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Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties

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  • Etay Hay
  • Sean Hill
  • Felix Schürmann
  • Henry Markram
  • Idan Segev

Abstract

The thick-tufted layer 5b pyramidal cell extends its dendritic tree to all six layers of the mammalian neocortex and serves as a major building block for the cortical column. L5b pyramidal cells have been the subject of extensive experimental and modeling studies, yet conductance-based models of these cells that faithfully reproduce both their perisomatic Na+-spiking behavior as well as key dendritic active properties, including Ca2+ spikes and back-propagating action potentials, are still lacking. Based on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics. We used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca2+ spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties. Furthermore, we show a useful way to analyze model parameters with our sets of models, which enabled us to identify some of the mechanisms responsible for the dynamic properties of L5b pyramidal cells as well as mechanisms that are sensitive to morphological changes. This automated framework can be used to develop a database of faithful models for other neuron types. The models we present provide several experimentally-testable predictions and can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities. Author Summary: The pyramidal cell of layer 5b in the mammalian neocortex extends its dendritic tree to all six layers of cortex, thus receiving inputs from the entire cortical column and supplying the major output of the column to other brain areas. L5b pyramidal cells have been the subject of extensive experimental and modeling studies, yet realistic models of these cells that faithfully reproduce both their perisomatic Na+ and dendritic Ca2+ firing behaviors are still lacking. Using an automated algorithm and a large body of experimental data, we generated a set of models that faithfully replicate a range of active dendritic and perisomatic properties of L5b pyramidal cells, as well as the experimental variability of the properties. Furthermore, we show a useful way to analyze model parameters with our sets of models, which enabled us to identify some of the mechanisms responsible for the dynamic properties of L5b pyramidal cells as well as mechanisms that are sensitive to morphological changes. This framework can be used to develop a database of faithful models for other neuron types. The models we present can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities.

Suggested Citation

  • Etay Hay & Sean Hill & Felix Schürmann & Henry Markram & Idan Segev, 2011. "Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties," PLOS Computational Biology, Public Library of Science, vol. 7(7), pages 1-18, July.
  • Handle: RePEc:plo:pcbi00:1002107
    DOI: 10.1371/journal.pcbi.1002107
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    References listed on IDEAS

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    1. Masanori Murayama & Enrique Pérez-Garci & Thomas Nevian & Tobias Bock & Walter Senn & Matthew E. Larkum, 2009. "Dendritic encoding of sensory stimuli controlled by deep cortical interneurons," Nature, Nature, vol. 457(7233), pages 1137-1141, February.
    2. Beume, Nicola & Naujoks, Boris & Emmerich, Michael, 2007. "SMS-EMOA: Multiobjective selection based on dominated hypervolume," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1653-1669, September.
    3. Arthur R. Houweling & Michael Brecht, 2008. "Behavioural report of single neuron stimulation in somatosensory cortex," Nature, Nature, vol. 451(7174), pages 65-68, January.
    4. Michael Brecht & Miriam Schneider & Bert Sakmann & Troy W. Margrie, 2004. "Whisker movements evoked by stimulation of single pyramidal cells in rat motor cortex," Nature, Nature, vol. 427(6976), pages 704-710, February.
    5. Matthew E. Larkum & J. Julius Zhu & Bert Sakmann, 1999. "A new cellular mechanism for coupling inputs arriving at different cortical layers," Nature, Nature, vol. 398(6725), pages 338-341, March.
    6. Jackie Schiller & Guy Major & Helmut J. Koester & Yitzhak Schiller, 2000. "NMDA spikes in basal dendrites of cortical pyramidal neurons," Nature, Nature, vol. 404(6775), pages 285-289, March.
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    Cited by:

    1. Yichen Zhang & Gan He & Lei Ma & Xiaofei Liu & J. J. Johannes Hjorth & Alexander Kozlov & Yutao He & Shenjian Zhang & Jeanette Hellgren Kotaleski & Yonghong Tian & Sten Grillner & Kai Du & Tiejun Huan, 2023. "A GPU-based computational framework that bridges neuron simulation and artificial intelligence," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    2. Alex D Bird & Hermann Cuntz, 2016. "Optimal Current Transfer in Dendrites," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-12, May.
    3. Timothy Rumbell & James Kozloski, 2019. "Dimensions of control for subthreshold oscillations and spontaneous firing in dopamine neurons," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-34, September.

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