DOI: 10.5281/zenodo.15006075
title: Blue Brain Project Canonical Electrical Neuron Models

description: Blue Brain Project Canonical E-Models

Authors

Darshan Mandge, Aurélien Jaquier, Ilkan Kiliç, Tanguy Damart, Alexis Arnaudon, Lida Kanari, Werner Van Geit, Henry Markram

EPFL - Blue Brain Project

Last Modified

2025-03-17

Summary

This repository consists of canonical electrical models (e-models) based on the somatosensory cortex (SSCx) and thalamus neuron data. These canonical e-models were used to construct morpho-electrical (me-models) of different brain regions for whole brain simulations.

Two sets of 16 e-models were constructed:



one with 3D reconstructed morphology (detailed)

the other with point-neuron (ball and stick) morphology representing a soma and axon initial segment (placeholders).


Each set has



eleven SSCx-based e-models: Excitatory:cADpyr and Inhibitory: bAC,bIR,bNAC,bSTUT,cAC,cIR,cNAC,cSTUT,dNAC and dSTUT

five thalamus-based e-models: bAC_IN, cNAD_noscltb, cAD_noscltb, dNAD_ltb and dAD_ltb


Repository Structure

The repository has two folder folders:



detailed: It contains e-models constructed using 3D reconstructed morphology

placeholders: It contains e-models constructed using ball-and-stick morphology with a soma and axon initial segment.


E-models/E-types

Each of the above folders has subfolders for 16 e-types (electrical firing types) corresponding to an e-model.

SSCx-based e-models are:



bAC (burst accommodating)

bIR (burst irregular firing)

bNAC (burst non-accommodating)

bSTUT (burst stuttering)

cAC (continuous accommodating)

cADpyr (continuous adapting-type pyramidal neuron)

cIR (continuous irregular)

cNAC (continuous non-accommodating)

cSTUT (continuous stuttering)

dNAC (delayed accommodating)

dSTUT (delayed stuttering)


Thalamus-based e-models are:



dNAD_ltb (delayed adapting low-threshold bursting)

dAD_ltb (delayed adapting low-threshold bursting)

cNAD_noscltb (continuous non-adapting non-oscillating low threshold bursting)

cAD_noscltb (continuous adapting non-oscillating low-threshold bursting)

bAC_IN (burst accomodating interneuron)


The SSCx-based were reconstructed based on the data reported in [2-4] and thalamus-based models are based on data reported [5,6].

Files and Folders





Resources generated from the e-model building pipeline BluePyEModel.



EM_*.json

EMC_*.json

EMPS_*.json

EMS_*.json

EMW_*.json

ETC_*.json

FCC_*.json


For more details about these files and resources, refer to the  nexus access point example in the BluePyEModel Github repository.




mechanisms folder: contains the ionic mechanism .mod files used for the e-model




model.hoc: the model hoc file with final parameters used to run via the NEURON simulator




Reconstructed or placeholder morphologies in 3 formats: .asc, .swc and .h5.



These e-models use the NEURON simulator[7]. You can use these models on the Open Brain Platform or locally on your PC using BlueCellulab using the example.

License

CC BY 4.0. For the mechanism (.mod) files for which the original source is available on ModelDB, any specific licenses mentioned on ModelDB, or the generic License of ModelDB apply.

Funding and Acknowledgement

This work was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.

References



BluePyEModel [Computer software]. https://doi.org/10.5281/zenodo.8283490

Reva, M., Rössert, C., Arnaudon, A., Damart, T., Mandge, D., Tuncel, A., Ramaswamy, S., Markram, H., & Geit, W. V. (2023). A universal workflow for creation, validation, and generalization of detailed neuronal models. Patterns, 0(0). https://doi.org/10.1016/j.patter.2023.100855

Isbister, J. B., Ecker, A., Pokorny, C., Bolaños-Puchet, S., Santander, D. E., Arnaudon, A., Awile, O., Barros-Zulaica, N., Alonso, J. B., Boci, E., Chindemi, G., Courcol, J.-D., Damart, T., Delemontex, T., Dietz, A., Ficarelli, G., Gevaert, M., Herttuainen, J., Ivaska, G., … Reimann, M. W. (2025). Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation. eLife, 13. https://doi.org/10.7554/eLife.99693.2

Markram, H., Muller, E., Ramaswamy, S., Reimann, M. W., Abdellah, M., Sanchez, C. A., ... & Schürmann, F. (2015). Reconstruction and simulation of neocortical microcircuitry. Cell, 163(2), 456-492. https://doi.org/10.1016/j.cell.2015.09.029

Iavarone, E., Simko, J., Shi, Y., Bertschy, M., García-Amado, M., Litvak, P., Kaufmann, A.-K., O’Reilly, C., Amsalem, O., Abdellah, M., Chevtchenko, G., Coste, B., Courcol, J.-D., Ecker, A., Favreau, C., Fleury, A. C., Geit, W. V., Gevaert, M., Guerrero, N. R., … Hill, S. L. (2023). Thalamic control of sensory processing and spindles in a biophysical somatosensory thalamoreticular circuit model of wakefulness and sleep. Cell Reports, 42(3). https://doi.org/10.1016/j.celrep.2023.112200

Iavarone, E., Yi, J., Shi, Y., Zandt, B. J., O’reilly, C., Van Geit, W., ... & Hill, S. L. (2019). Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons. PLoS computational biology, 15(5), e1006753. https://doi.org/10.1371/journal.pcbi.1006753

Carnevale, N. T., & Hines, M. L. (2006). The NEURON book. Cambridge University Press. https://doi.org/10.1017/CBO9780511541612

Tuncel, A., Van Geit, W., Gevaert, M., Torben-Nielsen, B., Mandge, D., Kılıç, İ., ... & Markram, H. (2024). BlueCelluLab: Biologically Detailed Neural Network Experimentation API. Journal of Open Source Software, 9(100), 7026. https://doi.org/10.21105/joss.07026


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