Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A probabilistic model of overt visual attention for cognitive robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient neural models for visual attention
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Dynamic pursuit with a bio-inspired neural model
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Reinforcement learning in mirrorbot
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
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Although biomimetic autonomous robotics relies on the massively parallel architecture of the brain, the key issue is to temporally organize behaviour. The distributed representation of the sensory information has to be coherently processed to generate relevant actions. In the visual domain, we propose here a model of visual exploration of a scene by the means of localized computations in neural populations whose architecture allows the emergence of a coherent behaviour of sequential scanning of salient stimuli. It has been implemented on a real robotic platform exploring a moving and noisy scene including several identical targets.