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PyNN (pronounced 'pine') is a simulator-independent language for building neuronal network models.In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON, NEST and Brian) and on a number of neuromorphic hardware systems.Home page: http://neuralensemble.org/PyNN/Documentation: http://neuralensemble.org/docs/PyNN/Mailing list: https://groups.google.com/forum/?fromgroups#!forum/neuralensembleBug reports: https://github.com/NeuralEnsemble/PyNN/issuesThis is version 0.9.6. of PyNN

Python code to simulate formose reaction in osmotically coupled droplets, and associated data, related to the article "Emergent fitness and ecological dynamics of compartmentalised autocatalytic chemical reactions" by Heng Lu, Alex Blokhuis, Rebecca Turk-MacLeod, Jayaprakash Karuppusamy, Andrea Franconi, Gabrielle Woronoff, Cyrille Jeancolas, Afshin Abrishamkar, Estelle Loire, Fabien Ferrage, Philippe Pelupessy, Ludovic Jullien, Eörs Szathmary, Philippe Nghe and Andrew. D. Griffiths This software is distributed under the Creative Commons International 4.0 License.

Givaro main features are implementations of the basic arithmetic of many mathematical entities: Primes fields, Extensions Fields, Finite Fields, Finite Rings, Polynomials, Algebraic numbers, Arbitrary precision integers and rationals (C++ wrappers over gmp), fixed precision integers. It also provides data-structures and templated classes for the manipulation of basic algebraic objects, such as vectors, matrices, univariate polynomials (and therefore recursive multivariate). It contains different program modules and is fully compatible with the FFLAS-FFPACK and LinBox linear algebra libraries.

The code of this toolbox is distributed under the terms of the GNU Public License version 3 (http://www.gnu.org/licenses/gpl.txt).The data files are distributed under specific licenses as stated in the related .txt files in the directory 'Data/'.; A toolbox to reproduce audio inpainting experiments from the paper Amir Adler, Valentin Emiya, Maria Jafari, Michael Elad, Rémi Gribonval, et al.. Audio Inpainting. IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2012, 20 (3), pp.922 - 932. ⟨10.1109/TASL.2011.2168211⟩. ⟨inria-00577079⟩

A SystemC simulator able to simulate embedded applications described using the AMALTHEA application model on top of crossbar-based manycore architectures in a cycle accurate way. This simulator has been developed by the LIRMM laboratory of Montpellier.Webpage: https://github.com/DreamCloud-Project/McSim-Cycle-accurate-Xbar

LinBox is a C++ template library of routines for solution of linear algebra problems including linear system solution, rank, determinant, minimal polynomial, characteristic polynomial, and Smith normal form. Algorithms are provided for matrices with integer entries or entries in a finite field. A number of matrix storage types is provided, especially for blackbox representation of sparse or structured matrix classes.A few algorithms for rational matrices are available. LinBox also uses underlying data structures and algorithms for integer, rational, polynomial, finite fields and rings, as well as dense and sparse matrix formats coming from the Givaro (https://casys.gricad-pages.univ-grenoble-alpes.fr/givaro) and FFLAS-FFPACK (http://linbox-team.github.io/fflas-ffpack) libraries.

Année de la première version : 2014Interface utilisateur : interface WebMode de diffusion : service en ligne; Common european tree species sampled in Gascony hills in South-West of France.

Python package that implements Tractograms As Linear Operators in Neuroimaging (TALON).

Neo is a Python package for working with electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, Igor Pro, and support for writing to a subset of these formats plus non-proprietary formats including Kwik and HDF5. The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data, by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to represention of data, with no functions for data analysis or visualization. Neo is used by a number of other software tools, including SpykeViewer (data analysis and visualization), Elephant (data analysis), the G-node suite (databasing), PyNN (simulations), tridesclous (spike sorting) and ephyviewer (data visualization). OpenElectrophy (data analysis and visualization) used an older version of Neo. Neo implements a hierarchical data model well adapted to intracellular and extracellular electrophysiology and EEG data with support for multi-electrodes (for example tetrodes). Neo’s data objects build on the quantities package, which in turn builds on NumPy by adding support for physical dimensions. Thus Neo objects behave just like normal NumPy arrays, but with additional metadata, checks for dimensional consistency and automatic unit conversion. This is version 0.9.0 of Neo. Release notes are available at https://neo.readthedocs.io/en/stable/releases/0.9.0.html

CBC4CS is a Matlab package to reproduce the convex blind calibration experiments of the following papers:[1] Cagdas Bilen, Gilles Puy, Rémi Gribonval, Laurent Daudet, "Convex Optimization Approaches for Blind Sensor Calibration using Sparsity", IEEE Transationc on Signal Processing 2014 .[2] ,Cagdas Bilen, Gilles Puy, Rémi Gribonval, Laurent Daudet, "Blind Phase Calibration in Sparse Recovery", EUSIPCO 2013.