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Distributed, Asynchronous, Numerical and Adaptive computing: from neurons to behavior

Nicolas P. Rougier 1, 2, 3 
3 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
Abstract : Computational models of the brain exist at several different levels of description, from the very precise modelling of a unique spiking neuron, taking into account ion channels and/or dendrites spatial geometry, up to the modelling of very large assemblies of neuron that express complex dynamic interactions. I'm interested in a mesoscopic approach of the brain where the 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 the units' value and weights are simply defined by a set of differential equations. This is a strongly constrained framework that has been designed such as to avoid any modeling artifact like a central supervisor or a homunculus. If some properties are to emerge from our models, we want to make sure they are properties of the model as opposed to properties of the software that run the simulation.
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Contributor : Nicolas P. Rougier Connect in order to contact the contributor
Submitted on : Friday, October 9, 2015 - 4:43:10 PM
Last modification on : Saturday, June 25, 2022 - 7:41:15 PM



Distributed under a Creative Commons Attribution 4.0 International License


  • HAL Id : hal-01213784, version 1



Nicolas P. Rougier. Distributed, Asynchronous, Numerical and Adaptive computing: from neurons to behavior. BioComp workshop, GDR BioComp, Oct 2015, Saint Paul de Vence, France. ⟨hal-01213784⟩



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