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Criteria for robustness of heteroclinic cycles in neural microcircuits

Overview of attention for article published in The Journal of Mathematical Neuroscience, November 2011
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Title
Criteria for robustness of heteroclinic cycles in neural microcircuits
Published in
The Journal of Mathematical Neuroscience, November 2011
DOI 10.1186/2190-8567-1-13
Pubmed ID
Authors

Peter Ashwin, Özkan Karabacak, Thomas Nowotny

Abstract

We introduce a test for robustness of heteroclinic cycles that appear in neural microcircuits modeled as coupled dynamical cells. Robust heteroclinic cycles (RHCs) can appear as robust attractors in Lotka-Volterra-type winnerless competition (WLC) models as well as in more general coupled and/or symmetric systems. It has been previously suggested that RHCs may be relevant to a range of neural activities, from encoding and binding to spatio-temporal sequence generation.The robustness or otherwise of such cycles depends both on the coupling structure and the internal structure of the neurons. We verify that robust heteroclinic cycles can appear in systems of three identical cells, but only if we require perturbations to preserve some invariant subspaces for the individual cells. On the other hand, heteroclinic attractors can appear robustly in systems of four or more identical cells for some symmetric coupling patterns, without restriction on the internal dynamics of the cells.

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Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 10%
Unknown 18 90%

Demographic breakdown

Readers by professional status Count As %
Professor 3 15%
Researcher 3 15%
Student > Master 3 15%
Student > Ph. D. Student 2 10%
Student > Postgraduate 2 10%
Other 3 15%
Unknown 4 20%
Readers by discipline Count As %
Mathematics 6 30%
Agricultural and Biological Sciences 3 15%
Physics and Astronomy 2 10%
Medicine and Dentistry 1 5%
Neuroscience 1 5%
Other 1 5%
Unknown 6 30%