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A synaptic organizing principle for cortical neuronal groups
Open accessAbstract
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.
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“A high clustering coefficient and an over-representation of highly connected motifs suggest that the local structure of the brain displays a high degree of non-randomness, consistent with previous studies in C. elegans7,21 and in the mouse cortex11,24,25.”
From Network statistics of the whole-brain connectome of Drosophila (2024) · cites this paper as [25]
Citations
Cites
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
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
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- 10.1016/j.neuron.2004.08.012 · DOI
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Liu, Y., & Barabási, A.-Ĺ. (2016) · Reviews of Modern Physics · doi:10.1103/revmodphys.88.035006 · Cited by 650
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Harris, K. D., & Mrsic‐Flogel, T. D. (2013) · Nature · doi:10.1038/nature12654 · Cited by 685
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Stam, C. J., & van Straaten, E. C. W. (2012) · Clinical Neurophysiology · doi:10.1016/j.clinph.2012.01.011 · Cited by 591
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Litwin-Kumar, A., & Doiron, B. (2012) · Nature Neuroscience · doi:10.1038/nn.3220 · Cited by 555
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Cossell, L., Iacaruso, M. F., Muir, D. R., Houlton, R., Sader, E., Ko, H., Hofer, S. B., & Mrsic‐Flogel, T. D. (2015) · Nature · doi:10.1038/nature14182 · Cited by 497
Projections from neocortex mediate top-down control of memory retrieval
Rajasethupathy, P., Sankaran, S., Marshel, J. H., Kim, C. K., Ferenczi, E., Lee, S. Y., Berndt, A., Ramakrishnan, C., Jaffe, A., Lo, M., et al. (2015) · Nature · doi:10.1038/nature15389 · Cited by 509
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Cadwell, C. R., Bhaduri, A., Mostajo-Radji, M. A., Keefe, M. G., & Nowakowski, T. J. (2019) · Neuron · doi:10.1016/j.neuron.2019.07.009 · Cited by 408
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Simple models of human brain functional networks
Vértes, P. E., Alexander‐Bloch, A. F., Gogtay, N., Giedd, J. N., Rapoport, J. L., & Bullmore, E. T. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111738109 · Cited by 356
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Neural reuse of action perception circuits for language, concepts and communication
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Reimann, M., Anastassiou, C. A., Perin, R., Hill, S., Markram, H., & Koch, C. (2013) · Neuron · doi:10.1016/j.neuron.2013.05.023 · Cited by 279
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Model-Free Reconstruction of Excitatory Neuronal Connectivity from Calcium Imaging Signals
Stetter, O., Battaglia, D. A., Soriano, J., & Geisel, T. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002653 · Cited by 254
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“The anatomical data includes total synapse count, average connection probability, number of common neighbors (see Perin et al., 2011), number of convergent and divergent neurons, and the mean number of synapses per connection (synapses/connection) for all connections between pairs of neurons…”
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Watanabe, T., Rees, G., & Masuda, N. (2019) · eLife · doi:10.7554/elife.42256 · Cited by 150
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Schmidt, M., Bakker, R., Shen, K., Bezgin, G., Diesmann, M., & van Albada, S. J. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006359 · Cited by 157
Moon, J.-Y., Lee, U., Blain-Moraes, S., & Mashour, G. A. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004225 · Cited by 136
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Lin, A., Yang, R., Dorkenwald, S., Matsliah, A., Sterling, A. R., Schlegel, P., Yu, S.-C., McKellar, C. E., Costa, M., Eichler, K., et al. (2024) · Nature · doi:10.1038/s41586-024-07968-y · Cited by 147
“The 236 overrepresentation of reciprocal connections in brains is well 237 established, and our results are consistent with previous obser- 238 vations both in Drosophila (16, 17, 19) and in other species 239 (5, 7, 14, 27, 28, 43).”
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Neurogenetics and auditory processing in developmental dyslexia
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Computing the size and number of neuronal clusters in local circuits
Perin, R., Telefont, M., & Markram, H. (2013) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2013.00001 · Cited by 121
Self-citationMicro-connectomics: probing the organization of neuronal networks at the cellular scale
Schröter, M. S., Paulsen, O., & Bullmore, E. T. (2017) · Nature reviews. Neuroscience · doi:10.1038/nrn.2016.182 · Cited by 128
How many neurons are sufficient for perception of cortical activity?
Dalgleish, H. W. P., Russell, L. E., Packer, A. M., Roth, A., Gauld, O. M., Greenstreet, F., Thompson, E., & Häusser, M. (2020) · eLife · doi:10.7554/elife.58889 · Cited by 117
An algorithm to predict the connectome of neural microcircuits
Reimann, M., King, J. G., Muller, E. B., Ramaswamy, S., & Markram, H. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00120 · Cited by 117
Self-citationSpontaneous Fluctuations and Non-linear Ignitions: Two Dynamic Faces of Cortical Recurrent Loops
Moutard, C., Dehaene, S., & Malach, R. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.018 · Cited by 99
Structured Connectivity in Cerebellar Inhibitory Networks
Rieubland, S., Roth, A., & Häusser, M. (2014) · Neuron · doi:10.1016/j.neuron.2013.12.029 · Cited by 116
Chameh, H. M., Rich, S., Wang, L., Chen, F., Zhang, L., Carlen, P. L., Tripathy, S. J., & Valiante, T. A. (2021) · Nature Communications · doi:10.1038/s41467-021-22741-9 · Cited by 110
Targeted photostimulation uncovers circuit motifs supporting short-term memory
Daie, K., Svoboda, K., & Druckmann, S. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00776-3 · Cited by 103
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Rochford, A. E., Carnicer‐Lombarte, A., Curto, V. F., Malliaras, G. G., & Barone, D. G. (2019) · Advanced Materials · doi:10.1002/adma.201903182 · Cited by 107
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Zhao, L., Beverlin, B., Netoff, T. I., & Nykamp, D. Q. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00028 · Cited by 104
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Yatsenko, D., Josić́, K., Ecker, A. S., Froudarakis, E., Cotton, R. J., & Tolias, A. S. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004083 · Cited by 97
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Homeostatic mechanisms regulate distinct aspects of cortical circuit dynamics
Wu, Y. K., Hengen, K. B., Turrigiano, G. G., & Gjorgjieva, J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1918368117 · Cited by 86
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Headley, D. B., & Paré, D. (2013) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2013.00170 · Cited by 102
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Peng, Y., Mittermaier, F. X., Planert, H., Schneider, U. C., Alle, H., & Geiger, J. R. P. (2019) · eLife · doi:10.7554/elife.48178 · Cited by 93
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High-Degree Neurons Feed Cortical Computations
Timme, N. M., Ito, S., Myroshnychenko, M., Nigam, S., Shimono, M., Yeh, F.-C., Hottowy, P., Litke, A. M., & Beggs, J. M. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004858 · Cited by 86
Recanatesi, S., Ocker, G. K., Buice, M. A., & Shea‐Brown, E. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006446 · Cited by 87
Varley, T. F., Sporns, O., Schaffelhofer, S., Scherberger, H., & Dann, B. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2207677120 · Cited by 84
Cell type- and activity-dependent extracellular correlates of intracellular spiking
Anastassiou, C. A., Perin, R., Buzsáki, G., Markram, H., & Koch, C. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00628.2014 · Cited by 87
Self-citationTime scales of memory, learning, and plasticity
Tetzlaff, C., Kolodziejski, C., Markelić, I., & Wörgötter, F. (2012) · Biological Cybernetics · doi:10.1007/s00422-012-0529-z · Cited by 87
Marti, D. N., Brunel, N., & Ostojic, S. (2018) · Physical review. E · doi:10.1103/physreve.97.062314 · Cited by 83
Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses
Ocker, G. K., Litwin-Kumar, A., & Doiron, B. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004458 · Cited by 86
Pyramidal Cell Subtypes and Their Synaptic Connections in Layer 5 of Rat Frontal Cortex
Kawaguchi, Y. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx252 · Cited by 85
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Jouhanneau, J.-S., Kremkow, J., Dorrn, A. L., & Poulet, J. F. A. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.11.011 · Cited by 77
Mendiratta, S., hussein, M., Nasser, H. A., & Ali, A. (2019) · Particle & Particle Systems Characterization · doi:10.1002/ppsc.201900195 · Cited by 89
Guescini, M., Leo, G., Genedani, S., Carone, C., Pederzoli, F., Ciruela, F., Guidolin, D., Stocchi, V., Mantuano, M., Borroto‐Escuela, D. O., et al. (2012) · Experimental Cell Research · doi:10.1016/j.yexcr.2012.01.005 · Cited by 80
Wang, G., Wyskiel, D. R., Yang, W., Wang, Y., Milbern, L. C., Lalanne, T., Jiang, X., Shen, Y., Sun, Q., & Zhu, J. (2015) · Nature Protocols · doi:10.1038/nprot.2015.019 · Cited by 84
A Brief History of Simulation Neuroscience
Fan, X., & Markram, H. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00032 · Cited by 89
Self-citation
Metadata
- 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
- 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
- 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.
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