The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Critical Branching Captures Activity in Living Neural Networks and Maximizes the Number of Metastable States
Abstract
Recent experimental work has shown that activity in living neural networks can propagate as a critical branching process that revisits many metastable states. Neural network theory suggests that attracting states could store information, but little is known about how a branching process could form such states. Here we use a branching process to model actual data and to explore metastable states in the network. When we tune the branching parameter to the critical point, we find that metastable states are most numerous and that network dynamics are not attracting, but neutral.
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Cites
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Self-citationModeling Brain Function: The World of Attractor Neural Networks
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Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks
Bertschinger, N., & Natschläger, T. (2004) · Neural Computation · doi:10.1162/089976604323057443 · Cited by 833
Optimal dynamical range of excitable networks at criticality
Kinouchi, O., & Copelli, M. (2006) · Nature Physics · doi:10.1038/nphys289 · Cited by 831
Are Biological Systems Poised at Criticality?
Mora, T., & Bialek, W. (2011) · Journal of Statistical Physics · doi:10.1007/s10955-011-0229-4 · Cited by 734
The Functional Benefits of Criticality in the Cortex
Shew, W. L., & Plenz, D. (2013) · The Neuroscientist · doi:10.1177/1073858412445487 · Cited by 607
The criticality hypothesis: how local cortical networks might optimize information processing
Beggs, J. M. (2008) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2007.2092 · Cited by 533
Self-citation
Metadata
- Authors
- Haldeman, C., & Beggs, J. M.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2005-02-07
- Volume
- 94
- Issue
- 5
- Pages
- 058101-058101
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2030362416
- Cited by
- 469
- References
- 25
- FWCI
- 5.91
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
