Skip to Main content Skip to Navigation
Journal articles

DANA: Distributed (asynchronous) Numerical and Adaptive modelling framework

Nicolas Rougier 1 Jérémy Fix 2
1 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
Abstract : DANA is a python framework (http://dana.loria.fr) whose computational paradigm is grounded on the notion of a unit that is essentially a set of time dependent values varying under the influence of other units via adaptive weighted connections. The evolution of a unit's value are defined by a set of differential equations expressed in standard mathematical notation which greatly ease their definition. The units are organized into groups that form a model. Each unit can be connected to any other unit (including itself) using a weighted connection. The DANA framework offers a set of core objects needed to design and run such models. The modeler only has to define the equations of a unit as well as the equations governing the training of the connections. The simulation is completely transparent to the modeler and is handled by DANA. This allows DANA to be used for a wide range of numerical and distributed models as long as they fit the proposed framework (e.g. cellular automata, reaction-diffusion system, decentralized neural networks, recurrent neural networks, kernel-based image processing, etc.).
Document type :
Journal articles
Complete list of metadatas

Cited literature [23 references]  Display  Hide  Download

https://hal.inria.fr/hal-00718780
Contributor : Nicolas P. Rougier <>
Submitted on : Wednesday, July 18, 2012 - 10:53:27 AM
Last modification on : Tuesday, April 21, 2020 - 10:58:29 AM
Document(s) archivé(s) le : Friday, December 16, 2016 - 12:52:45 AM

File

revision.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Nicolas Rougier, Jérémy Fix. DANA: Distributed (asynchronous) Numerical and Adaptive modelling framework. Network: Computation in Neural Systems, Taylor & Francis, 2012, 23 (4), pp.237-253. ⟨10.3109/0954898X.2012.721573⟩. ⟨hal-00718780⟩

Share

Metrics

Record views

837

Files downloads

563