in oscillation frequency we studied GABAAR-mediated inhibition, which plays a crucial role in gamma oscillation production, in L3 pyramidal neurons (L3 PNs) from the rhesus monkey DLPFC or PPC. Recordings of GABAAR-mediated synaptic currents from L3 PNs, while suggesting a contribution to network synchronization in both areas, revealed no DLPFC-PPC differences in the strength or kinetics of GABAAR-mediated inhibition. Likewise, the expression of GABAAR genes in L3 PNs did not differ between regions.In the absence of differences in inhibition, DLPFC L3 PNs showed greater dendritic spine density and higher expression of AMPAR and NMDAR subunit genes relative to PPC L3 PNs, suggesting that the excitatory synaptic drive onto L3 PNs could be stronger in the DLPFC. Simulations in computational models of the cortical microcircuit showed that, with constant synaptic inhibition, increasing the strength of recurrent excitatory synaptic drive increased the network oscillation frequency. Hence, the DLPFC-PPC differences in gamma oscillation frequency could depend on stronger recurrent excitation in the DLPFC relative to PPC.Significance statementGamma oscillations may contribute to the neural substrate of working memory and exhibit a higher frequency in the prefrontal (DLPFC) than parietal (PPC) areas of primate cortex. To investigate the basis of these oscillation frequency differences which may be crucial for working memory encoding, we studied GABAAR-mediated inhibition on L3 pyramidal neurons (L3 PNs) from rhesus monkey DLPFC or PPC. Our data revealed no DLPFC-PPC differences in GABAAR-mediated inhibition but showed greater dendritic spine density in DLPFC L3 PNs, suggesting stronger excitatory synaptic drive. Simulations in computational network models showed that stronger recurrent excitatory synaptic drive increased the network oscillation frequency, suggesting that the higher oscillation frequency could depend on stronger recurrent excitation in the DLPFC relative to PPC.

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“…A substantial body of anatomical and neurophysiological data on higher cortical areas and their connectivity to V1 is currently available for that, see e.g. [Siegle et al, 2021, Campagnola et al, 2021, Iavarone et al, 2022]. We expect that deficits in visual processing capabilities of the V1 model, such as limited spatial integration of image features and a relatively short working memory time span, will disappear when the V1 model is combined with models of higher brain areas.…”

Section: Discussionmentioning

confidence: 99%

Anatomical and neurophysiological data on primary visual cortex suffice for reproducing brain-like robust multiplexing of visual function

Chen

Scherr

Maass

2021

Preprint

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The neocortex is a network of rather stereotypical cortical microcircuits that share an exquisite genetically encoded architecture: Neurons of a fairly large number of different types are distributed over several layers (laminae), with specific probabilities of synaptic connections that depend on the neuron types involved and their spatial locations. Most available knowledge about this structure has been compiled into a detailed model for a generic cortical microcircuit in the primary visual cortex, consisting of 51,978 neurons of 111 different types. We add a noise model to the network that is based on experimental data, and analyze the results of network computations that can be extracted by projection neurons on layer 5. We show that the resulting model acquires through alignment of its synaptic weights via gradient descent training the capability to carry out a number of demanding visual processing tasks. Furthermore, this weight-alignment induces specific neural coding features in the microcircuit model that match those found in the living brain: High dimensional neural codes with an arguably close to optimal power-law decay of explained variance of PCA components, specific relations between signal- and noise-coding dimensions, and network dynamics in a critical regime. Hence these important features of neural coding and dynamics of cortical microcircuits in the brain are likely to emerge from aspects of their genetically encoded architecture that are captured by this data-based model in combination with learning processes. In addition, the model throws new light on the relation between visual processing capabilities and details of neural coding.

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Local connectivity and synaptic dynamics in mouse and human neocortex

Cited by 200 publications

References 90 publications

Tuned inhibitory firing rate and connection weights as emergent network properties

Tuned inhibitory firing rate and connection weights as emergent network properties

Mechanisms regulating the frequency of inhibition-based gamma oscillations in primate prefrontal and parietal cortices

Anatomical and neurophysiological data on primary visual cortex suffice for reproducing brain-like robust multiplexing of visual function

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