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Michael Campos

Researcher at Qualcomm

Publications -  38
Citations -  961

Michael Campos is an academic researcher from Qualcomm. The author has contributed to research in topics: Saccade & Signal. The author has an hindex of 9, co-authored 38 publications receiving 876 citations. Previous affiliations of Michael Campos include Northwestern University & California Institute of Technology.

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Journal ArticleDOI

Space–time wiring specificity supports direction selectivity in the retina

TL;DR: A mathematical model shows how such ‘space–time wiring specificity’ could endow SAC dendrites with receptive fields that are oriented in space–time and therefore respond selectively to stimuli that move in the outward direction from the soma.
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Dynamic Scheduling and Division of Labor in Social Insects

TL;DR: A method for assigning tasks or resources, based on a model of division of labor in social insects, is introduced and applied to a dynamic flow shop scheduling problem and both systems are able to adapt well to changing conditions.
Journal ArticleDOI

Deep brain stimulation for obsessive-compulsive disorder: past, present, and future

TL;DR: The authors review the development of DBS for OCD, describe the current understanding of the pathophysiological mechanisms of the disorder and how the underlying neural circuits might be modulated by DBS, and discuss the clinical studies that provide evidence for the use of this evolving therapy.
Journal ArticleDOI

Supplementary Motor Area Encodes Reward Expectancy in Eye-Movement Tasks

TL;DR: It is shown that neurons in the supplementary motor area (SMA), an area concerned with movements of the body and limbs, also carry a reward expectancy signal in the postsaccadic period of oculomotor tasks, and this signal is broadcast over a large extent of motor cortex, and may facilitate the learning of new, coordinated behavior between different body parts.
Patent

Method and system for implementing evolutionary algorithms

TL;DR: In this article, a method, computer program storage medium and system that implements evolutionary algorithms on heterogeneous computers is described, in which a central process resident in a central computer delegates subpopulations of individuals of similar fitness from a central pool to separate processes resident on peripheral computers where they evolve for a certain number of generations after which they return to the central pool before the delegation is repeated.