Charles M Higgins

Abstract:

Tracking of a target in a cluttered environment requires extensive computational architecture. However, even a small housefly is adept at pursuing its prey. Biomimetic algorithms suggest a novel way of looking at this problem. In the lobula plate of a fly's brain, a neural circuit is hypothesized based on a tangential cell called the figure detection (FD) cell. The proposed small target fixation algorithm based on electrophysiological recordings does not take into account the translation of the pursuer during pursuit. We have modified the biological algorithm to include this aspect of tracking. In this paper, we present the elaborated biological algorithm for small target tracking, and an analog VLSI implementation of this algorithm.

PMID: 15597180;Abstract:

Flies have the capability to visually track small moving targets, even across cluttered backgrounds. Previous computational models, based on figure detection (FD) cells identified in the fly, have suggested how this may be accomplished at a neuronal level based on information about relative motion between the target and the background. We experimented with the use of this "small-field system model" for the tracking of small moving targets by a simulated fly in a cluttered environment and discovered some functional limitations. As a result of these experiments, we propose elaborations of the original small-field system model to support stronger effects of background motion on small-field responses, proper accounting for more complex optical flow fields, and more direct guidance toward the target. We show that the elaborated model achieves much better tracking performance than the original model in complex visual environments and discuss the biological implications of our elaborations. The elaborated model may help to explain recent electrophysiological data on FD cells that seem to contradict the original model.

Abstract:

We introduce a biologically inspired computational architecture for small-field detection and wide-field spatial integration of visual motion based on the general organizing principles of visual motion processing common to organisms from insects to primates. This highly parallel architecture begins with two-dimensional (2-D) image transduction and signal conditioning, performs small-field motion detection with a number of parallel motion arrays, and then spatially integrates the small-field motion units to synthesize units sensitive to complex wide-field patterns of visual motion. We present a theoretical analysis demonstrating the architecture's potential in discrimination of wide-field motion patterns such as those which might be generated by self-motion. A custom VLSI hardware implementation of this architecture is also described, incorporating both analog and digital circuitry. The individual custom VLSI elements are analyzed and characterized, and system-level test results demonstrate the ability of the system to selectively respond to certain motion patterns, such as those that might be encountered in self-motion, at the exclusion of others. © 2002 IEEE.

PMID: 21161268;Abstract:

Insect navigational behaviors including obstacle avoidance, grazing landings, and visual odometry are dependent on the ability to estimate flight speed based only on visual cues. In honeybees, this visual estimate of speed is largely independent of both the direction of motion and the spatial frequency content of the image. Electrophysiological recordings from the motion-sensitive cells believed to underlie these behaviors have long supported spatio-temporally tuned correlation-type models of visual motion detection whose speed tuning changes as the spatial frequency of a stimulus is varied. The result is an apparent conflict between behavioral experiments and the electrophysiological and modeling data. In this article, we demonstrate that conventional correlation-type models are sufficient to reproduce some of the speed-dependent behaviors observed in honeybees when square wave gratings are used, contrary to the theoretical predictions. However, these models fail to match the behavioral observations for sinusoidal stimuli. Instead, we show that non-directional motion detectors, which underlie the correlation-based computation of directional motion, can be used to mimic these same behaviors even when narrowband gratings are used. The existence of such non-directional motion detectors is supported both anatomically and electrophysiologically, and they have been hypothesized to be critical in the Dipteran elementary motion detector (EMD) circuit. © 2010 Springer-Verlag.