Circuit Model Suite

A suite to build, analyze and visualize circuits.


The purpose of Connectome Utilities is to simplify running topological analyses on detailed models of networks by providing a bridge between existing analyses and the model representation. The purpose is not to provide the analyses themselves, there are great existing packages and solutions for that. But to simplify their application to the case of complex, non-homogeneous networks associated with interdependent node and edge properties. This comes in the form of two types of functionality: First, loading complex connectomes into a reduced representation that still keeps salient details. Second, automate standard operations, such as extraction of specific subnetworks and generation of statistical controls.

With respect to the first point, loading from SONATA models is provided. But once loaded, the representation is independent from Sonata and the second point provides utility also for non-Sonata networks.


CoreNEURON is a compute engine for the NEURON simulator optimised for both memory usage and computational speed. Its goal is to simulate large cell networks with small memory footprint and optimal performance.

Pair recording app



Blue Brain Simulation and Neural network Analysis Productivity layer (Blue Brain SNAP).

Blue Brain SNAP is a Python library for accessing BlueBrain circuit models represented in SONATA format.

Blue Brain Brayns

Blue Brain’s Brayns interactive ray tracing can highlight areas of the circuits where cells touch each other and where synapses are being created. In combination with ‘global illumination,’ which uses light, shadow, and depth of field effects to simulate photo-realistic images, this technique makes it easier to visualize how the neurons function. Brayns provides an abstraction of the underlying rendering engines, so that the best possible acceleration libraries can be used for the relevant hardware.

As a minimalistic library that allows optimized ray tracing rendering of meshes and parametric geometry, Brayns makes it possible to use the best rendering engine depending on the case.


A C++ / Python reader for SONATA circuits


Neurodamus is the BBP in-house developed application for setting up large-scale neuronal simulations. It has traditionally been implemented as a set of extensions to Neuron, in the form of .hoc and .mod files. The parameters of the simulation are loaded from a configuration file, by default BlueConfig.

To address several limitations of the Hoc implementation, including development effort, the high-level layers of Neurodamus have been reimplemented in Python. Such implementation effectively makes available to the user a Python module with a comprehensive API, suitable to fine control simulation aspects, as well as inspect and eventually adapt the simulations as intended.