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A Canonical Model of Multistability and Scale-Invariance in Biological Systems

Overview of attention for article published in PLoS Computational Biology, August 2012
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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Title
A Canonical Model of Multistability and Scale-Invariance in Biological Systems
Published in
PLoS Computational Biology, August 2012
DOI 10.1371/journal.pcbi.1002634
Pubmed ID
Authors

Frank Freyer, James A. Roberts, Petra Ritter, Michael Breakspear

Abstract

Multistability and scale-invariant fluctuations occur in a wide variety of biological organisms from bacteria to humans as well as financial, chemical and complex physical systems. Multistability refers to noise driven switches between multiple weakly stable states. Scale-invariant fluctuations arise when there is an approximately constant ratio between the mean and standard deviation of a system's fluctuations. Both are an important property of human perception, movement, decision making and computation and they occur together in the human alpha rhythm, imparting it with complex dynamical behavior. Here, we elucidate their fundamental dynamical mechanisms in a canonical model of nonlinear bifurcations under stochastic fluctuations. We find that the co-occurrence of multistability and scale-invariant fluctuations mandates two important dynamical properties: Multistability arises in the presence of a subcritical Hopf bifurcation, which generates co-existing attractors, whilst the introduction of multiplicative (state-dependent) noise ensures that as the system jumps between these attractors, fluctuations remain in constant proportion to their mean and their temporal statistics become long-tailed. The simple algebraic construction of this model affords a systematic analysis of the contribution of stochastic and nonlinear processes to cortical rhythms, complementing a recently proposed biophysical model. Similar dynamics also occur in a kinetic model of gene regulation, suggesting universality across a broad class of biological phenomena.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 224 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 2%
Switzerland 3 1%
Netherlands 2 <1%
Cuba 2 <1%
United States 2 <1%
Spain 2 <1%
Australia 1 <1%
France 1 <1%
Unknown 207 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 61 27%
Researcher 57 25%
Student > Master 29 13%
Professor 12 5%
Student > Bachelor 9 4%
Other 30 13%
Unknown 26 12%
Readers by discipline Count As %
Neuroscience 55 25%
Agricultural and Biological Sciences 45 20%
Engineering 18 8%
Medicine and Dentistry 13 6%
Mathematics 12 5%
Other 43 19%
Unknown 38 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 March 2024.
All research outputs
#5,449,869
of 25,446,666 outputs
Outputs from PLoS Computational Biology
#4,157
of 8,978 outputs
Outputs of similar age
#37,963
of 185,090 outputs
Outputs of similar age from PLoS Computational Biology
#41
of 107 outputs
Altmetric has tracked 25,446,666 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,978 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 53% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 185,090 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.