Section: Software

Event Neural Assembly Simulation

Participants : Bruno Cessac [correspondent] , Sélim Kraria [Inria DREAM] , Olivier Marre [Institut de la vision, Paris] , Hassan Nasser, Thierry Viéville [Inria Mnemosyne Bordeaux] .

Enas is a library providing numerical tools for the simulation of neural networks and the analysis of spike trains either coming from neural simulators or from biological experiments.

It is designed mainly as

• An existing simulator plug-in (e.g. MVASpike or other simulators via the NeuralEnsemble meta-simulation platform),

• Additional modules for computations with neural unit assembly on standard platforms (e.g. Python, Matlab or the Scilab platform).

• Original modules for the analysis of spike train statistics intended to be used by the neuroscientists community.

Achievements include:

• Spike trains statistical analysis via Gibbs distributions. They are based on the estimation of a parametric Gibbs potential optimaly characterizing the statistics of empirical spike trains (by minimisation of the Kullback-Leibler divergence between the empirical measure and the Gibbs measure). From this, classical statistical indicators such as firing rate, correlations, higher order moments and statistical entropy are obtained. Also, the form of the Gibbs potential provides essential informations on the underlying neural network and its structure. This method does not only allows us to estimate the spikes statistics but also to compare different models, thus answering such questions about the neural code as: are correlations (or time synchrony or a given set of spike patterns,. . . ) significant with respect to rate coding?

• Spiking network programing for exact event's sequence restitution;

• Discrete neural field parameters algorithmic adjustments and time-constrained event-based network simulation reconciling clock and event based simulation methods.

Compared to existing libraries Enas offers new computational methods taking into account time constraints in neural networks (such as memory effects), based on theoretical methods rooted in statistical physics and applied mathematics. The algorithms used are based on linear programming, nonlinear parameter estimations, statistical methods. The C/C++ code has been organized as “bean java” to ease its use by programmers non specialized in advanced object programming. As a consequence the code is distributed in the form of an include source for the lightest and the most universal integration into users codes. The standard algorithms are based on the best free libraries in the domain such as gsl http://www.gnu.org/software/gsl .

Event neural assembly simulation is developed in gForge. It is under CeCILL C licence

APP logiciel Enas: IDDN.FR.OO1.360008.000.S.P.2009.000.10600.

Its development as a friendly software designed for the neuroscience community is our present purpose. This is done with the support of an ADT Inria.

Website: http://enas.gforge.inria.fr/