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

Nicolas P. Rougier (1, 2, 3)

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique
2 IMN - Institut des Maladies Neurodégénératives [Bordeaux]
3 Mnemosyne - Mnemonic Synergy

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.

Mots clés: neuroscience; cognition; behavior

Domaines: Neurosciences

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⟩