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
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Debian Science Modeling of neural systems packages
Official Debian packages with high relevance
cnrun
??? missing short description for package cnrun :-(
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Versions of package cnrun |
Release | Version | Architectures |
jessie | 1.1.14-1 | amd64,armel,armhf,i386 |
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License: DFSG free
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neuron
Simulation environment for computational models of neurons
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Versions of package neuron |
Release | Version | Architectures |
bullseye | 7.6.3-1 | amd64,arm64,i386,ppc64el |
buster | 7.6.3-1 | amd64,arm64,i386 |
bookworm | 8.2.2-4 | amd64,arm64,armel,armhf,i386,ppc64el,s390x |
sid | 8.2.2-7 | amd64,arm64,armel,armhf,i386,ppc64el,riscv64,s390x |
trixie | 8.2.2-7 | amd64,arm64,armel,armhf,i386,ppc64el,riscv64,s390x |
upstream | 9.0.dev |
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License: DFSG free
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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
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python3-brian
simulator for spiking neural networks
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Versions of package python3-brian |
Release | Version | Architectures |
trixie | 2.7.1+ds-1 | all |
sid | 2.7.1+ds-1 | all |
bullseye | 2.4.2-6 | all |
bookworm | 2.5.1-3 | all |
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License: DFSG free
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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
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Official Debian packages with lower relevance
python3-pynn
simulator-independent specification of neuronal network models
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Versions of package python3-pynn |
Release | Version | Architectures |
bullseye | 0.9.6-1 | all |
bookworm | 0.10.1-2 | all |
sid | 0.10.1-3 | all |
upstream | 0.12.3 |
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License: DFSG free
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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.
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xppaut
Phase Plane Plus Auto: Solves many kinds of equations
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Versions of package xppaut |
Release | Version | Architectures |
trixie | 6.11b+1.dfsg-1.1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
sid | 6.11b+1.dfsg-1.1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 6.11b+1.dfsg-1.1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bullseye | 6.11b+1.dfsg-1.1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 6.11b+1.dfsg-1 | amd64,arm64,armhf,i386 |
stretch | 6.11b+1.dfsg-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
jessie | 6.11b+1.dfsg-1 | amd64,armel,armhf,i386 |
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License: DFSG free
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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.
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Packaging has started and developers might try the packaging code in VCS
nest
A simulator for networks of spiking neurons
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License: non-FOSS
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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)
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No known packages available but some record of interest (WNPP bug)
simulator of heterogeneous networks of neurons and synapses
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License: GPL-3+
Debian package not available
Language: C, Python
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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.
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No known packages available
invt
iLab Neuromorphic Vision C++ Toolkit
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License: GPL-2+
Debian package not available
Language: C++ + Perl, Tcl, Matlab
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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.
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moose
multiscale simulation environment for neuroscience
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License: LGPL
Debian package not available
Language: C++, Python
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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.
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