International Journal of Computer Vision
New results on binary space partitions in the plane
Computational Geometry: Theory and Applications
Sparse coding in the primate cortex
The handbook of brain theory and neural networks
Multidimensional binary search trees used for associative searching
Communications of the ACM
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Image Segmentation by Networks of Spiking Neurons
Neural Computation
An automated vision based on-line novel percept detection method for a mobile robot
Robotics and Autonomous Systems
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An issue of critical importance to both robots and biological creatures is the efficient use of the limited resources available for survival. One strategy employed by higher animals is the focusing of attention onto the few items, e.g., percepts, salient to reaching the current goals, and ignoring distracting input. In these animals, the pre-frontal cortex working memory plays a significant role in the focus of attention. A recently developed Working Memory Toolkit (WMtk) is based on a computational neuroscience model of working memory. We apply this model/toolkit to two perceptual learning problems from robot vision related to navigation and landmark detection. Our system is described along with two perceptual learning experiments. The results of these experiments are given and show impressive performance both in terms of accuracy and speed of learning. To our knowledge, this is the first such application of a computational neuroscience model of working memory to a robot.