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.
“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]
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Self-citation
- 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
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