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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

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Perin, Rodrigo, Berger, Thomas K., Markram, Henry·2011·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.1016051108PMC PMC3069183
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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.

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  1. 10.1016/j.neuron.2004.08.012 · DOI

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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
ORCID
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
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

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