Feature binding through temporally correlated neural activity in a robot model of visual perception
An agent performing a task in an environment must be able to selectively attend to visual stimuli. This ability is of critical importance for adaptive behavior in (vision-based) biological and artificial agents. In this paper we present a connectionist model of how visual attention can serve an agent to perform its task. The model is embedded in a mobile robot. Visual stimuli are segregated by means of synchronization of spiking neurons. They then enter a selection process, the result of which determines what region of the visual field the robot will attend and consequently react to. Results from the behavior of the robot as well as the underlying neuronal dynamics are presented, and limitations as well as future extensions of the model are discussed.
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