A suite to handle single-cell electrophysiological data and to build and validate detailed electrical models.
- eFEL — Electrophys Feature Extraction Library
- BluePyEfe — Blue Brain Python E-feature extraction
- BluePyOpt — Blue Brain Python Optimisation Library
- BluePyMM — Blue Brain Python Cell Model Management
- BlueCelluLab - Blue Brain Cellular Laboratory
- BluePyEModel - Blue Brain Python Electrical Modeling Pipeline
- EModelRunner — Runs cells from stand-alone packages
- BlueNaaS-SingleCell - Interacts with single cell models through a web application
- Currentscape - Plot currents in electrical models
- SSCxEModelExamples - Reproduce Reva, Maria, et al. "A universal workflow for creation, validation and generalization of detailed neuronal models" (2023).
Useful links: GitHub repo, Documentation.
Electrophys Feature Extraction Library
The Electrophys Feature Extraction Library (eFEL) allows neuroscientists to automatically extract features from time series data recorded from neurons (both in vitro and in silico). Examples are the action potential width and amplitude in voltage traces recorded during whole-cell patch clamp experiments. The user of the library provides a set of traces and selects the features to be calculated. The library will then extract the requested features and return the values to the user.
Useful links: GitHub repo, Documentation.
Blue Brain Python E-feature extraction
BluePyEfe aims at easing the process of reading experimental recordings and extracting batches of electrical features from these recordings. To do so, it combines trace reading functions and features extraction functions from the eFel library.
Useful links: GitHub repo, Documentation.
Blue Brain Python Optimisation Library
The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible framework for data-driven model parameter optimisation that wraps and standardises several existing open-source tools.
Useful links: GitHub repo, Documentation.
Blue Brain Python Cell Model Management
When building a network simulation, biophysically detailed electrical models (e-models) need to be tested for every morphology that is possibly used in the circuit.
Useful links: GitHub repo, Documentation.
Blue Brain Cellular Laboratory
BlueCelluLab is designed for simulations and experiments on individual cells or groups of cells. Suitable use cases for BlueCelluLab include:
- Scripting and statistical analysis for single cells or cell pairs.
- Lightweight, detailed reporting on specific state variables after simulation.
- Developing synaptic plasticity rules.
- Validating dynamics of synaptic properties.
- Automating in-silico whole-cell patching experiments.
- Debugging, both scientifically and computationally.
Useful links: GitHub repo, Documentation.
Blue Brain Python Electrical Modeling Pipeline
The Blue Brain Python Electrical Modeling Pipeline (BluePyEModel) is a Python package facilitating the configuration and execution of electrical neuron model (e-model) building tasks. It covers tasks such as extraction of electrical features from electrophysiology data, e-model parameters optimisation and model validation. As such, it builds on top of eFEL, BluePyEfe and BluePyOpt.
Useful links: GitHub repo, Documentation.
Runs cells from stand-alone packages
EModelRunner is a python library designed to run the cell models provided by the Blue Brain portals in a simple and straightforward way.
Useful links: GitHub repo, Documentation
Interacts with single cell models through a web application
Blue-Neuroscience-as-a-Service-SingleCell is an open source web application. It enables users to quickly visualize single cell model morphologies in 3D or as a dendrogram. Using a simple web user interface, single cell simulations can be easily configured and launched, producing voltage traces from selected compartments.
Currentscape is a Python tool enabling scientists to easily plot the currents in electrical neuron models. The code is based on the paper Alonso and Marder, 2019.
Useful links: GitHub repo, Documentation.
Software repository to reproduce the results of the publication below.
Reva, M., Rössert, C., Arnaudon, A., Damart, T., Mandge, D., Tuncel, A., Ramaswamy, S., Markram, H., & Van Geit, W. (2023). A universal workflow for creation, validation, and generalization of detailed neuronal models. Patterns, 100855. https://doi.org/10.1016/j.patter.2023.100855
In the paper, we present a generalized automated workflow for the creation of robust electrical models and illustrate its performance by building cell models for the rat somatosensory cortex. Each model is based on a 3D morphological reconstruction and a set of ionic mechanisms. We use an evolutionary algorithm to optimize neuronal parameters to match the electrophysiological features extracted from experimental data. Then we validate the optimized models against additional stimuli and assess their generalizability on a population of similar morphologies. Compared to the state-of-the-art canonical models, our models show 5-fold improved generalizability. This versatile approach can be used to build robust models of any neuronal type.