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Woodrow L. Shew
Researcher at University of Arkansas
Publications - 68
Citations - 3901
Woodrow L. Shew is an academic researcher from University of Arkansas. The author has contributed to research in topics: Population & Local field potential. The author has an hindex of 24, co-authored 64 publications receiving 3311 citations. Previous affiliations of Woodrow L. Shew include École normale supérieure de Lyon & University of Maryland, College Park.
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Neuronal avalanches imply maximum dynamic range in cortical networks at criticality
TL;DR: In this article, the authors show that cortical networks that generate neuronal avalanches benefit from a maximized dynamic range, i.e., the ability to respond to the greatest range of stimuli.
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The Functional Benefits of Criticality in the Cortex
Woodrow L. Shew,Dietmar Plenz +1 more
TL;DR: An introductory-level thought experiment is described to provide the reader with an intuitive understanding of criticality and quantitative evidence that three functional properties of the cortex are optimized at criticality is reviewed.
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Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches.
TL;DR: In this paper, the authors measured activity patterns obtained from multisite local field potential recordings in cortex cultures, urethane-anesthetized rats, and awake macaque monkeys and found that both information capacity and information transmission are maximized at a particular intermediate E/I, at which ongoing activity emerges as neuronal avalanches.
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Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches
TL;DR: Close agreement between in vitro and model results suggest that neuronal avalanches and peak information capacity arise because of criticality and are general properties of cortical networks with balanced E/I.
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Predicting criticality and dynamic range in complex networks: effects of topology.
TL;DR: A general theoretical approach to study the effects of network topology on dynamic range is developed, which quantifies the range of stimulus intensities resulting in distinguishable network responses and finds that the largest eigenvalue of the weighted network adjacency matrix governs the network dynamic range.