IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1002107.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002107
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002107&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1002107?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Arthur R. Houweling & Michael Brecht, 2008. "Behavioural report of single neuron stimulation in somatosensory cortex," Nature, Nature, vol. 451(7174), pages 65-68, January.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matteo Farinella & Daniel T Ruedt & Padraig Gleeson & Frederic Lanore & R Angus Silver, 2014. "Glutamate-Bound NMDARs Arising from In Vivo-like Network Activity Extend Spatio-temporal Integration in a L5 Cortical Pyramidal Cell Model," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-21, April.
    2. Liagkouras, Konstantinos & Metaxiotis, Konstantinos, 2021. "Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1019-1036.
    3. Gong, Wenyin & Cai, Zhihua, 2009. "An improved multiobjective differential evolution based on Pareto-adaptive [epsilon]-dominance and orthogonal design," European Journal of Operational Research, Elsevier, vol. 198(2), pages 576-601, October.
    4. Jan C. Frankowski & Alexa Tierno & Shreya Pavani & Quincy Cao & David C. Lyon & Robert F. Hunt, 2022. "Brain-wide reconstruction of inhibitory circuits after traumatic brain injury," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    5. Oren Amsalem & Hidehiko Inagaki & Jianing Yu & Karel Svoboda & Ran Darshan, 2024. "Sub-threshold neuronal activity and the dynamical regime of cerebral cortex," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    6. Andrea Ponti & Antonio Candelieri & Ilaria Giordani & Francesco Archetti, 2023. "Intrusion Detection in Networks by Wasserstein Enabled Many-Objective Evolutionary Algorithms," Mathematics, MDPI, vol. 11(10), pages 1-14, May.
    7. David Quintana & Roman Denysiuk & Sandra García-Rodríguez & Antonio Gaspar-Cunha, 2017. "Portfolio implementation risk management using evolutionary multiobjective optimization," Post-Print hal-01881379, HAL.
    8. Yunsong Han & Hong Yu & Cheng Sun, 2017. "Simulation-Based Multiobjective Optimization of Timber-Glass Residential Buildings in Severe Cold Regions," Sustainability, MDPI, vol. 9(12), pages 1-18, December.
    9. Laumanns, Marco & Zenklusen, Rico, 2011. "Stochastic convergence of random search methods to fixed size Pareto front approximations," European Journal of Operational Research, Elsevier, vol. 213(2), pages 414-421, September.
    10. Ivo Couckuyt & Dirk Deschrijver & Tom Dhaene, 2014. "Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization," Journal of Global Optimization, Springer, vol. 60(3), pages 575-594, November.
    11. Omri Harish & David Hansel, 2015. "Asynchronous Rate Chaos in Spiking Neuronal Circuits," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-38, July.
    12. Arjun A. Bhaskaran & Théo Gauvrit & Yukti Vyas & Guillaume Bony & Melanie Ginger & Andreas Frick, 2023. "Endogenous noise of neocortical neurons correlates with atypical sensory response variability in the Fmr1−/y mouse model of autism," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    13. Derbel, Bilel & Humeau, Jérémie & Liefooghe, Arnaud & Verel, Sébastien, 2014. "Distributed localized bi-objective search," European Journal of Operational Research, Elsevier, vol. 239(3), pages 731-743.
    14. Balázs Ujfalussy & Tamás Kiss & Péter Érdi, 2009. "Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields," PLOS Computational Biology, Public Library of Science, vol. 5(9), pages 1-16, September.
    15. Hang Xu, 2024. "A Dynamic Tasking-Based Evolutionary Algorithm for Bi-Objective Feature Selection," Mathematics, MDPI, vol. 12(10), pages 1-23, May.
    16. Sergio Cabello, 2023. "Faster distance-based representative skyline and k-center along pareto front in the plane," Journal of Global Optimization, Springer, vol. 86(2), pages 441-466, June.
    17. Sven Schulz & Udo Buscher & Liji Shen, 2020. "Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices," Journal of Business Economics, Springer, vol. 90(9), pages 1315-1343, November.
    18. Lourdes Uribe & Johan M Bogoya & Andrés Vargas & Adriana Lara & Günter Rudolph & Oliver Schütze, 2020. "A Set Based Newton Method for the Averaged Hausdorff Distance for Multi-Objective Reference Set Problems," Mathematics, MDPI, vol. 8(10), pages 1-29, October.
    19. Giuseppe Chindemi & Marwan Abdellah & Oren Amsalem & Ruth Benavides-Piccione & Vincent Delattre & Michael Doron & András Ecker & Aurélien T. Jaquier & James King & Pramod Kumbhar & Caitlin Monney & Ro, 2022. "A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    20. Houssem R. E. H. Bouchekara & Yusuf A. Sha’aban & Mohammad S. Shahriar & Makbul A. M. Ramli & Abdullahi A. Mas’ud, 2023. "Wind Farm Layout Optimization/Expansion with Real Wind Turbines Using a Multi-Objective EA Based on an Enhanced Inverted Generational Distance Metric Combined with the Two-Archive Algorithm 2," Sustainability, MDPI, vol. 15(3), pages 1-32, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1002107. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.