@Article{Supelec806,

author = {Jérémy Fix and Nicolas Rougier},

title = {DANA: Distributed numerical and adaptive modelling framework},

journal = {Network: Computation in Neural Systems},

year = {2012},

pages = {1-17},

url = {http://informahealthcare.com/doi/abs/10.3109/0954898X.2012.721573\'eprevSearch=allfield%253A%2528ROugier%2529\&searchHistoryKey=},

doi = {10.3109/0954898X.2012.721573},

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.).}

}