Debian Science Project
Summary
Modeling of neural systems
pacchetti per Debian Science per modellazione di sistemi neurali

Questo metapacchetto installa i pacchetti Debian che possono essere utili per gli scienziati interessati alla modellazione di sistemi neurali reali a vari livelli (dal singolo neurone fino a reti complesse).

La selezione di pacchetti è indirizzata all'applicazione di tecniche di simulazione. Gli sviluppatori di metodi devono far riferimento ai metapacchetti science-statistics, science-imageanalysis, science-numericalcomputation, med-imaging e med-imaging-dev per una varietà di software aggiuntivo che può essere utile nella ricerca in neuroscienza.

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
simulatore di reti neurali con gestione di NeuroML
Maintainer: Andrei Zavada
Versions of package cnrun
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jessie1.1.14-1amd64,armel,armhf,i386
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

CNrun è un simulatore di modelli di reti neurali, simile in scopo a NEURON tranne per il fatto che i singoli neuroni non sono compartimentalizzati. Può leggere file NeuroML (ad esempio quelli generati da neuroConstruct); fornisce un neurone di Hodgkin-Huxley (più alcune varianti), sinapsi Rall e Alpha-Beta, oscillatori di Poisson, Van der Pol e Colpitts e un generatore di impulsi regolari, e input esterni e variabili di stato con log. Usa un metodo di integrazione Runge-Kutta 6-5. Ha funzionalità di scripting di base e (se eseguito interattivamente) completamento automatico sensibile al contesto.

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)
Neuron
ambiente di simulazione per modelli computazionali di neuroni
Versions of package neuron
ReleaseVersionArchitectures
sid7.6.3-1amd64,arm64,i386,ppc64el
buster7.6.3-1amd64,arm64,i386,ppc64el
bullseye7.6.3-1amd64,arm64,i386,ppc64el
upstream7.7
Popcon: 10 users (3 upd.)*
Newer upstream!
License: DFSG free
Git

NEURON è un ambiente di simulazione per modellare neuroni individuali e reti di neuroni. Fornisce strumenti comodi per costruire, gestire e usare modelli in un modo che è numericamente solido e computazionalmente efficiente. È particolarmente bene adatto ai problemi che sono strettamente collegati a dati sperimentali, specialmente quelli che coinvolgono celle con proprietà biofisiche e anatomiche complesse.

NEURON offre

  • "sintassi naturale", che permette di specificare proprietà del modello in idiomi familiari;
  • discretizzazione efficiente e indolore, spaziale e temporale;
  • svariati metodi numerici di integrazione differenti, selezionabili dall'utente;
  • interfaccia utente comoda (interpreti + GUI);
  • libreria di meccanismi biofisici ampliabile dall'utente.

Official Debian packages with lower relevance

Xppaut
Phase Plane Plus Auto: risolve molti tipi di equazioni
Versions of package xppaut
ReleaseVersionArchitectures
buster6.11b+1.dfsg-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bullseye6.11b+1.dfsg-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
stretch6.11b+1.dfsg-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
wheezy6.11b+1.dfsg-1amd64,armel,armhf,i386,ia64,kfreebsd-amd64,kfreebsd-i386,mips,mipsel,powerpc,s390,s390x,sparc
jessie6.11b+1.dfsg-1amd64,armel,armhf,i386
sid6.11b+1.dfsg-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
upstream7.0
Popcon: 1 users (0 upd.)*
Newer upstream!
License: DFSG free
Git

XPPAUT è uno strumento per risolvere:

  • equazioni differenziali,
  • equazioni alle differenze,
  • equazioni differenziali con ritardo,
  • equazioni funzionali,
  • problemi al contorno ed
  • equazioni stocastiche.

Il codice raggruppa insieme alcuni utili algoritmi ed è estremamente portabile. Tutte le grafiche e l'interfaccia sono scritte completamente in Xlib, il che spiega l'interfaccia in qualche modo idiosincratica e con widget primitivi.

Screenshots of package xppaut

Debian packages in experimental

Python3-brian
simulator for spiking neural networks
Versions of package python3-brian
ReleaseVersionArchitectures
experimental2.2.2.1-1all
Popcon: 0 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)

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 203233