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Task: Modeling of neural systems
Install: false
Description: 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.
Recommends: python3-brian
Suggests: python3-pynn
; Added by blends-inject 0.0.5. [Please note here if modified manually]
Suggests: moose
Homepage: http://moose.sourceforge.net
Language: C++, Python
License: LGPL
Pkg-Description: multiscale simulation environment for neuroscience
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.
; Added by blends-inject 0.0.5. [Please note here if modified manually]
Suggests: pcsim
Homepage: http://www.lsm.tugraz.at/pcsim/
Language: C, Python
WNPP: 605414
License: GPL-3+
Pkg-Description: simulator of heterogeneous networks of neurons and synapses
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.
Published-Authors: Pecevski D, Natschläger T and Schuch K
Published-DOI: 10.3389/neuro.11.011.2009
Published-In: Front. Neuroinform. 3:11
Published-Title: PCSIM: a parallel simulation environment for neural circuits fully integrated with Python
Published-Year: 2009
Remark: last release is more than 1 year ago, but there is development in newbuild branch
; Added by blends-inject 0.0.7. [Please note here if modified manually]
Suggests: nest
Homepage: http://www.nest-initiative.org/index.php/Software:About_NEST
Responsible: Yury V. Zaytsev <yury@shurup.com>
License: non-FOSS
Vcs-Browser: http://git.zaytsev.net/?p=nest-packaging.git;a=summary
Vcs-Git: git://git.zaytsev.net/nest-packaging.git
Pkg-URL: http://git.zaytsev.net/?p=nest-packaging.git;a=summary
Pkg-Description: A simulator for networks of spiking neurons
NEST is a simulation system for large networks of biologically realistic
point-neurons and neurons with a small number of electrical compartments.
Published-Authors: Gewaltig M-O and Diesmann M
Published-In: Scholarpedia 2(4):1430
Published-Title: NEST (Neural Simulation Tool)
Published-Year: 2007
Registration: http://www.nest-initiative.org/download/Software:Download.php
Remark: Mentioned packaging is not providing NEST itself but rather only packaging materials so you could build package yourself.
; Added by blends-inject 0.0.6. [Please note here if modified manually]
Suggests: invt
Homepage: http://ilab.usc.edu/toolkit/
Language: C++ + Perl, Tcl, Matlab
License: GPL-2+
Pkg-Description: iLab Neuromorphic Vision C++ Toolkit
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.
Registration: http://ilab.usc.edu/cgi-bin/yabb/YaBB.pl?board=;action=register
; Added by blends-inject 0.0.7. [now official package - information obtained from package pool]
Suggests: xppaut
Recommends: cnrun
Recommends: neuron
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