Skip to main navigation Skip to search Skip to main content

Rich cell-type-specific network topology in neocortical microcircuitry

  • Eyal Gal*
  • , Michael London
  • , Amir Globerson
  • , Srikanth Ramaswamy
  • , Michael W. Reimann
  • , Eilif Muller
  • , Henry Markram
  • , Idan Segev
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

88 Scopus citations

Abstract

Uncovering structural regularities and architectural topologies of cortical circuitry is vital for understanding neural computations. Recently, an experimentally constrained algorithm generated a dense network reconstruction of a ∼0.3-mm3 volume from juvenile rat somatosensory neocortex, comprising ∼31,000 cells and ∼36 million synapses. Using this reconstruction, we found a small-world topology with an average of 2.5 synapses separating any two cells and multiple cell-type-specific wiring features. Amounts of excitatory and inhibitory innervations varied across cells, yet pyramidal neurons maintained relatively constant excitation/inhibition ratios. The circuit contained highly connected hub neurons belonging to a small subset of cell types and forming an interconnected cell-type-specific rich club. Certain three-neuron motifs were overrepresented, matching recent experimental results. Cell-type-specific network properties were even more striking when synaptic strength and sign were considered in generating a functional topology. Our systematic approach enables interpretation of microconnectomics 'big data' and provides several experimentally testable predictions.

Original languageEnglish
Pages (from-to)1004-1013
Number of pages10
JournalNature Neuroscience
Volume20
Issue number7
DOIs
StatePublished - 27 Jun 2017

Bibliographical note

Publisher Copyright:
© 2017 Nature America, Inc., part of Springer Nature. All rights reserved.

Fingerprint

Dive into the research topics of 'Rich cell-type-specific network topology in neocortical microcircuitry'. Together they form a unique fingerprint.

Cite this