Cited by 718Neural dynamics and brain functionNeuroscience and Neuropharmacology ResearchMemory and Neural Mechanisms

The DOI matches its Crossref record; 76 of the 78 references it cites were found in a registry or an index.

A synaptic organizing principle for cortical neuronal groups

Open access

Perin, Rodrigo, Berger, Thomas K., Markram, Henry·2011·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.1016051108PMC PMC3069183

Open this paper in CoChat — free account

Abstract

Neuronal circuitry is often considered a clean slate that can be dynamically and arbitrarily molded by experience. However, when we investigated synaptic connectivity in groups of pyramidal neurons in the neocortex, we found that both connectivity and synaptic weights were surprisingly predictable. Synaptic weights follow very closely the number of connections in a group of neurons, saturating after only 20% of possible connections are formed between neurons in a group. When we examined the network topology of connectivity between neurons, we found that the neurons cluster into small world networks that are not scale-free, with less than 2 degrees of separation. We found a simple clustering rule where connectivity is directly proportional to the number of common neighbors, which accounts for these small world networks and accurately predicts the connection probability between any two neurons. This pyramidal neuron network clusters into multiple groups of a few dozen neurons each. The neurons composing each group are surprisingly distributed, typically more than 100 μm apart, allowing for multiple groups to be interlaced in the same space. In summary, we discovered a synaptic organizing principle that groups neurons in a manner that is common across animals and hence, independent of individual experiences. We speculate that these elementary neuronal groups are prescribed Lego-like building blocks of perception and that acquired memory relies more on combining these elementary assemblies into higher-order constructs.

Integrity signals

Registry

Crossref · 10.1073/pnas.1016051108

Cites

76 of 76 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
The Organization of Behavior
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
Clustering by Passing Messages Between Data Points
Frey, B. J., & Dueck, D. (2007) · Science · doi:10.1126/science.1136800 · Cited by 6,855
An Essay concerning Human Understanding
Locke, J. (1690) · Oxford University Press eBooks · doi:10.1093/oseo/instance.00018020 · Cited by 5,214
The Organization of Behavior; A Neuropsychological Theory.
Attneave, F., B., M., & Hebb, D. O. (1950) · The American Journal of Psychology · doi:10.2307/1418888 · Cited by 5,146
The Organization of Behavior: A Neuropsychological Theory
(1950) · JAMA · doi:10.1001/jama.1950.02910470083028 · Cited by 4,272
Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs
Markram, H., Lübke, J., Frotscher, M., & Sakmann, B. (1997) · Science · doi:10.1126/science.275.5297.213 · Cited by 3,832
Self-citation

Show all 78 references

Research works printed but not linked: 1

10.1016/j.neuron.2004.08.012 · DOI

Sorted by citations

Items 201–300 of 716

Multiscale and multimodal reconstruction of cortical structure and functionPreprint
Turner, N. L., Macrina, T., Bae, J. A., Yang, R., Wilson, A. M., Schneider-Mizell, C. M., Lee, K., Lu, R., Wu, J., Bodor, Á. L., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.14.338681 · Cited by 37
Differential excitatory control of 2 parallel basket cell networks in amygdala microcircuits
Andrási, T., Veres, J. M., Rovira-Esteban, L., Kozma, R., Vikór, A., Gregori, E., & Hájos, N. (2017) · PLoS Biology · doi:10.1371/journal.pbio.2001421 · Cited by 34
Rich-club connectivity, diverse population coupling, and dynamical activity patterns emerging from local cortical circuits
Gu, Y., Qi, Y., & Gong, P. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006902 · Cited by 33
Computation of the electroencephalogram (EEG) from network models of point neurons
Martínez‐Cañada, P., Ness, T. V., Einevoll, G. T., Fellin, T., & Panzeri, S. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008893 · Cited by 39
Associative memory model with long-tail-distributed Hebbian synaptic connections
Hiratani, N., Teramae, J.-N., & Fukai, T. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00102 · Cited by 36
Reactivation in Working Memory: An Attractor Network Model of Free Recall
Lansner, A., Marklund, P., Sikström, S., & Nilsson, L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073776 · Cited by 34
Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks
Brendan, C., & MacLean, J. N. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005078 · Cited by 36
Effect of dilution in asymmetric recurrent neural networks
Folli, V., Gosti, G., Leonetti, M., & Ruocco, G. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.04.003 · Cited by 35
Formation and Maintenance of Robust Long-Term Information Storage in the Presence of Synaptic Turnover
Fauth, M. J., Wörgötter, F., & Tetzlaff, C. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004684 · Cited by 37
The Connectome Predicts Resting State Functional Connectivity Across the Drosophila Brain
Turner, M. H., Mann, K. J., & Clandinin, T. R. (2020) · Current Biology · doi:10.1016/j.cub.2021.03.004 · Cited by 34
Long‐range connectomics
Jbabdi, S., & Behrens, T. E. J. (2013) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12271 · Cited by 31
Model-based analysis of pattern motion processing in mouse primary visual cortex
Muir, D. R., Roth, M., Helmchen, F., & Kampa, B. M. (2015) · Frontiers in Neural Circuits · doi:10.3389/fncir.2015.00038 · Cited by 29
Beyond the Maximum Storage Capacity Limit in Hopfield Recurrent Neural Networks
Gosti, G., Folli, V., Leonetti, M., & Ruocco, G. (2019) · Entropy · doi:10.3390/e21080726 · Cited by 35
Biophysical Kv3 channel alterations dampen excitability of cortical PV interneurons and contribute to network hyperexcitability in early Alzheimer’s
Oláh, V. J., Goettemoeller, A. M., Rayaprolu, S., Dammer, E. B., Seyfried, N. T., Rangaraju, S., Dimidschstein, J., & Rowan, M. J. (2022) · eLife · doi:10.7554/elife.75316 · Cited by 34
Cell Assembly Signatures Defined by Short-Term Synaptic Plasticity in Cortical Networks
Carrillo‐Reid, L., López-Huerta, V. G., García-Muñoz, M., Theiss, S., & Arbuthnott, G. W. (2015) · International Journal of Neural Systems · doi:10.1142/s0129065715500264 · Cited by 33
In vitro Cortical Network Firing is Homeostatically Regulated: A Model for Sleep Regulation
Saberi-Moghadam, S., Simi, A., Setareh, H., Mikhail, C., & Tafti, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-24339-6 · Cited by 30
Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model
Kobayashi, R., & Kitano, K. (2013) · Journal of Computational Neuroscience · doi:10.1007/s10827-013-0443-y · Cited by 31
Studies of cortical connectivity using optical circuit mapping methods
Anastasiades, P. G., Marques–Smith, A., & Butt, S. J. B. (2017) · The Journal of Physiology · doi:10.1113/jp273463 · Cited by 32
Functional triplet motifs underlie accurate predictions of single-trial responses in populations of tuned and untuned V1 neurons
Dechery, J. B., & MacLean, J. N. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006153 · Cited by 31
From Isles of Königsberg to Islets of Langerhans: Examining the Function of the Endocrine Pancreas Through Network Science
Stožer, A., Šterk, M., Leitgeb, E. P., Markovič, R., Klemen, M. S., Ellis, C. E., Bombek, L. K., Dolenšek, J., MacDonald, P. E., & Gosak, M. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.922640 · Cited by 37
Homophilic wiring principles underpin neuronal network topology in vitro
Akarca, D., Dunn, A. W., Hornauer, P., Ronchi, S., Michele, F., Wang, C., Terrigno, M., Jagasia, R., Vértes, P. E., Mierau, S. B., et al. (2025) · eLife · doi:10.7554/elife.85300 · Cited by 31
Experience-Dependent Emergence of Fine-Scale Networks in Visual Cortex
Ishikawa, A. W., Komatsu, Y., & Yoshimura, Y. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.1346-14.2014 · Cited by 31
A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity
Haß, J., Hertäg, L., & Durstewitz, D. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004930 · Cited by 33
Cell-Autonomous Regulation of Dendritic Spine Density by PirB
Vidal, G. S., Djurisic, M. R., Brown, K., Sapp, R. W., & Shatz, C. J. (2016) · eNeuro · doi:10.1523/eneuro.0089-16.2016 · Cited by 31
Brain Computation Is Organized via Power-of-Two-Based Permutation Logic
Xie, K., Fox, G. E., Liu, J., Lyu, C., Lee, J. C., Kuang, H., Jacobs, S., Li, M., Liu, T., Song, S., et al. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00095 · Cited by 30
Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits
Duarte, R., & Morrison, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006781 · Cited by 30
Recurrent connections between CA2 pyramidal cells
Okamoto, K., & Ikegaya, Y. (2018) · Hippocampus · doi:10.1002/hipo.23064 · Cited by 33
On the dynamics of cortical development: synchrony and synaptic self-organization
Wright, J. J., & Bourke, P. D. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00004 · Cited by 30
Local Context Normalization: Revisiting Local Normalization
Ortiz, A., Robinson, C., Morris, D., Fuentes, O., Kiekintveld, C. D., Hassan, M., & Jojić, N. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01129 · Cited by 29
Neuron Geometry Underlies Universal Network Features in Cortical MicrocircuitsPreprint
Gal, E., Perin, R., Markram, H., London, M., & Segev, I. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/656058 · Cited by 30
Self-citation
Rate chaos and memory lifetime in spiking neural networks
Klinshov, V. V., Kovalchuk, A. V., Franović, I., Perc, M., & Svetec, M. (2022) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.112011 · Cited by 29
Cortical attractor network dynamics with diluted connectivity
Rolls, E. T., & Webb, T. J. (2011) · Brain Research · doi:10.1016/j.brainres.2011.08.002 · Cited by 31
Classification of neocortical interneurons using affinity propagation
Santana, R., McGarry, L. M., Bielza, C., Larrañaga, P., & Yuste, R. (2013) · Frontiers in Neural Circuits · doi:10.3389/fncir.2013.00185 · Cited by 30
Independently Outgrowing Neurons and Geometry-Based Synapse Formation Produce Networks with Realistic Synaptic Connectivity
van Ooyen, A., Carnell, A., de Ridder, S., Tarigan, B., Mansvelder, H. D., Bijma, F., de Gunst, M. C. M., & van Pelt, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085858 · Cited by 30
Emergence of Connectivity Motifs in Networks of Model Neurons with Short- and Long-Term Plastic Synapses
Vasilaki, E., & Giugliano, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0084626 · Cited by 29
The emergence of multiscale connectomics-based approaches in stroke recovery
Latifi, S., & Carmichael, S. T. (2024) · Trends in Neurosciences · doi:10.1016/j.tins.2024.01.003 · Cited by 30
Contributions of cortical neuron firing patterns, synaptic connectivity, and plasticity to task performance
Insanally, M. N., Albanna, B. F., Toth, J., DePasquale, B., Fadaei, S. S., Gupta, T., Lombardi, O., Kuchibhotla, K. V., Rajan, K., & Froemke, R. C. (2024) · Nature Communications · doi:10.1038/s41467-024-49895-6 · Cited by 30
Specificity and randomness: structure–function relationships in neural circuits
Lee, W.-C. A., & Reid, R. C. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.07.004 · Cited by 29
Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits
Zhang, D., Zhang, C., & Stepanyants, A. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.3218-18.2019 · Cited by 26
Relating network connectivity to dynamics: opportunities and challenges for theoretical neuroscience
Curto, C., & Morrison, K. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.06.003 · Cited by 31
A spatiotemporal mechanism of visual attention: Superdiffusive motion and theta oscillations of neural population activity patterns
Chen, G., & Gong, P. (2022) · Science Advances · doi:10.1126/sciadv.abl4995 · Cited by 28
Toward computational neuroconstructivism: a framework for developmental systems neuroscience
Astle, D. E., Johnson, M. H., & Akarca, D. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.04.009 · Cited by 34
Developmental Self-Construction and -Configuration of Functional Neocortical Neuronal Networks
Bauer, R., Zubler, F., Pfister, S., Hauri, A., Pfeiffer, M., Muir, D. R., & Douglas, R. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003994 · Cited by 28
The relevance of network micro-structure for neural dynamics
Pernice, V., Deger, M., Cardanobile, S., & Rotter, S. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00072 · Cited by 28
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits
Shababo, B., Paige, B., Pakman, A., & Paninski, L. (2013) · Cited by 29
Cortical cell assemblies and their underlying connectivity: An in silico study
Ecker, A., Santander, D. E., Bolaños‐Puchet, S., Isbister, J. B., & Reimann, M. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011891 · Cited by 31
Feedback through graph motifs relates structure and function in complex networks
Hu, Y., Brunton, S. L., Cain, N., Mihalaş, Ş., Kutz, J. N., & Shea‐Brown, E. (2018) · Physical review. E · doi:10.1103/physreve.98.062312 · Cited by 25
Very Large-Scale Neuromorphic Systems for Biological Signal Processing
Catthoor, F., Mitra, S., Das, A. K., & Schaafsma, S. (2017) · Springer eBooks · doi:10.1007/978-3-319-67723-1_13 · Cited by 27
The growth of cognition: Free energy minimization and the embryogenesis of cortical computation
Wright, J. J., & Bourke, P. D. (2020) · Physics of Life Reviews · doi:10.1016/j.plrev.2020.05.004 · Cited by 27
Bringing Anatomical Information into Neuronal Network Models
van Albada, S. J., Morales-Gregorio, A., Dickscheid, T., Goulas, A., Bakker, R., Bludau, S., Palm, G., Hilgetag, C. C., & Diesmann, M. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-89439-9_9 · Cited by 29
Emergence of Irregular Activity in Networks of Strongly Coupled Conductance-Based Neurons
Sanzeni, A., Histed, M. H., & Brunel, N. (2022) · Physical Review X · doi:10.1103/physrevx.12.011044 · Cited by 26
From neurons to epidemics: How trophic coherence affects spreading processes
Klaise, J., & Johnson, S. (2016) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4953160 · Cited by 29
Reorganization of Recurrent Layer 5 Corticospinal Networks Following Adult Motor Training
Biane, J. S., Takashima, Y., Scanziani, M., Conner, J. M., & Tuszynski, M. H. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.3442-17.2019 · Cited by 31
On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes
Vegué, M., Perin, R., & Roxin, A. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0984-17.2017 · Cited by 25
Self-citation
Robust design of polyrhythmic neural circuits
Schwabedal, J. T. C., NEIMAN, A. B., & SHILNIKOV, A. L. (2014) · Physical Review E · doi:10.1103/physreve.90.022715 · Cited by 26
Circuit mechanisms for cortical plasticity and learning
Chéreau, R., Williams, L. E., Bawa, T., & Holtmaat, A. J. D. G. (2021) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2021.07.012 · Cited by 28
Long-Term Desynchronization by Coordinated Reset Stimulation in a Neural Network Model With Synaptic and Structural Plasticity
Manos, T., Diaz-Pier, S., & Tass, P. A. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.716556 · Cited by 27
Graph Theoretical Model of a Sensorimotor Connectome in Zebrafish
Stobb, M. T., Peterson, J. M., Mazzag, B., & Gahtan, E. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037292 · Cited by 28
Conditions for wave trains in spiking neural networks
Senk, J., Korvasová, K., Schuecker, J., Hagen, E., Tetzlaff, T., Diesmann, M., & Helias, M. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.023174 · Cited by 35
A reservoir of timescales emerges in recurrent circuits with heterogeneous neural assemblies
Stern, M., Istrate, N., & Mazzucato, L. (2023) · eLife · doi:10.7554/elife.86552 · Cited by 36
Dynamics of Competition between Subnetworks of Spiking Neuronal Networks in the Balanced State
Lagzi, F., & Rotter, S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138947 · Cited by 27
Differential Wiring of Layer 2/3 Neurons Drives Sparse and Reliable Firing During Neocortical Development
Benedetti, B. L., Takashima, Y., Wen, J. A., Urban‐Ciećko, J., & Barth, A. L. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs257 · Cited by 25
Bifurcation analysis of the dynamics of interacting subnetworks of a spiking network
Lagzi, F., Atay, F. M., & Rotter, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-47190-9 · Cited by 24
Mapping and modeling age-related changes in intrinsic neural timescales
Wu, K., & Gollo, L. L. (2025) · Communications Biology · doi:10.1038/s42003-025-07517-x · Cited by 26
Of mice and men: Dendritic architecture differentiates human from mouse neuronal networks
Kanari, L., Shi, Y., Arnaudon, A., Barros-Zulaica, N., Benavides‐Piccione, R., Coggan, J. S., DeFelipe, J., Hess, K., Mansvelder, H. D., Mertens, E. J., et al. (2025) · iScience · doi:10.1016/j.isci.2025.112928 · Cited by 37
Self-citation
Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks
Vincent‐Lamarre, P., Lajoie, G., & Thivierge, J. (2016) · Journal of Computational Neuroscience · doi:10.1007/s10827-016-0619-3 · Cited by 24
A stepwise neuron model fitting procedure designed for recordings with high spatial resolution: Application to layer 5 pyramidal cells
Mäki‐Marttunen, T., Halnes, G., Devor, A., Metzner, C., Dale, A. M., Andreassen, O. A., & Einevoll, G. T. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.10.007 · Cited by 25
From neurons to epidemics : how trophic coherence affects spreading processes
Klaise, J., & Johnson, S. (2016) · Warwick Research Archive Portal (University of Warwick) · Cited by 24
Finding communities in sparse networks
Singh, A. K., & Humphries, M. D. (2015) · Scientific Reports · doi:10.1038/srep08828 · Cited by 24
Strong and localized recurrence controls the dimensionality of neural activity across brain areas
Dahmen, D., Recanatesi, S., Jia, X., Ocker, G. K., Lahiji, N., Musall, S., Campagnola, L., Seeman, S. C., Jarsky, T., Helias, M., et al. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02395-w · Cited by 45
Networks that learn the precise timing of event sequences
Veliz‐Cuba, A., Shouval, H. Z., Josić́, K., & Kilpatrick, Z. P. (2015) · Journal of Computational Neuroscience · doi:10.1007/s10827-015-0574-4 · Cited by 23
Spontaneous emergence of fast attractor dynamics in a model of developing primary visual cortex
Miconi, T., McKinstry, J. L., & Edelman, G. M. (2016) · Nature Communications · doi:10.1038/ncomms13208 · Cited by 22
Bridging structure and function: A model of sequence learning and prediction in primary visual cortex
Klos, C., Miner, D. C., & Triesch, J. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006187 · Cited by 22
Stable learning of functional maps in self-organizing spiking neural networks with continuous synaptic plasticity
Srinivasa, N., & Jiang, Q. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00010 · Cited by 23
Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons
Setareh, H., Deger, M., Petersen, C. C. H., & Wulfram Gerstner, B. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00052 · Cited by 22
Two classes of functional connectivity in dynamical processes in networks
Voutsa, V., Battaglia, D. A., Bracken, L. J., Brovelli, A., Costescu, J., Muñoz, M. D., Fath, B. D., Funk, A., Guirro, M., Hein, T. I. A., et al. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0486 · Cited by 26
A developmental switch between electrical and neuropeptide communication in the ventromedial hypothalamus
Shao, Y.-Q., Fan, L., Wu, W., Zhu, Y.-J., & Xu, H. (2022) · Current Biology · doi:10.1016/j.cub.2022.05.029 · Cited by 21
Multi-scale spiking network model of human cerebral cortex
Pronold, J., van Meegen, A., Shimoura, R. O., Vollenbröker, H., Senden, M., Hilgetag, C. C., Bakker, R., & van Albada, S. J. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae409 · Cited by 26
Principles of visual cortex excitatory microcircuit organization
Chou, C. Y. C., Wong, H. H., Guo, C., Boukoulou, K. E., Huang, C., Jannat, J., Klimenko, T., Li, V. Y., Liang, T. A., Wu, V. C., et al. (2024) · The Innovation · doi:10.1016/j.xinn.2024.100735 · Cited by 26
Persistence and storage of activity patterns in spiking recurrent cortical networks: modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine
Palma, J., Grossberg, S., & Massimiliano Versace, A. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00042 · Cited by 21
Low-dimensional morphospace of topological motifs in human fMRI brain networks
Morgan, S. E., Achard, S., Termenón, M., Vértes, P. E., & Bullmore, E. T. (2018) · Network Neuroscience · doi:10.1162/netn_a_00038 · Cited by 23
Common Microscale and Macroscale Principles of Connectivity in the Human Brain
Scholtens, L. H., Pijnenburg, R., de Lange, S. C., Huitinga, I., van den Heuvel, M. P., & (NBB), N. B. B. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1572-21.2022 · Cited by 26
A Neurospheroid‐Based Microrobot for Targeted Neural Connections in a Hippocampal Slice
Kim, E., Jeon, S., Yang, Y., Jin, C., Kim, J., Oh, Y., Rah, J., & Choi, H. (2023) · Advanced Materials · doi:10.1002/adma.202208747 · Cited by 21
Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala
Abatis, M., Perin, R., Niu, R., van den Burg, E., Hegoburu, C., Kim, R., Okamura, M., Bito, H., Markram, H., & Stoop, R. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01676-6 · Cited by 23
Self-citation
Functional Network Overlap as Revealed by fMRI Using sICA and Its Potential Relationships with Functional Heterogeneity, Balanced Excitation and Inhibition, and Sparseness of Neuron Activity
Xu, J., Calhoun, V. D., Worhunsky, P. D., Xiang, H., Li, J., Wall, J. T., Pearlson, G. D., & Potenza, M. N. (2015) · PLoS ONE · doi:10.1371/journal.pone.0117029 · Cited by 21
Emergence of functional subnetworks in layer 2/3 cortex induced by sequential spikes in vivo
Kim, T.-K., Oh, W. C., Choi, J. H., & Kwon, H. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1513410113 · Cited by 21
Efficient Multispike Learning for Spiking Neural Networks Using Probability-Modulated Timing Method
Hu, R., Chang, S., Wang, H., He, J. L., & Huang, Q. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2875471 · Cited by 21
Cyclic transitions between higher order motifs underlie sustained asynchronous spiking in sparse recurrent networks
Bojanek, K., Zhu, Y., & MacLean, J. N. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007409 · Cited by 19
Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks
Weidel, P., Duarte, R., & Morrison, A. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.543872 · Cited by 25
Circuits and Synapses: Hypothesis, Observation, Controversy and Serendipity – An Opinion Piece
Thomson, A. M. (2021) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.732315 · Cited by 20
The Features and Functions of Neuronal Assemblies: Possible Dependency on Mechanisms beyond Synaptic Transmission
Badin, A.-S., Fermani, F., & Greenfield, S. A. (2017) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00114 · Cited by 23
State-Dependent Regulation of Cortical Processing Speed via Gain Modulation
Wyrick, D. G., & Mazzucato, L. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1895-20.2021 · Cited by 27
Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes
Santander, D. E., Pokorny, C., Ecker, A., Lazovskis, J., Santoro, M., Smith, J. P., Hess, K. P., Levi, R., & Reimann, M. (2024) · iScience · doi:10.1016/j.isci.2024.111585 · Cited by 22
Synapse-Centric Mapping of Cortical Models to the SpiNNaker Neuromorphic Architecture
Knight, J. C., & Furber, S. B. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00420 · Cited by 20
Single-Cell Membrane Potential Fluctuations Evince Network Scale-Freeness and Quasicriticality
Johnson, J. K., Wright, N. C., Xià, J., & Weßel, R. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.3163-18.2019 · Cited by 21
Cellular and Synaptic Diversity of Layer 2-3 Pyramidal Neurons in Human IndividualsPreprint
Planert, H., Mittermaier, F. X., Grosser, S., Fidzinski, P., Schneider, U. C., Radbruch, H., Onken, J., Holtkamp, M., Schmitz, D., Alle, H., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.08.467668 · Cited by 21
Decreased intrinsic neural timescale in treatment-naïve adolescent depression
Zheng, R., Bu, C., Chen, Y., Wei, Y., Zhou, B., Jiang, Y. B., Zhu, C., Wang, K., Wang, C., Li, S., et al. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.12.048 · Cited by 22
Spatially structured inhibition defined by polarized parvalbumin interneuron axons promotes head direction tuning
Peng, Y., Tomás, F. J. B., Pfeiffer, P., Drangmeister, M., Schreiber, S., Vida, I., & Geiger, J. R. P. (2021) · Science Advances · doi:10.1126/sciadv.abg4693 · Cited by 20
GABAergic-like dopamine synapses in the brain
Kim, H., Hwang, B., Reva, M., Lee, J.-E., Lee, B. E., Lee, Y., Cho, E. J., Jeong, M., Lee, S. E., Myung, K., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113239 · Cited by 19
Motifs in health and disease: the promise of circuit interrogation by optogenetics
Tiesinga, P. H. E. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08186.x · Cited by 20

Authors: Perin, R., Berger, T. K., & Markram, H.
Affiliations: École Polytechnique Fédérale de Lausanne (CH)
University of California, San Francisco (US)
Corresponding author: Markram, Henry
ORCID: Perin, Rodrigo
Berger, Thomas K.
Markram, Henry
Venue: Proceedings of the National Academy of Sciences
Publisher: National Academy of Sciences
Published: 2011-03-07
Volume: 108
Issue: 13
Pages: 5419-5424
ISSN: 0027-8424 (ISSN-L)
1091-6490
DOI: 10.1073/pnas.1016051108
PMC: PMC3069183
OpenAlex: W2011525418
Cited by: 718
References: 78
Citations by year: 2026: 31 · 2025: 37 · 2024: 61 · 2023: 40 · 2022: 36 · 2021: 39 · 2020: 43 · 2019: 61 · 2018: 45 · 2017: 55 · 2016: 59 · 2015: 59 · 2014: 48 · 2013: 53 · 2012: 40
FWCI: 24.70
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Proceedings of the National Academy of Sciences (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Max Planck Digital Library (submittedVersion)
Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
MPG.PuRe (Max Planck Society) (submittedVersion)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
Keywords: synaptic connectivity, pyramidal neurons, neocortex, small-world networks, network topology, clustering coefficient, synaptic weights, cortical circuits, perception
MeSH terms: Animals, Cerebral Cortex, Models, Neurological, Nerve Net, Synapses, Rats, Wistar, Pyramidal Cells, Patch-Clamp Techniques, Rats

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A synaptic organizing principle for cortical neuronal groups

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks