Charles M Higgins

Charles M Higgins

Associate Professor, Neuroscience
Associate Professor, Neuroscience - GIDP
Associate Professor, Applied Mathematics - GIDP
Associate Professor, Electrical and Computer Engineering
Associate Professor, Entomology / Insect Science - GIDP
Associate Professor, BIO5 Institute
Department Affiliations
Contact
(520) 621-6604

Research Interest

Research Interest

Charles Higgins, PhD, is an Associate Professor in the Department of Neuroscience with a dual appointment in Electrical Engineering at the University of Arizona where he is also leader of the Higgins Lab. Though he started his career as an electrical engineer, his fascination with the natural world has led him to study insect vision and visual processing, while also trying to meld together the worlds of robotics and biology. His research ranges from software simulations of brain circuits to interfacing live insect brains with robots, but his driving interest continues to be building truly intelligent machines.Dr. Higgins’ lab 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. These projects are conducted in close collaboration with neurobiology laboratories that perform anatomical, electrophysiological, and histological studies, mostly in insects.More than three years ago he captured news headlines when he and his lab team demonstrated a robot they built which was guided by the brain and eyes of a moth. The moth, immobilized inside a plastic tube, was mounted on a 6-inch-tall wheeled robot. When the moth moved its eyes to the right, the robot turned in that direction, proving brain-machine interaction. While the demonstration was effective, Charles soon went to work to overcome the difficulty the methodology presented in keeping the electrodes attached to the brain of the moth while the robot was in motion. This has led him to focus his work on another insect species.

Publications

Higgins, C. M. (2004). Nondirectional motion may underlie insect behavioral dependence on image speed. Biological Cybernetics, 91(5), 326-332.

PMID: 15490223;Abstract:

Behavioral experiments suggest that insects make use of the apparent image speed on their compound eyes to navigate through obstacles, control flight speed, land smoothly, and measure the distance they have flown. However, the vast majority of electrophysiological recordings from motion-sensitive insect neurons show responses which are tuned in spatial and temporal frequency and are thus unable to unambiguously represent image speed. We suggest that this contradiction may be resolved at an early stage of visual motion processing using nondirectional motion sensors that respond proportionally to image speed until their peak response. We describe and characterize a computational model of these sensors and propose a model by which a spatial collation of such sensors could be used to generate speed-dependent behavior.

Northcutt, B. D., & Higgins, C. M. (2017). An Insect-Inspired Model for Visual Binding II: Functional Analysis and Visual Attention. Biological Cybernetics, 111(2), 207-227.
Özalevli, E., & Higgins, C. M. (2005). Reconfigurable biologically inspired visual motion systems using modular neuromorphic VLSI Chips. IEEE Transactions on Circuits and Systems I: Regular Papers, 52(1), 79-92.

Abstract:

Visual motion information provides a variety of clues that enable biological organisms from insects to primates to efficiently navigate in unstructured environments. We present modular mixed-signal very large-scale integration (VLSI) implementations of the three most prominent biological models of visual motion detection. A novel feature of these designs is the use of spike integration circuitry to implement the necessary temporal filtering. We show how such modular VLSI building blocks make it possible to build highly powerful and flexible vision systems. These three biomimetic motion algorithms are fully characterized and compared in performance. The visual motion detection models are each implemented on separate VLSI chips, but utilize a common silicon retina chip to transmit changes in contrast, and thus four separate mixed-signal VLSI designs are described. Characterization results of these sensors show that each has a saturating response to contrast to moving stimuli, and that the direction of motion of a sinusoidal grating can be detected down to less than 5% contrast, and over more than an order of magnitude in velocity, while retaining modest power consumption. © 2005 IEEE.

Rivera-Alvidrez, Z., & Higgins, C. M. (2005). Contrast saturation in a neuronally-based model of elementary motion detection. Neurocomputing, 65-66(SPEC. ISS.), 173-179.

Abstract:

The Hassenstein-Reichardt (HR) correlation model is commonly used to model elementary motion detection in the fly. Recently, a neuronally-based computational model was proposed which, unlike the HR model, is based on identified neurons. The response of both models increases as the square of contrast, although the response of insect neurons saturates at high contrasts. We introduce a saturating nonlinearity into the neuronally-based model in order to produce contrast saturation and discuss the neuronal implications of these elements. Furthermore, we show that features of the contrast sensitivity of movement-detecting neurons are predicted by the modified model. © 2004 Elsevier B.V. All rights reserved.

Pant, V., & Higgins, C. M. (2004). A biomimetic VLSI architecture for small target tracking. Proceedings - IEEE International Symposium on Circuits and Systems, 3, III5-III8.

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.