Debian Science Project
Summary
Modeling of neural systems
Debian Science - pakker til modellering af neurale systemer

Denne metapakke vil installere Debianpakker, som kan være nyttige for videnskabsmænd interesseret i modellering af neurale netværkssystemer på forskellige niveauer (fra en enkel neuron til komplekse netværk).

Udvalget af pakker er målrettet anvendelsen af simuleringsteknikker. Metoder som udviklere refereres til er metapakkerne science-statistics, science-imageanalysis, science-numericalcomputation, med-imaging, og med-imaging-dev for en række yderligere programmer, som kan være nyttige indenfor neurovidenskabelig forskning.

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-funktionsdygtigt neuraonal netværksimulator
Maintainer: Andrei Zavada
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CNrun er en neuronal netværksmodelsimulator, der i formål svarer til NEURON med undtagelse af at individuelle neuroner ikke er opdelt. Simulatoren kan læse NeuroML-filer (f.eks. som oprettet af neuroConstruct); tilbyder en Hodgkin-Huxley-neuron (plus nogle varianter), en Rall og Alpha-Beta synaps, Poisson, Van der Pol, Colpitts-oscillatorer og regelmæssig pulsgenerator, eksterne indgange og logningstilstandsvariable. Bruger en 6-5 Runge-Kutta integrationsmetode. Grundlæggende skripting og (hvis kørt interaktivt) kontekst-bevidst automatisk fuldførelse.

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
Simuleringsmiljø for beregningsmiljøer med neuroner
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NEURON er et simuleringsmiljø til modellering af individuelle neuroner og netværk af neuroner. Det tilbyder værktøjer til nem bygning, håndtering og brug af modeller på en måde, der er numerisk fornuftig og beregningsmæssig effektiv. Det er specielt velegnet til problemer, der er tæt forbundet med eksperimentelle data, specielt dem der involverer celler med komplekse anatomiske og biofysiske egenskaber.

NEURON tilbyder

  • »naturlig syntaks«, der gør det muligt at specificere modelegenskaber i kendte idiomer
  • effektiv og smerteløs delvis og midlertidig diskretisering
  • flere forskellige, brugervalgte numeriske integreringsmetoder
  • nem brugerflade (fortolkere + grafisk brugerflade)
  • bibliotek med biofysiske mekanismer der kan udvides af brugeren
python3-brian
Simulator for spikingneurale netværk
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Brian er en ur-drevet simulator for spikingneurale netværk. Det er designet med vægt på fleksibilitet og udvidelse, for hurtig udvikling og raffinement af neurale modeller. Neuronmodeller er specificeret af sæt af brugerangivne differentialligninger, tærskelbetingelser og nulstillingsbetingelser (givet som strenge). Fokus er primært på netværk med »single compartment neuron«-modeller (f.eks. leaky integrate-and-fire- eller Hodgkin-Huxley-neuroner). Inkluderede funktioner:

  • et system for angivelse af kvantiteter med fysiske dimensioner
  • præcis numerisk integration for lineære differentialligninger
  • Euler, Runge-Kutta og eksponentiel Eulerintegration for ikkelineære differentialligninger
  • synaptiske forbindelser med forsinkelser
  • korttids og langtids plasticitet (spike-timing-afhængig plasticitet)
  • et bibliotek med modelkomponenter, inklusive integrate-and-fire ligninger, synapser og ionic-strømme
  • en værktøjskasse for automatisk tilpasning af spiking neuron-modeller til elektrofysiologiske optagelser
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

python3-pynn
Simulatoruafhænging specifikation af neuronale netværksmodeller
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PyNN gør det muligt at kode en model på en gang og afvikle den uden ændring på enhver simulator, som PyNN understøtter (i øjeblikket NEURON, NEST, PCSIM og Brian). PyNN oversætter cellemodelnavne og parameternavne til simulatorspecifikke navne.

xppaut
Phase Plane Plus Auto - løser mange slags ligninger
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XPPAUT er et værktøj til løsning af

  • Differentialligninger
  • Differensligninger
  • Forsinkelsesligninger
  • Funktionelle ligninger,
  • Grænseværdiproblemer, og
  • Stokastiske ligninger

Koden samler et antal nyttige algoritmer og er ekstrem flytbar. Al grafikken og grænsefladen er skrevet helt i Xlib, hvilket forklarer den noget idiosynkratiske og primitive kontrolgrænseflade.

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 237229