Charles Higgins Video
Computational neuroscience and bio-inspired engineering
My laboratory conducts research in areas that vary from computational neuroscience to biologically-inspired engineering. The unifying goal of all these projects is to understand the representations and computational architectures used by biological systems, which are quite different from (and in many cases functionally superior to) conventional engineering systems. These projects are conducted in close collaboration with "wet" neurobiology laboratories who perform anatomical, electrophysiological, and histological studies, mostly in insects.
In the area of computational neuroscience, we do mathematical and computational modeling of identified or postulated neural systems at levels from the biophysical to the highly abstract. This work is exemplified by our recent explorations into the neuronal basis of elementary visual motion detection in flies (Higgins, Douglass, and Strausfeld, Visual Neuroscience, 2004).
Our work in biologically-inspired engineering involves building highly efficient parallel continuous-time computing designs, the architectures of which directly mimic fundamental aspects of neuronal circuits. This work is primarily focused in the area of visual motion processing. At the core of such designs is an array of highly sensitive low-level visual motion detectors. Systems currently in development for autonomous airborne visual navigation include self-motion estimators, obstacle avoidance systems, and target tracking systems.
Dyhr JP, Higgins CM. 2010. The spatial frequency tuning of optic-flow-dependent behaviors in the bumblebee Bombus impatiens. J Exp Biol, 213(Pt10):1643-50.
Ozalevli E, Hasler P, and Higgins CM, 2006. "Winner-Take-All based Visual Motion Sensors," IEEE Transactions on Circuits and Systems II, vol. 53, no. 8, pp. 717-721.
Melano T, and Higgins CM, "The neuronal basis of direction selectivity in lobula plate tangential cells," Neurocomputing 65-66, pp 153-159.
Rivera-Alvidrez Z, and Higgins CM. 2005. "Contrast saturation in a neuronally-based model of elementary motion detection," Neurocomputing 65-66, pp. 173-179.
Higgins CM, Pant V, and Deutschmann R. 2005. "Analog VLSI implementation of spatio-temporal frequency tuned visual motion algorithms," IEEE Transactions on Circuits and Systems I, vol 52, no. 3, pp. 489-502.
Ozalevli E, and Higgins CM. 2005. "Recon gurable Biologically-Inspired Visual Motion Systems using Modular Neuromorphic VLSI chips," IEEE Transactions on Circuits and Systems I, vol. 52, no. 1, pp 79-92.
Higgins CM, and Pant V. 2004. "A biomimetic VLSI sensor for visual tracking of small moving targets," IEEE Transactions on Circuits and Systems I, Vol. 51, No. 12, pp. 2384-2394.
Higgins CM, Pant V. 2004. An elaborated model of fly small-target tracking. Biol Cybern. 91(6):417-28.
Higgins CM, Douglass JK, Strausfeld NJ. 2004. The computational basis of an identified neuronal circuit for elementary motion detection in dipterous insects. Vis Neurosci.21(4):567-86
Higgins CM. 2004. Nondirectional motion may underlie insect behavioral dependence on image speed. Biol Cybern. 91(5):326-32.