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Modeling attentional control in the brain

Objectif

Better understanding of attention mechanisms in the brain is feasible through analysing of experimental data from the viewpoint of control theory and computational neural models.

The objectives are:
a) construct an observer framework for attention and from this framework
b) develop its mathematical structure in a neural network environment;
c) simulate present single cell data in monkeys and lower animals to justify the existence of a control module for directing attention;
d) analyse psychophysical data in humans for directed search, attention movement time, pre-cued shift benefits and other attention paradigms;
e) analyse brain imaging human and single cell monkey data, especially on frontal involvement, in movement of attention to determine the involvement of a possible state estimator;
f) extend this framework to briefly relate to saccade control systems;
g) extend the framework to include reward learning (of ACE/ASE/doparnine form).

Champ scientifique (EuroSciVoc)

Programme(s)

Thème(s)

Données non disponibles

Appel à propositions

IHP-MCIF-99-1
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Régime de financement

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Coordinateur

KING'S COLLEGE LONDON, DEPARTMENT OF MATHEMATICS, CENTRE FOR NEURAL NETWORKS
Contribution de l’UE
Aucune donnée
Adresse


Royaume-Uni
Coût total
Aucune donnée