J
John M. Beggs
Researcher at Indiana University
Publications - 79
Citations - 7100
John M. Beggs is an academic researcher from Indiana University. The author has contributed to research in topics: Artificial neural network & Information processing. The author has an hindex of 28, co-authored 74 publications receiving 6058 citations. Previous affiliations of John M. Beggs include Virginia Bioinformatics Institute & National Institutes of Health.
Papers
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Journal ArticleDOI
Neuronal Avalanches in Neocortical Circuits
John M. Beggs,Dietmar Plenz +1 more
TL;DR: This work shows that propagation of spontaneous activity in cortical networks is described by equations that govern avalanches, and suggests that “neuronal avalanches” may be a generic property of cortical networks, and represent a mode of activity that differs profoundly from oscillatory, synchronized, or wave-like network states.
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Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures.
John M. Beggs,Dietmar Plenz +1 more
TL;DR: The long-term stability, diversity, and temporal precision of these avalanches indicate that they fulfill many of the requirements expected of a substrate for memory and suggest that they play a central role in both information transmission and storage within cortical networks.
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Being Critical of Criticality in the Brain
John M. Beggs,Nicholas M. Timme +1 more
TL;DR: The concept of criticality is explained and substantial objections to the criticality hypothesis raised by skeptics are reviewed, and counter points are presented in dialog form.
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Critical Branching Captures Activity in Living Neural Networks and Maximizes the Number of Metastable States
Clayton Haldeman,John M. Beggs +1 more
TL;DR: When the branching parameter is tuned to the critical point, it is found that metastable states are most numerous and that network dynamics are not attracting, but neutral.
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The criticality hypothesis: how local cortical networks might optimize information processing
TL;DR: In this paper, the authors review recent experiments on networks of cortical neurons, showing that they appear to be operating near the critical point in a phase transition between total randomness and boring order, and suggest that criticality may allow cortical networks to optimize information processing.