Software

STEPS

steps-iconSTEPS is a package for exact stochastic simulation of reaction- diffusion systems in arbitrarily complex 3D geometries. Our core simulation algorithm is an implementation of Gillespie's SSA, extended to deal with diffusion of molecules over the elements of a 3D tetrahedral mesh.
While it was mainly developed for simulating detailed models of neuronal signaling pathways in stretches of dendrites and around synapses, it is a general tool and can be used for studying any biochemical pathway in which spatial gradients and morphology are thought to play a role.
We have implemented STEPS as a set of Python modules, which means STEPS users can use Python scripts to control all aspects of setting up the model, generating a mesh, controlling the simulation and generating and analysing output. The core computational routines are still implemented as C/C++ extension modules for maximal speed of execution.
An MPI-based parallel version is now available.
More recently, we expanded STEPS to enable full nanoscale modeling of neurons and synapses on distributed meshes: electrophysiology (membrane potential), molecular reactions and support to model the vesicle cycle.
For more information and downloads, please visit our SourceForge site.

Pycabnn

Pycabbn is an open-source software tool that is dedicated to generating an anatomical model, which serves as the basis of a full network model. In pycabnn, we implemented efficient algorithms for generating physiologically realistic cell positions and for determining connectivity based on extended geometrical structures such as axonal and dendritic morphology.

Pycabnn is efficient enough to carry out all the required tasks on a laptop computer within reasonable runtime, although it can also run in a parallel computing environment. Written purely in Python with limited external dependencies, pycabnn is easy to use and extend. Pycabnn can be downloaded here.

NeuroMaC

NeuroMaC (Neuronal Morphologies and Circuits) is a computational framework to generate large amounts of virtual neuronal morphologies simultaneously in a microcircuit. For more information and downloads, please visit our github site. There you can find the original NeuroMaC version described in Torben-Nielsen and De Schutter 2014, as well as a python 3 implementation with the model scripts from the paper. Documentation for version 0.1 is here.

We are working on a new software called NeuroDevSim replicating the NeuroMaC approach with better performance and many new features. Contact us if you are interested in early access to NeuroDevSim. 

Neurofitter

neurofitter-iconNeurofitter is software for parameter tuning of electrophysiological neuron models.
It automatically searches for sets of parameters of neuron models that best fit available experimental data, and therefore acts as an interface between neuron simulators, like Neuron or Genesis, and optimization algorithms, like Particle Swarm Optimization, Evolutionary Strategies, etc.
For more information and downloads, please visit our SourceForge site

NEMO

NEMO is a prototype implementation of a layer-oriented language for describing computational models of ion channels.
For more information please visit the NEMO page.

CSPRC

CSPRC is a MATLAB toolbox for estimating the infinitesimal phase response curve (PRC) by using Compressive Sensing (CS) algorithms.
For more information, please visit the code repository.