Neural mechanisms for learning of attention control and pattern categorization as basis for robot cognition

We present mechanisms for attention control and pattern categorization as the basis for robot cognition. For attention, we gather information from attentional feature maps extracted from sensory data: constructing salience maps to decide where to foveate. For identification multi-feature maps are used as input to an associative memory, allowing the system to classify a pattern representing a region of interest. As a practical result, our robotic platforms are able to select regions of interest and perform shifts of attention focusing on the selected regions, and to construct and maintain attentional maps of the environment in an efficient manner.