Eye movements present during acquisition of a retinal image with optical coherence tomography (OCT) introduce
motion artifacts into the image, complicating analysis and registration. This effect is especially pronounced in highresolution
data sets acquired with adaptive optics (AO)-OCT instruments. Several retinal tracking systems have been
introduced to correct retinal motion during data acquisition. We present a method for correcting motion artifacts in AOOCT
volume data after acquisition using simultaneously captured adaptive optics-scanning laser ophthalmoscope (AOSLO)
images. We extract transverse eye motion data from the AO-SLO images, assign a motion adjustment vector to
each AO-OCT A-scan, and re-sample from the scattered data back onto a regular grid. The corrected volume data
improve the accuracy of quantitative analyses of microscopic structures.
A significant challenge in robotics is providing a robot with the ability to sense its environment and then autonomously
move while accommodating obstacles. The DARPA Grand Challenge, one of the most visible examples, set the goal of
driving a vehicle autonomously for over a hundred miles avoiding obstacles along a predetermined path. Map-Seeking
Circuits have shown their biomimetic capability in both vision and inverse kinematics and here we demonstrate their
potential usefulness for intelligent exploration of unknown terrain using a multi-articulated linear robot. A robot that
could handle any degree of terrain complexity would be useful for exploring inaccessible crowded spaces such as rubble
piles in emergency situations, patrolling/intelligence gathering in tough terrain, tunnel exploration, and possibly even
planetary exploration. Here we simulate autonomous exploratory navigation by an interaction of terrain discovery using
the multi-articulated linear robot to build a local terrain map and exploitation of that growing terrain map to solve the
propulsion problem of the robot.
The commonality of cortical architecture in perceptual, motor and higher cognitive areas of the brain suggests that one or just a few basic computational mechanisms underlie a variety of seemingly unrelated abilities. The map-seeking circuit (MSC) is a computational mechanism with plausible neuronal implementations which efficiently solves inverse transformation-discovery problems of the dimensionality found in vision, inverse kinematics, route-planning and other "brain-solvable" natural tasks. As in the brain, the cooperative interaction of MSCs operating in different domains allows efficient and robust solution to problem such as recognition of articulated objects. The algorithmic versions of MSC benefit from the same combinatorial efficiencies as the neuronal versions, making it a practical method for target recognition and other defense and security tasks. Several areas of application MSC are demonstrated.