Abstract

Progression through different synchronized and desynchronized regimes in brain networks has been reported to reflect physiological and behavioral states, such as working memory and attention. Moreover, intracranial recordings of epileptic seizures show a progression towards synchronization as brain regions are recruited and the seizures evolve. In this paper, we build on our previous work on noise- induced transitions on networks to explore the interplay between transitions and synchronization. We consider a bistable dynamical system that is initially at a stable equilibrium (quiescent) that coexists with an oscillatory state (active). The addition of noise will typically lead to escape from the quiescent to the active state. If a number of such systems are coupled, these escapes can spread sequentially in the manner of a “domino effect.” We illustrate our findings numerically in an example system with three coupled nodes. We first show that a symmetrically coupled network with amplitude-dependent coupling exhibits new phenomena of accelerating and decelerating domino effects modulated by the strength and sign of the coupling. This is quantified by numerically computing escape times for the system with weak coupling. We then apply phase-amplitude-dependent coupling and explore the interplay between synchronized and desynchronized dynamics in the system. We consider escape phases between nodes where the cascade of noise-induced escapes is associated with various types of partial synchrony along the sequence. We show examples for the three-node system in which there is multistability between in-phase and antiphase solutions where solutions switch between the two as the sequence of escapes progresses.

Keywords

  1. generalized Hopf normal form
  2. escape phase
  3. escape time
  4. sequential escape
  5. noise-induced transition

MSC codes

  1. 92C42
  2. 34D06
  3. 37H20
  4. 37G05

Get full access to this article

View all available purchase options and get full access to this article.

Supplementary Material

Index of Supplementary Materials

Title of paper: Sequential Escapes and Synchrony Breaking for Networks of Bistable Oscillatory Nodes

Authors: Jennifer Creaser, Peter Ashwin, and Krasimira Tsaneva-Atanasova

File: M134577_01.pdf

Type: PDF

Contents: Figure SM1 shows the choice of escape threshold lies between the potential barrier and the sink for parameter values considered here. Figures SM2-3 show realizations of a three node network with amplitude coupling; Figure SM4 shows realizations and order parameter values of a three node network with amplitude coupling; Figure SM5 shows an alternative approach to getting changes in synchrony in a three node system by changing the shear of each node. Figure SM6 shows the distributions of the escape times and the order parameters for a three node network at parameter values for which there is a change in synchrony during the sequence of escapes.

References

1.
D. G. Aronson, G. B. Ermentrout, and N. Kopell, Amplitude response of coupled oscillators, Phys. D, 41 (1990), pp. 403--449.
2.
P. Ashwin, J. Creaser, and K. Tsaneva-Atanasova, Fast and slow domino regimes in transient network dynamics, Phys. Rev. E, 96 (2017), 052309.
3.
P. Ashwin, J. Creaser, and K. Tsaneva-Atanasova, Sequential escapes: Onset of slow domino regime via a saddle connection, Eur. Phys. J. Special Topics, 227 (2018), pp. 1091--1100.
4.
A. K. al Azad and P. Ashwin, Within-burst synchrony changes for coupled elliptic bursters, SIAM J. Appl. Dynam. Syst., 9 (2010), pp. 261--281, https://doi.org/10.1137/090746045.
5.
O. Benjamin, T. H. Fitzgerald, P. Ashwin, K. Tsaneva-Atanasova, F. Chowdhury, M. P. Richardson, and J. R. Terry, A phenomenological model of seizure initiation suggests network structure may explain seizure frequency in idiopathic generalised epilepsy, J. Math. Neurosci., 2 (2012), pp. 1--30.
6.
N. Berglund and B. Gentz, Noise-Induced Phenomena in Slow-Fast Dynamical Systems: A Sample-Paths Approach, Springer Science+Business Media, 2006.
7.
N. Berglund, B. Fernandez, and B. Gentz, Metastability in interacting nonlinear stochastic differential equations I: From weak coupling to synchronization, Nonlinearity, 20 (2007), pp. 2551--2581.
8.
N. Berglund, B. Fernandez, and B. Gentz, Metastability in interacting nonlinear stochastic differential equations II: Large-n behaviour, Nonlinearity, 20 (2007), pp. 2583--2614.
9.
N. Berglund and B. Gentz, The Eyring-Kramers' Law for Potentials with Nonquadratic Saddles, preprint, http://arxiv.org/abs/0807.1681, 2008.
10.
N. Berglund, Kramers' law: Validity, derivations and generalisations, Markov Process. Related Fields, 19 (2013), pp. 459--490.
11.
T. O. Bergmann and J. Born, Phase-amplitude coupling: A general mechanism for memory processing and synaptic plasticity?, Neuron, 97 (2018), pp. 10--13.
12.
M. Chávez, M. Le Van Quyen, V. Navarro, M. Baulac, and J. Martinerie, Spatio-temporal dynamics prior to neocortical seizures: Amplitude versus phase couplings, IEEE Trans. Biomed. Engrg., 50 (2003), pp. 571--583.
13.
F. Clément and J.-P. Françoise, Mathematical modeling of the GnRH pulse and surge generator, SIAM J. Appl. Dyn. Syst., 6 (2007), pp. 441--456, https://doi.org/10.1137/060673825.
14.
J. Creaser, K. Tsaneva-Atanasova, and P. Ashwin, Sequential noise-induced escapes for oscillatory network dynamics, SIAM J. Appl. Dyn. Syst., 17 (2018), pp. 500--525, https://doi.org/10.1137/17M1126412.
15.
G. B. Ermentrout, n:m phase-locking of weakly coupled oscillators, J. Math. Biol., 12 (1981), pp. 327--342.
16.
H. Eyring, The activated complex in chemical reactions, J. Chem. Phys., 3 (1935), pp. 107--115.
17.
E. D. Fagerholm, R. J. Moran, I. R. Violante, R. Leech, and K. J. Friston, Dynamic causal modelling of phase-amplitude interactions, NeuroImage, 208 (2020), 116452.
18.
S. Fernández-García, M. Desroches, M. Krupa, and F. Clément, A multiple time scale coupling of piecewise linear oscillators: Application to a neuroendocrine system, SIAM J. Appl. Dyn. Syst., 14 (2015), pp. 643--673, https://doi.org/10.1137/140984464.
19.
K. D. Harris and A. Thiele, Cortical state and attention, Nature Rev. Neurosci., 12 (2011), pp. 509--523.
20.
L. Higginbotham, L. Trotti, D. Bliwise, and S. Miocinovic, Cortical phase amplitude coupling and pathologic synchronization during sleep in Parkinson's disease, Neurology, 92 (2019), S10.004.
21.
D. Holcman and M. Tsodyks, The emergence of up and down states in cortical networks, PLoS Comput. Biol., 2 (2006), e23.
22.
E. M. Izhikevich, Dynamical Systems in Neuroscience, MIT Press, 2007.
23.
D. Jercog, A. Roxin, P. Bartho, A. Luczak, A. Compte, and J. De La Rocha, Up-down cortical dynamics reflect state transitions in a bistable network, Elife, 6 (2017), e22425.
24.
P. Jiruska, M. De Curtis, J. G. Jefferys, C. A. Schevon, S. J. Schiff, and K. Schindler, Synchronization and desynchronization in epilepsy: Controversies and hypotheses, J. Physiol., 591 (2013), pp. 787--797.
25.
N. Kopell and G. Ermentrout, Symmetry and phaselocking in chains of weakly coupled oscillators, Comm. Pure Appl. Math., 39 (1986), pp. 623--660.
26.
H. A. Kramers, Brownian motion in a field of force and the diffusion model of chemical reactions, Physica, 7 (1940), pp. 284--304.
27.
A. Luczak, P. Barthó, S. L. Marguet, G. Buzsáki, and K. D. Harris, Sequential structure of neocortical spontaneous activity in vivo, Proc. Natl. Acad. Sci. USA, 104 (2007), pp. 347--352.
28.
K. Majumdar, P. D. Prasad, and S. Verma, Synchronization implies seizure or seizure implies synchronization?, Brain Topography, 27 (2014), pp. 112--122.
29.
H. Malchow, W. Ebeling, R. Feistel, and L. Schimansky-Geier, Stochastic bifurcations in a bistable reaction-diffusion system with Neumann boundary conditions, Ann. Phys., 495 (1983), pp. 151--160.
30.
R. Milton, N. Shahidi, and V. Dragoi, Dynamic states of population activity in prefrontal cortical networks of freely-moving macaque, Nature Comm., 11 (2020), 1948.
31.
A. Neiman, Synchronization-like phenomena in coupled stochastic bistable systems, Phys. Rev. E, 49 (1994), pp. 3484--3487.
32.
A. Pérez-Cervera, P. Ashwin, G. Huguet, T. M. Seara, and J. Rankin, The uncoupling limit of identical Hopf bifurcations with an application to perceptual bistability, J. Math. Neurosci., 9 (2019), pp. 1--33.
33.
S. Röblitz, C. Stötzel, P. Deuflhard, H. M. Jones, D.-O. Azulay, P. H. van der Graaf, and S. W. Martin, A mathematical model of the human menstrual cycle for the administration of GnRH analogues, J. Theoret. Biol., 321 (2013), pp. 8--27.
34.
A. Sherman, Anti-phase, asymmetric and aperiodic oscillations in excitable cells -- I: Coupled bursters, Bull. Math. Biol., 56 (1994), pp. 811--835.
35.
R. C. Sotero, Modeling the generation of phase-amplitude coupling in cortical circuits: From detailed networks to neural mass models, BioMed Res. Int., 2015 (2015), 915606.
36.
M. Steriade, A. Nunez, and F. Amzica, A novel slow ($< 1$ Hz) oscillation of neocortical neurons in vivo: Depolarizing and hyperpolarizing components, J. Neurosci., 13 (1993), pp. 3252--3265.
37.
A. T. Winfree, The Geometry of Biological Time, Interdiscip. Appl. Math. 12, Springer, 2001.
38.
E. Zavala, K. C. Wedgwood, M. Voliotis, J. Tabak, F. Spiga, S. L. Lightman, and K. Tsaneva-Atanasova, Mathematical modelling of endocrine systems, Trends Endocrinology Metabolism, 30 (2019), pp. 244--257.

Information & Authors

Information

Published In

cover image SIAM Journal on Applied Dynamical Systems
SIAM Journal on Applied Dynamical Systems
Pages: 2829 - 2846
ISSN (online): 1536-0040

History

Submitted: 16 June 2020
Accepted: 7 October 2020
Published online: 17 December 2020

Keywords

  1. generalized Hopf normal form
  2. escape phase
  3. escape time
  4. sequential escape
  5. noise-induced transition

MSC codes

  1. 92C42
  2. 34D06
  3. 37H20
  4. 37G05

Authors

Affiliations

Funding Information

Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 : EP/N014391/1

Funding Information

Institute for Advanced Study, Technische Universität München https://doi.org/10.13039/501100015072

Funding Information

Medical Research Council https://doi.org/10.13039/501100000265 : MR/S019499/1

Metrics & Citations

Metrics

Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited By

There are no citations for this item

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share on social media