The DOI matches its Crossref record; 116 of the 116 references it cites were found in a registry or an index.
Being Critical of Criticality in the Brain
Open accessAbstract
Relatively recent work has reported that networks of neurons can produce avalanches of activity whose sizes follow a power law distribution. This suggests that these networks may be operating near a critical point, poised between a phase where activity rapidly dies out and a phase where activity is amplified over time. The hypothesis that the electrical activity of neural networks in the brain is critical is potentially important, as many simulations suggest that information processing functions would be optimized at the critical point. This hypothesis, however, is still controversial. Here we will explain the concept of criticality and review the substantial objections to the criticality hypothesis raised by skeptics. Points and counter points are presented in dialog form.
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Cites
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Cited by
Self-organized criticality as a fundamental property of neural systems
Hesse, J., & Gross, T. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00166 · Cited by 440
Inverse statistical problems: from the inverse Ising problem to data science
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Power-law statistics and universal scaling in the absence of criticality
Touboul, J., & Destexhe, A. (2017) · Physical Review E · doi:10.1103/physreve.95.012413 · Cited by 210
Identification of optimal structural connectivity using functional connectivity and neural modeling
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Revisiting the Quantum Brain Hypothesis: Toward Quantum (Neuro)biology?
Jedlicka, P. (2017) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2017.00366 · Cited by 97
Metadata
- Authors
- Beggs, J. M., & Timme, N.
- Venue
- Frontiers in Physiology
- Publisher
- Frontiers Media SA
- Published
- 2012
- Volume
- 3
- Pages
- 163-163
- ISSN
- 1664-042X (ISSN-L)
- OpenAlex
- W2142211992
- Cited by
- 494
- References
- 116
- FWCI
- 15.86
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
