MEArec: Fast and customizable simulation of extracellular recordings on Multi-Electrode-Arrays

MEArec is a project for using generating biophysical extracellular neural recording on Multi-Electrode Arrays (MEA). The recording generations combines a Extracellular Action Potentials (EAP) templates generation and spike trains generation. The recordings are built by convoluting and modulating EAP templates with spike trains and adding noise.

To clone this repo open your terminal and run:

git clone https://github.com/alejoe91/MEArec.git

Pre-requisites and Installation

The neural simulations rely on NEURON 7.5 (https://www.neuron.yale.edu/neuron/) (it can be downloaded from https://neuron.yale.edu/ftp/neuron/versions/) and the LFPy 2.0. NEURON should be installed manually (I you are running a Linux system add export PYTHONPATH="/usr/local/nrn/lib/python/:$PYTHONPATH" to your .bashrc. On Linux systems you also install libncurses: sudo apt install lib32ncurses5-dev. MEArec also uses LFPy (https://github.com/LFPy/LFPy), which requires mpi installation. On linux distributions, run: sudo apt install libopenmpi-dev.

After installing NEURON and openmpi, the MEArec package can be installed with:

pip install MEArec

or, from the cloned folder:

python setup.py develop

You could also create a conda environment (https://www.anaconda.com/download/) using the environment file. Open your terminal and run:

For Anaconda conda env create -f environment.yml

Then activate the environment:

On Linux/MacOS: source activate mearec

On Windows: activate mearec

mearec is a command line interface: to show available commands you can run: mearec --help

Usage: mearec [OPTIONS] COMMAND [ARGS]...

  MEArec: Fast and customizable simulation of extracellular recordings on
  Multi-Electrode-Arrays

Options:
  --help  Show this message and exit.

Commands:
  default-config          Print default configurations
  gen-recordings          Generates recordings from TEMPLATES and...
  gen-templates           Generates EAP templates on multi-electrode arrays...
  recfromhdf5             Convert recordings from hdf5
  rectohdf5               Convert recordings to hdf5
  set-cell-models-folder  Set default cell_models folder
  set-recordings-folder   Set default recordings output folder
  set-recordings-params   Set default templates output folder
  set-templates-folder    Set default templates output folder
  set-templates-params    Set default templates output folder
  tempfromhdf5            Convert templates from hdf5
  temptohdf5              Convert templates to hdf5

Configure simulations

the first time a command is run, mearec will generate a configuration file in $HOME/.config/mearec/mearec.conf and copy some default parameters in the $HOME/.config/mearec/default_params folder.

Before running any simulation, tt is necessary to point to the package where the cell models folder are. Cell models can be downloaded from the Neocortical Micro Circuit Portal https://bbp.epfl.ch/nmc-portal/welcome (13 models from layer 5 for testing are already included in the repo - cell_models/bbp/). The cell models folder can be set with:

mearec set-cell-models-folder folder (e.g. mearec set-cell-models-folder MEArec/cell_models/bbp)

Moreover, the user can set the default folders and params yaml files for templates, spike trains, and recording outputs with:

mearec set-templates-folder folder

mearec set-spiketrains-folder folder

mearec set-recordings-folder folder

mearec set-templates-params folder

mearec set-spiketrains-params folder

mearec set-recordings-params folder

(by default in $HOME/.config/mearec/templates, $HOME/.config/mearec/spiketrains, and $HOME/.config/mearec/recordings)

EAP templates generation

The command to generate templates is:

mearec gen_templates

Run it with --help to show available arguments.

In order to check available MEA probes, just run mearec gen_templates, or do not provide the --probe option. During the first run of the scripts, the NEURON model in the cell_models/bbp/ will be first compiled. Simulation parameters can be changed from the params/template_params.yaml file, provided with an external yaml file (with the --params option) or overwritten with command line argument.

EAP templates will be generated and saved in templates\<rotation-type>\templates_<n>_<meaname>_<date> (where n is the number of EAPs per cell model) and they can be loaded with the tools.load_eaps(path-to-recordings) function.

Recordings generation

The command to generate recordings is:

mearec gen_recordings

Run it with --help to show available arguments.

Run the command with --template or -t option to point to the templates path. In brief, first spike trains are generated with the SpikeTrainGenerator class based on the spiketrain parameters in the recording_params. Then, the templates are selected based on the number of simulated spike trains and other parameters (templates parameters in the recording_params). Then, templates are convoluted in time with the spikes to create clean recordings. During convolution, single eap can be modulated either at the template level, or at the single electrode level (eith the --modulation ot -m option - none | template | electrode). Finally, a gaussian noise is added to the clean recordings (--noise-lev or -nl allows to change the noise sd in uV) and the recordings are filtered (unless the --no-filter option is used). All parameters for convolution and noise can be set in the recordings parameters in the recording_params.

Recordings are saved in recordings\recording_<neurons>cells_<meaname>_<duration>s_<noise-level>uV_<date> and they can be loaded with the tools.load_recordings(path-to-recordings) function.

Save and load in hdf5 format

mearec temptohdf5 | tempfromhdf5 | rectohdf5 | recfromhdf5 allow the user to convert the output folders to and from hdf5.

Loading the simulated data

The example_plotting.py script shows how to load eap templates, spike trains, and recordings. It also shows how to use some plotting functions in tools.py.

Running the simulations in Python (without command line interface)

It is also possible to run the simulation in the python environment.

import MEArec as mr

# Generate templates
temp_gen = mr.gen_templates('path-to-cell-models-folder')
rec_gen = mr.gen_recordings(tempgen = temp_gen)

temp_gen is a TemplateGenerator object that has templates, locations, rotations, celltypes, and info fields. rec_gen is a RecordingGenerator object that has recordings, positions, spiketrains, locations, peaks, sources, and info fields.

The user can pass a params argument (either a dict or a path to a yaml file) to both gen_templates and gen_recordings to overwrite default simulation parameters (see MEArec/default_params/templates_params.yaml and MEArec/default_params/recordings_params.yaml for default values and explanation).