↓ Skip to main content

Critical Fluctuations in Cortical Models Near Instability

Overview of attention for article published in Frontiers in Physiology, January 2012
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)

Mentioned by

2 tweeters
2 Google+ users


28 Dimensions

Readers on

57 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Critical Fluctuations in Cortical Models Near Instability
Published in
Frontiers in Physiology, January 2012
DOI 10.3389/fphys.2012.00331
Pubmed ID

Matthew J. Aburn, C. A. Holmes, James A. Roberts, Tjeerd W. Boonstra, Michael Breakspear


Computational studies often proceed from the premise that cortical dynamics operate in a linearly stable domain, where fluctuations dissipate quickly and show only short memory. Studies of human electroencephalography (EEG), however, have shown significant autocorrelation at time lags on the scale of minutes, indicating the need to consider regimes where non-linearities influence the dynamics. Statistical properties such as increased autocorrelation length, increased variance, power law scaling, and bistable switching have been suggested as generic indicators of the approach to bifurcation in non-linear dynamical systems. We study temporal fluctuations in a widely-employed computational model (the Jansen-Rit model) of cortical activity, examining the statistical signatures that accompany bifurcations. Approaching supercritical Hopf bifurcations through tuning of the background excitatory input, we find a dramatic increase in the autocorrelation length that depends sensitively on the direction in phase space of the input fluctuations and hence on which neuronal subpopulation is stochastically perturbed. Similar dependence on the input direction is found in the distribution of fluctuation size and duration, which show power law scaling that extends over four orders of magnitude at the Hopf bifurcation. We conjecture that the alignment in phase space between the input noise vector and the center manifold of the Hopf bifurcation is directly linked to these changes. These results are consistent with the possibility of statistical indicators of linear instability being detectable in real EEG time series. However, even in a simple cortical model, we find that these indicators may not necessarily be visible even when bifurcations are present because their expression can depend sensitively on the neuronal pathway of incoming fluctuations.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 5%
Germany 2 4%
Netherlands 2 4%
United States 2 4%
Korea, Republic of 1 2%
Switzerland 1 2%
Unknown 46 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 30%
Student > Ph. D. Student 14 25%
Professor > Associate Professor 5 9%
Student > Master 5 9%
Unspecified 5 9%
Other 11 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 30%
Unspecified 9 16%
Medicine and Dentistry 8 14%
Physics and Astronomy 8 14%
Engineering 4 7%
Other 11 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 30 April 2013.
All research outputs
of 12,441,812 outputs
Outputs from Frontiers in Physiology
of 5,170 outputs
Outputs of similar age
of 124,019 outputs
Outputs of similar age from Frontiers in Physiology
of 3 outputs
Altmetric has tracked 12,441,812 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 5,170 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has gotten more attention than average, scoring higher than 74% 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 124,019 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 71% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.