Tackling Learning Intractability Through Topological Organization and Regulation of Cortical Networks

Thangavelautham, Jekan; D’Eleuterio, Gabriele M. T.

doi:10.1109/tnnls.2011.2178311

Cited by 18 publications

(40 citation statements)

References 27 publications

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“…Among them, the modeling of neuronal networks of a brain can be viewed as a typical application of complex networks [2], [3]. In [4]- [6], the theoretical modeling, tackling learning of neuronal networks and the applications of neuronal networks to image processing have been investigated, respectively. Modern brain mapping approaches such as diffusion MRI, functional MRI, EEG, and MEG have constantly produced large datasets of anatomical and functional connection patterns.…”

Section: Introductionmentioning

confidence: 99%

On Controllability of Neuronal Networks With Constraints on the Average of Control Gains

Tang

Wang

Gao

et al. 2014

IEEE Trans. Cybern.

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Abstract-Control gains play an important role in the control of a natural or technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible solutions as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that, as the constraint becomes more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide insightful lights on developing new techniques to control a realistic complex network efficiently.

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Section: Introductionmentioning

confidence: 99%

On Controllability of Neuronal Networks With Constraints on the Average of Control Gains

Tang

Wang

Gao

et al. 2014

IEEE Trans. Cybern.

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“…SphereX has the same goals as the Microbots, but with the goal of launching fewer robots, that are better equipped with sciencegrade instruments. Moreover, the path planning approach addressed in this paper is motivated by multiple hopping robots navigating a maze [38], where one robot hops at a time within a local area of safety, collects more information and plans for their next hop. Our past work has shown has shown the feasibility of multiple small robots working together as a network [25], [26], [27], [28].…”

Section: Related Work and Motivationmentioning

confidence: 99%

Path planning and navigation inside off-world lava tubes and caves

Kalita

Morad

Ravindran

et al. 2018

2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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Detailed surface images of the Moon and Mars reveal hundreds of cave-like openings. These cave-like openings are theorized to be remnants of lava-tubes and their interior maybe in pristine conditions. These locations may have well preserved geological records of the Moon and Mars, including evidence of past water flow and habitability. Exploration of these caves using wheeled rovers remains a daunting challenge. These caves are likely to have entrances with caved-in ceilings much like the lava-tubes of Arizona and New Mexico. Thus, the entrances are nearly impossible to traverse even for experienced human hikers. Our approach is to utilize the SphereX robot, a 3 kg, 30 cm diameter robot with computer hardware and sensors of a smartphone attached to rocket thrusters. Each SphereX robot can hop, roll or fly short distances in low gravity, airless or lowpressure environments. Several SphereX robots maybe deployed to minimize single-point failure and exploit cooperative behaviors to traverse the cave. There are some important challenges for navigation and path planning in these cave environments. Localization systems such as GPS are not available nor are they easy to install due to the signal blockage from the rocks. These caves are too dark and too large for conventional sensor such as cameras and miniature laser sensors to perform detailed mapping and navigation. In this paper, we identify new techniques to map these caves by performing localized, cooperative mapping and navigation. In our approach, a team of SphereX robots much like a team of cave explorer will adopt specialized roles to perform navigation. For a minimal science mission, these robots need to obtain camera images and basic maps of the cave interior to be transmitted back to a lander or rover situated outside the cave. The teams of SphereX robots form a bucket brigade and partition the currently accessible volume of the cave. Then the teams of robots attempt to expand their reach deeper into the cave and sense their progress. Imaging the cave interior is expensive and require use of high-power strobe lights. The images would be compiled into a 3D point cloud and meshed by the lander or transmitted to ground. Using this conservative approach, we ensure the robots are always within communication reach of a lander/rover outside the cave. Once large segments of the cave are mapped, the rovers may lay down a network of mirrors to beam sunlight and laser light from a base station at the cave entrance to the far reaches of the cave. These mirrors also help the robots identify a pathway back to the cave entrance. Efforts are underway to perform field experiments to validate the feasibility our proposed approach to cave exploration.

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“…AMIGO is advancement over our nano-lander concept, as it contains a multi-functional inflatable [29][30][31][32] for use in mobility, communications and tracking and has a propulsion system finely tuned to the asteroid low-gravity environment [26]. AIMGO by hopping and rolling will be able to navigate the maze-like surface pathways using autonomous control [36].…”

Section: Introductionmentioning

confidence: 99%

A Milli-Newton Propulsion System for the Asteroid Mobile Imager and Geologic Observer (AMIGO)

Wilburn

Asphaug

Thangavelautham

2019

2019 IEEE Aerospace Conference

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Exploration of small bodies, namely comets and asteroids remain a challenging endeavor due to their low gravity. The risk is so high that missions such as Hayabusa II and OSIRIS-REx will be performing touch and go missions to obtain samples. The next logical step is to perform longer-term mobility on the surface of these asteroid. This can be accomplished by sending small landers of a 1 kg or less with miniature propulsion systems that can just offset the force of asteroid gravity. Such a propulsion system would ideally be used to hop on the surface of the asteroid. Hopping has been found to be most efficient form of mobility on low-gravity. Use of wheels for rolling presents substantial challenges as the wheel can't gain traction to roll. The Asteroid Mobile Imager and Geologic Observer (AMIGO) utilizes 1 kg landers that are stowed in a 1U CubeSat configuration and deployed, releasing an inflatable that is 1-m in diameter. The inflatable is attached to the top of the 1U lander, enabling high speed communications and a means of easily tracking lander from an overhead mothership. Milligravity propulsion is required for the AMIGO landers to perform ballistic hops on the asteroid surface. The propulsion system is used to navigate the lander across the surface of the asteroid under the extremely low gravity while taking care to not exceed escape velocity. Although the concept for AMIGO missions is to use multiple landers, the more surface area evaluated by each lander the better. Without a propulsion system, each AMIGO will have a limited range of observable area. The propulsion system also serves as a rough attitude control system (ACS), as it enables pointing and regulation over where the lander is positioned via an array of MEMS thrusters. Several different techniques have been proposed for hopping nano-landers on low gravity environments including use of reaction wheels, electro-polymers, and rocket thrusters. In this concept, we will be heating sublimate to provide propulsive thrust which is simple and effective. Storing the propulsive gas as a solid provides much better storage density to ensure the longest lifetime possible. The starting point for this propulsion system involves selection of an appropriate, high-performance sublimate that meets the mobility needs of AMIGO. The paper will cover selection of the right sublimate, initial design and prototyping of the thruster using standard off-the-shelf arrays of electro-active MEMs valves. The device will be integrated with the fuel source with thrust profiles measured inside a millinewton test stand inside a vacuum chamber. MEMS devices are to be used because of the weight and volume saving potential compared to traditional large-sized thrusting mechanisms. Through these efforts we are advancing on a milli-newton thruster that use non-combustibles and can be readily integrated onto nano-landers for asteroid surface exploration.

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Tackling Learning Intractability Through Topological Organization and Regulation of Cortical Networks

Cited by 18 publications

References 27 publications

On Controllability of Neuronal Networks With Constraints on the Average of Control Gains

On Controllability of Neuronal Networks With Constraints on the Average of Control Gains

Path planning and navigation inside off-world lava tubes and caves

A Milli-Newton Propulsion System for the Asteroid Mobile Imager and Geologic Observer (AMIGO)

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