cerebellar-models 0.12.0

Implementation of the BSB framework for cerebellar cortex reconstructions and simulations.

DBBS Cerebellar models: Models of the cerebellum, using the BSB.

This repository provides the code, configuration and morphology data to reconstruct and simulate cerebellar cortex circuits using the Brain Scaffold Builder framework. These models are based on the iterative work of the Department of Brain and Behavioral Sciences (DBBS) of the university of Pavia.

:closed_book: Read the documentation on https://cerebellar-models.readthedocs.io/en/latest

Installation

cerebellar-models is a package that contains implementation of connectivity or placement rules for BSB. The cerebellar-models package requires Python 3.10+.

Pre-requirements

BSB parallelizes the network reconstruction using MPI, and translates simulator instructions to the simulator backends with it as well, for effortless parallel simulation. To use MPI from Python the mpi4py package is required, which in turn needs a working MPI implementation installed in your environment.

On your local machine you can install OpenMPI:

sudo apt-get update && sudo apt-get install -y libopenmpi-dev openmpi-bin

Then, depending on the types of simulations, you want to perform you will need to install the simulator(s) of your interest. Please follow the instructions:

• For the NEST simulator
• For the NEURON simulator

Cerebellar-models installation

pip

Cerebellar-models can be installed from PyPI through pip:

pip install cerebellar-models

To include simulation support for NEST and/or NEURON:

    pip install cerebellar-models[nest]  # For NEST
    pip install cerebellar-models[neuron]  # For NEURON

Developers

Developers best use pip's editable install. This creates a live link between the installed package and the local git repository:

 git clone [e-mail]:dbbs-lab/cerebellar-models
 cd cerebellar_models
 # do not forget to add the optional simulators supports required.
 pip install -e .[nest]

Contents

Morphologies

Cerebellar cortex neuron morphology reconstructions used in our microcircuits are stored in the morphologies folder. The folder contains also more information about each file.

BSB configuration files for cerebellar cortex circuits

In this repository, the BSB configurations are stored in the configurations folder. Sub-folders within configurations corresponds to different species reconstructions. Each specie have its default configuration to reconstruct the models as well as extensions that can be added to override or extend the default one. This includes the different simulation' paradigms.

Building a circuit

Depending on the circuit you wish to obtain and/or simulate, the process will vary. This package provides a command-line interface to generate the BSB configuration of the canonical circuits developed by the DBBS based on a few choices.

Assuming you are in the cerebellar-models folder, run the following command in your terminal:

cerebellar-models configure

Once you filled the forms provided by the command within your terminal, your BSB configuration should be ready to be compiled:

bsb compile circuit.yaml -v4 --clear

This command will produce the desired circuit of the cerebellar cortex and store it in an .hdf5 file. This process might take a while depending on your machine.

Running a simulation

As for the previous paragraph the following command might vary depending on your reconstruction and simulation.

Assuming you are in the cerebellar-models folder, and you want to run the simulation simulation_name, run the following command in your terminal:

bsb simulate cerebellum.hdf5 simulation_name -o output_file_name -v4

Acknowledgements

This research has received funding from the European Union’s Horizon 2020 Framework Program for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3) and Specific Grant Agreement No. 785907 (Human Brain Project SGA2) and from Centro Fermi project “Local Neuronal Microcircuits” to ED. The project is also receiving funding from the Virtual Brain Twin Project under the European Union's Research and Innovation Program Horizon Europe under grant agreement No 101137289.

We acknowledge the use of EBRAINS platform and Fenix Infrastructure resources, which are partially funded from the European Union’s Horizon 2020 research and innovation programme under the Specific Grant Agreement No. 101147319 (EBRAINS 2.0 Project) and through the ICEI project under the grant agreement No. 800858 respectively.