Below are instances of models from the reconstructed neocortical microcircuit. The models are made available in the native format in use at the BBP, based on the NEURON simulation package (http://www.neuron.yale.edu).
See the Tools page for instructions on the execution of simulations using NEURON based models.
Models of individual neurons (in the NEURON simulation environment) can be obtained from the me-type fact sheets. A model package contains a morphology file, the ion channel descriptions, the synapse descriptions and templates to instantiate the cell. There is code to construct the model, distribute the ion channels and synapses on the morphology, generate a GUI, and inject current clamp and synaptic input into the model.
The morphology description is a formatted as a Neurolucide ASCII file. The ion channel and synapse description are available as NEURON MOD files. The code for the templates, GUI and simulations is written in the NEURON HOC language or Python. The files containing the synaptic parameters and m-type mappings are tab-separated files.
The complete set of neuron models is available here
You can select specific neuron models in the interface below or by browsing to the relevant me-type fact sheet.
A JSON file with some extra data about each neuron model, like the resting membrane potential, input resistance and membrane time constant, can be downloaded here
Thanks to a collaboration with Padraig Gleeson of the Open Source Brain, the model packages can be converted to NeuroML2:
A file with the NeuroML2 versions of the model packages is available here
.JSON file containing information about the circuit can be downloaded here:
Units are the same as in the circuit factsheet.
.JSON files containing information about the layers can be downloaded here:
Units are the same as in the layers factsheets.
.JSON files containing anatomical and physiological data for all the pathways can be downloaded here:
Units are the same as in the pathways factsheets.
Connectivity of an instance of a modeled microcircuit is available as an HDF5 file.
Gal, E., London, M., Globerson, A., Ramaswamy, S., Reimann, M.W., Muller, E., Markram, H., and Segev, I. (2017).
Rich cell-type-specific network topology in neocortical microcircuitry. Nat. Neurosci.
Reimann, M.W., Horlemann, A.-L., Ramaswamy, S., Muller, E.B., and Markram, H. (2017).
Morphological Diversity Strongly Constrains Synaptic Connectivity and Plasticity. Cereb. Cortex 1-16.
To download click the file name
- AVERAGE: 7 stochastic instances of a model microcircuit based on averaged measurements of neuron densities, E/I balance and layer widths.
- CONTROLS: 35 stochastic instances of a CONTROL microcircuit where connectivity is based on the overlap of AVERAGE morphologies instead of individual morphologies. To be used as a CONTROL. Specifically, there are 5 random instances, each preserving the number of connections found in one of 7 stochastic instances of a reconstructed microcircuit.
- INDIVIDUALS: 35 stochastic instances of model microcircuits based on individual measurements of neuron densities, E/I balance and layer widths. Specifically, 7 random instances for each of 5 such measurements.
Welcome to the Blue Brain Project ("BBP"") Neocortical Microcircuit Collaboration Portal ("Portal"").
This Portal provides an online public resource of the BBP's first release of a digital reconstruction of the microcircuitry of juvenile Rat somatosensory cortex, access to experimental databases used in the reconstruction, and the resulting models (collectively "Databases and Models""). The following functionality is provided through this Portal to support community engagement to use and refine the reconstruction.
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The Citation shall be as follows:
1. Markram H, et al. (2015). Reconstruction and Simulation of Neocortical Microcircuitry. Cell 163:2, 456 - 492. doi: 10.1016/j.cell.2015.09.029
2. Ramaswamy S, et al., (2015). The Neocortical Microcircuit Collaboration Portal: A Resource for Rat Somatosensory Cortex. Front. Neural Circuits 9:44. doi: 10.3389/fncir.2015.00044
3. Reimann MW, et al., (2015). An Algorithm to Predict the Connectome of Neural Microcircuits. Front. Comput Neurosci. 9:28. doi: 10.3389/fncom.2015.00120
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