Debian Science Project
Modeling of neural systems
Debian Science packages for modeling of neural systems

This metapackage will install Debian packages which might be useful for scientists interested in modeling of real neural systems at different levels (from single neuron to complex networks).

The selection of packages is targeting the application of simulation techniques. Methods developers are referred to the science-statistics, science-imageanalysis, science-numericalcomputation, med-imaging, and med-imaging-dev metapackages for a variety of additional software that might be useful for neuroscience research.


For a better overview of the project's availability as a Debian package, each head row has a color code according to this scheme:

If you discover a project which looks like a good candidate for Debian Science to you, or if you have prepared an unofficial Debian package, please do not hesitate to send a description of that project to the Debian Science mailing list

Links to other tasks

Debian Science Modeling of neural systems packages

Official Debian packages with high relevance

NeuroML-capable neuronal network simulator
Maintainer: Andrei Zavada
Versions of package cnrun
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License: DFSG free

CNrun is a neuronal network model simulator, similar in purpose to NEURON except that individual neurons are not compartmentalised. It can read NeuroML files (e.g., as generated by neuroConstruct); provides a Hodgkin-Huxley neuron (plus some varieties), a Rall and Alpha-Beta synapses, Poisson, Van der Pol, Colpitts oscillators and regular pulse generator; external inputs and logging state variables. Uses a 6-5 Runge-Kutta integration method. Basic scripting and (if run interactively) context-aware autocompletion.

Please cite: A. Zavada, C.L. Buckley, D. Martinez, J-P. Rospars and T. Nowotny: Competition-based model of pheromone component ratio detection in the moth. (PubMed,eprint) PLoS ONE 6(2):e16308 (2011)
Simulation environment for computational models of neurons
Versions of package neuron
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License: DFSG free

NEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties.

NEURON offers

  • "natural syntax", which allows one to specify model properties in familiar idioms
  • efficient and painless spatial and temporal discretization
  • several different, user-selectable numerical integration methods
  • convenient user interface (interpreters + GUI)
  • user-extendable library of biophysical mechanisms
Simulator für »spiking neural networks« (zeitabhängige neuronale Netze)
Versions of package python3-brian
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License: DFSG free

Brian ist ein zeitgesteuerter Simulator für »spiking neural networks«. Er wurde mit Schwerpunkt auf Flexibilität und Erweiterbarkeit ausgelegt, für die schnelle Entwicklung und Verfeinerung von neuronalen Modellen. Neuronale Modelle werden durch Sätze von durch den Benutzer vorgegebenen Differentialgleichungen, Schwellwert- und Rücksetzbedingungen (als Strings ausgedrückt) formuliert. Der Schwerpunkt liegt dabei vor allem in Netzwerken von »single compartment neuron«-Modellen (z.B. leaky integrate-and-fire oder Neuronen vom Hodgkin-Huxley-Typ). Die Eigenschaften umfassen:

  • ein System zur Angabe von Mengen mit physikalischen Dimensionen
  • exakte numerische Integration für lineare Differentialgleichungen
  • Euler-, Runge-Kutta- und exponentielle Euler-Integration für nichtlineare Differentialgleichungen
  • synaptische Verbindungen mit Verzögerungen
  • kurzfristige und langfristige Plastizität (vom spike-timing abhängige Plastizität)
  • eine Bibliothek von Standard-Modellkomponenten, einschließlich »integrate-and-fire«-Gleichungen, Synapsen und Ionenströmen
  • einen Werkzeugsatz für die automatische Anpassung von »spiking neuron«-Modellen an elektrophysiologische Messdaten
Please cite: D.F. Goodman and R. Brette: Brian: A Simulator for Spiking Neural Networks in Python. (PubMed,eprint) Frontiers in Neuroinformatics 2(5) (2008)

Official Debian packages with lower relevance

Phase Plane Plus Auto: Löst viele verschiedene Arten von Gleichungen
Versions of package xppaut
Popcon: 3 users (2 upd.)*
Versions and Archs
License: DFSG free

XPPAUT ist ein Werkzeug zum Lösen von

  • Differentialgleichungen,
  • Differenzengleichungen,
  • Delaygleichungen,
  • Funktionalgleichungen,
  • Randwertproblemen und
  • stochastischen Gleichungen.

Das Programm vereint eine Vielzahl praktischer Algorithmen und ist sehr leicht zu portieren. Die Oberfläche ist vollständig in Xlib programmiert, wodurch sich das etwas eigenwillig und primitiv anmutende Aussehen erklärt.

Screenshots of package xppaut

Packaging has started and developers might try the packaging code in VCS

A simulator for networks of spiking neurons
Responsible: Yury V. Zaytsev
License: non-FOSS

NEST is a simulation system for large networks of biologically realistic point-neurons and neurons with a small number of electrical compartments.

Please register by following this link if you are using nest.
Please cite: Gewaltig M-O and Diesmann M: NEST (Neural Simulation Tool) (2007)
Remark of Debian Science team: Mentioned packaging is not providing NEST itself but rather only packaging materials so you could build package yourself.

No known packages available but some record of interest (WNPP bug)

Pcsim - wnpp
simulator of heterogeneous networks of neurons and synapses
License: GPL-3+
Debian package not available
Language: C, Python

PCSIM is a tool for simulating heterogeneous networks composed of different model neurons and synapses. This simulator is written in C++ with a primary interface to the programming language Python. It is intended to simulate networks containing up to millions of neurons and on the order of billions of synapses. This is achieved by distributing the network over different nodes of a computing cluster by using MPI.

Please cite: Pecevski D, Natschläger T and Schuch K: PCSIM: a parallel simulation environment for neural circuits fully integrated with Python. (2009)
Remark of Debian Science team: last release is more than 1 year ago, but there is development in newbuild branch

No known packages available

iLab Neuromorphic Vision C++ Toolkit
License: GPL-2+
Debian package not available
Language: C++ + Perl, Tcl, Matlab

The iLab Neuromorphic Vision C++ Toolkit (iNVT, pronounced ``invent'') is a comprehensive set of C++ classes for the development of neuromorphic models of vision. Neuromorphic models are computational neuroscience algorithms whose architecture and function is closely inspired from biological brains. The iLab Neuromorphic Vision C++ Toolkit comprises not only base classes for images, neurons, and brain areas, but also fully-developed models such as our model of bottom-up visual attention and of Bayesian surprise.

Features at a glance:

  • Low-level neural network simulation classes.
  • High-level neuromorphic classes.
  • Neuromorphic models of visual attention.
  • Hardware interfacing
  • Parallel processing classes for the simulation of complex models.
  • Neuromorphic modeling environment.
Please register by following this link if you are using invt.
multiscale simulation environment for neuroscience
License: LGPL
Debian package not available
Language: C++, Python

MOOSE is the Multiscale Object-Oriented Simulation Environment. It is the base and numerical core for large, detailed simulations including Computational Neuroscience and Systems Biology. MOOSE spans the range from single molecules to sub-cellular networks, from single cells to neuronal networks, and to still larger systems. It is backwards-compatible with GENESIS, and forward compatible with Python and XML-based model definition standards like SBML and MorphML. MOOSE is coordinating with the GENESIS-3 project towards the goals of developing educational resources for modeling.

*Popularitycontest results: number of people who use this package regularly (number of people who upgraded this package recently) out of 207262