Single Cell E-Model Suite

A suite to handle single-cell electrophysiological data and to build and validate detailed electrical models.

The analysis stage is initially for model validation and then subsequently for simulation-based investigations of brain function and dysfunction, diagnostic tools and possible treatments. The analysis step of Blue Brain’s workflow is supported by three open source software tools – the Electrophysiology Feature Extraction Library (eFEL)Blue Brain Python E-feature extraction (BluePyEfe) and NeuroM.

eFEL

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. This software is used in use cases in the EBRAINS Cellular Level Simulation Platform.

BluePyOpt

The Blue Brain Python Optimization Library (BluePyOpt) is an extensible framework for data-driven model parameter optimization that wraps and standardises several existing open-source tools.

It simplifies the task of creating and sharing these optimization, and the associated techniques and knowledge.

This is achieved by abstracting the optimization and evaluation tasks into various reusable and flexible discrete elements according to established best-practices.

Further, BluePyOpt provides methods for setting up both small- and large-scale optimizations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures.

This software is used in use cases in the EBRAINS Cellular Level Simulation Platform.

BluePyMM

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.

E-models can e.g. be obtained using BluePyOpt by data-driven model parameter optimization. Developing e-models can take a lot of time and computing resources.

Therefore, these models are not reoptimized for every morphology in the network. Instead we want to test if an existing e-model matches that particular morphology `well enough’.

This process is called Cell Model Management (MM). It takes as input a morphology release, a circuit recipe and a set of e-models with some extra information. Next, it finds all possible (morphology, e-model)-combinations (me-combos) based on e-type, m-type, and layer as described by the circuit recipe, and calculates the scores for every combination.

Finally, it writes out the resulting accepted me-combos to a database, and produces a report with information on the number of matches – BluePyMM. This software is used in use cases in the EBRAINS Cellular Level Simulation Platform.

BluePyEFE

Blue Brain Python E-feature extraction (BluePyEfe) aims to ease 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.

BluePyEfe outputs protocols and features files in the format used by BluePyOpt for neuron electrical model building.

This software is used in use cases in the EBRAINS Cellular Level Simulation Platform.

EModelRunner

EModelRunner is a python library designed to run the cell models provided by the Blue Brain portals in a simple and straightforward way.