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Bayesian inference in neural dynamics: linking biophysical and computational approaches to neuroscience

Objectif

The team project is to identify the neural mechanisms that form our internal belief systems, and to learn how optimal strategies and decisions are generated in an unpredictable and ever changing world. To do so we will integrate complementary computation al neuroscience approaches. The first studies neurons and neural networks as biophysical entities, concentrating on mathematically describing their dynamical properties. We will integrate it with efforts to infer what is the computational role of neurons a nd neural networks. In particular we consider the Bayesian framework that has been very successful for describing human and animal behavior and learning in perceptual and motor domains. It explains how probable perceptual interpretations or expected outcom es of actions can be computed from ambiguous sensory data and prior knowledge about the likelihood of events. Despite the logical attractiveness of such probabilistic computations, their neural basis remains largely unexplained. We shall consider that comp utations at the single cell, network and brain level can be described as Bayesian inference and learning in progressively more complex statistical models. Thus, the most exquisite aspects of single neurons and synaptic dynamic might have a direct behaviora l correlate. The originality of our approach is to provide both a novel canonical model to describe the complex dynamics of neurons and synapses, and a new theory of neural coding, thus bridging the gap between levels of analyses traditionally considered a s distinct. We will use analytical tools and computer simulations, combining expertise in neuroscience, physics and machine learning. The ensuing models will be tested against the important body of pre-existing literature in experimental neuroscience. Our approach will further generate specific predictions that will be tested in collaboration with experimental laboratories in Europe and the US; industrial and medical implications shall be explored.

Mots‑clés

Thème(s)

Appel à propositions

FP6-2004-MOBILITY-8
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Régime de financement

EXT - Marie Curie actions-Grants for Excellent Teams

Coordinateur

ECOLE NORMALE SUPȒIEURE PARIS
Contribution de l’UE
Aucune donnée
Coût total
Aucune donnée