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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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  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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4 tweeters

Citations

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86 Dimensions

Readers on

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177 Mendeley
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2 CiteULike
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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.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 177 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 2%
United States 3 2%
Cuba 2 1%
Spain 2 1%
Netherlands 2 1%
Australia 1 <1%
France 1 <1%
Unknown 159 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 51 29%
Student > Ph. D. Student 49 28%
Student > Master 22 12%
Professor 12 7%
Professor > Associate Professor 9 5%
Other 25 14%
Unknown 9 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 26%
Neuroscience 43 24%
Medicine and Dentistry 13 7%
Mathematics 12 7%
Engineering 11 6%
Other 34 19%
Unknown 18 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 15 October 2017.
All research outputs
#6,714,624
of 12,472,057 outputs
Outputs from PLoS Computational Biology
#3,446
of 4,978 outputs
Outputs of similar age
#55,033
of 121,133 outputs
Outputs of similar age from PLoS Computational Biology
#52
of 87 outputs
Altmetric has tracked 12,472,057 research outputs across all sources so far. This one is in the 45th percentile – i.e., 45% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,978 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.4. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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 121,133 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 87 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.