Debian Science Project
Summary
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.

Description

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

Cnrun
NeuroML-capable neuronal network simulator
Maintainer: Andrei Zavada
Versions of package cnrun
ReleaseVersionArchitectures
jessie1.1.14-1amd64,arm64,armel,armhf,i386,mips,mipsel,powerpc,ppc64el,s390x
Popcon: 1 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

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)
Python-brian
simulator for spiking neural networks
Versions of package python-brian
ReleaseVersionArchitectures
jessie1.4.1-2all
stretch1.4.3-1all
buster1.4.3-1all
sid1.4.3-1all
wheezy1.3.1-1all
Debtags of package python-brian:
fieldbiology, mathematics
roleprogram
sciencemodelling
scopeutility
usesimulating
Popcon: 19 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Brian is a clock-driven simulator for spiking neural networks. It is designed with an emphasis on flexibility and extensibility, for rapid development and refinement of neural models. Neuron models are specified by sets of user-specified differential equations, threshold conditions and reset conditions (given as strings). The focus is primarily on networks of single compartment neuron models (e.g. leaky integrate-and-fire or Hodgkin-Huxley type neurons). Features include:

  • a system for specifying quantities with physical dimensions
  • exact numerical integration for linear differential equations
  • Euler, Runge-Kutta and exponential Euler integration for nonlinear differential equations
  • synaptic connections with delays
  • short-term and long-term plasticity (spike-timing dependent plasticity)
  • a library of standard model components, including integrate-and-fire equations, synapses and ionic currents
  • a toolbox for automatically fitting spiking neuron models to electrophysiological recordings
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)
Screenshots of package python-brian

Official Debian packages with lower relevance

Python-pynn
simulator-independent specification of neuronal network models
Versions of package python-pynn
ReleaseVersionArchitectures
wheezy0.7.4-1all
jessie0.7.4-1all
sid0.7.4-1.1all
experimental0.7.5-1.1all
upstream0.9.1
Popcon: 9 users (0 upd.)*
Newer upstream!
License: DFSG free
Git

PyNN allows for coding a model once and run it without modification on any simulator that PyNN supports (currently NEURON, NEST, PCSIM and Brian). PyNN translates standard cell-model names and parameter names into simulator-specific names.

Xppaut
Phase Plane Plus Auto: Solves many kinds of equations
Versions of package xppaut
ReleaseVersionArchitectures
wheezy6.11b+1.dfsg-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
jessie6.11b+1.dfsg-1amd64,arm64,armel,armhf,i386,mips,mipsel,powerpc,ppc64el,s390x
stretch6.11b+1.dfsg-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
buster6.11b+1.dfsg-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid6.11b+1.dfsg-1amd64,arm64,armel,armhf,hurd-i386,i386,kfreebsd-amd64,kfreebsd-i386,mips,mips64el,mipsel,powerpc,ppc64el,s390x
upstream7.0
Popcon: 5 users (1 upd.)*
Newer upstream!
License: DFSG free
Git

XPPAUT is a tool for solving

  • differential equations,
  • difference equations,
  • delay equations,
  • functional equations,
  • boundary value problems, and
  • stochastic equations.

The code brings together a number of useful algorithms and is extremely portable. All the graphics and interface are written completely in Xlib which explains the somewhat idiosyncratic and primitive widgets interface.

Screenshots of package xppaut

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

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

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

Invt
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.
Moose
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 200581