Chimera States and Seizures in a Mouse Neuronal Model

Christopher M. Danforth; Henry M. Mitchell; J. Matthew Mahoney; Peter Sheridan Dodds

REVIEW 3 major objections 6 minor 28 references

Chimera States and Seizures in a Mouse Neuronal Model

T0 review · 3 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A modified Hindmarsh-Rose network on the mouse connectome produces persistent chimera states—coexisting synchrony and asynchrony—across a large region of coupling space, matching seizure-like EEG activity.

desk verdict A well-executed application of a standard chimera index to a new connectome, but the headline 'large region' claim needs a null baseline and better statistical support before it can be taken at face value. read the letter →

arxiv 1908.07039 v2 pith:EGRR3SM3 submitted 2019-08-02 q-bio.NC nlin.PS

Henry M. Mitchell , Peter Sheridan Dodds , J. Matthew Mahoney , Christopher M. Danforth This is my paper · ORCID

classification q-bio.NCnlin.PS MSC 92C2037N2534C15

keywords chimerastatesHindmarsh-Rosemodelmouseconnectomeneuralmassepileptiformactivitysynchronymetastabilityseizuredynamics

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to show that a network of modified Hindmarsh-Rose neurons, wired according to the mesoscale mouse connectome, spontaneously generates persistent chimera states—where some brain regions fire in synchrony while others remain desynchronized—and that the resulting activity looks superficially like epileptic seizures on an EEG. The claim matters because chimera states are a leading mathematical framework for epilepsy, and if a connectome-realistic model produces them without being designed to do so, it strengthens the case that chimeras are a generic, perhaps universal, feature of brain-network dynamics. The paper maps a two-parameter space of coupling strengths—$\alpha$ within cortices and $\beta$ between cortices—and reports high chimera-like index values in a low-coupling island ($\alpha$ below about 0.1, $\beta$ below about 0.05) and along a band near the boundary of the model's unphysical region. These states persist for simulation times up to 4000 time units, and their EEG-like traces show seizure-like spike runs in regions such as the medulla, hypothalamus, and cortical subplate.

What carries the argument

The central machinery is the modified Hindmarsh-Rose neural-mass model (Eqs. 6–10): each of 213 masses obeys a 3-variable spiking equation with a sigmoidal activation function, coupled twice—once within cortices with strength $\alpha$ through matrix $G'$ and once between cortices with strength $\beta$ through matrix $G''$. The two coupling matrices come from the mouse connectome, with connection strengths binned into four levels (Eq. 11). The detectors are the chimera-like index $\chi$ (time-averaged variance of the order parameter across the 13 coarse areas, Eq. 2) and the metastability index $m$ (average within-area variance of the order parameter over time, Eq. 3), following Shanahan. The model is integrated with a 4th-order Runge-Kutta scheme for 5000 time units, discarding the first 1000 as transients, and phases are extracted from spike times via Eq. 12. The two-parameter sweep in $\alpha$ and $\beta$, with the physical–aphysical boundary delimiting the valid region, is what turns the connectome into a phase diagram for chimeric seizure-like activity.

What would settle it

For a parameter pair inside the low-coupling island (e.g., $\alpha\approx 0.04$, $\beta\approx 0.02$), record the instantaneous phases of all 213 subregions and map them spatially; the claim is falsified if no coarse area simultaneously contains a phase-locked cluster and a phase-scattered cluster, or if shuffling the intra-areal connections leaves $\chi$ just as high.

Watch

Extended reading notes

Core claim

On the paper's own terms, the core discovery is that a network of 213 Hindmarsh-Rose neural masses, grouped into 13 coarse cortical areas according to the mouse connectome, exhibits persistent chimera states in a large region of the $(\alpha,\beta)$ coupling plane rather than only in a narrow parameter sliver. The chimera-like index $\chi$, computed as the time-averaged variance of the order parameter across the 13 areas, reaches its highest values in a surprising low-coupling patch at $\alpha\lesssim 0.1$ and $\beta\lesssim 0.05$, and again along a roughly diagonal band near the boundary where the model becomes aphysical (some neurons stop firing). The paper argues this is not a calculation artifact: the patch persists across repeated runs, has low run-to-run variance, and matches a qualitative phase portrait showing synchronized domains in the medulla, hypothalamus, and isocortex alongside asynchronous areas. It also finds that $\alpha$ has a slightly larger influence than $\beta$ on the physical–aphysical boundary, consistent with mean intra-cortical connection strengths exceeding mean inter-cortical strengths once zero-connection areas are set aside.

Load-bearing premise

The result rests on the assumption that the chimera-like index $\chi$—a single number tracking how much synchrony varies across the 13 brain regions—truly detects coexisting synchronized and desynchronized domains, rather than merely picking up activity differences between regions or artifacts caused by the nearby unphysical boundary.

Editorial extensions

If this is right

If the model's persistent chimera states are real, then seizure-like EEG traces can emerge from the mouse connectome without any seizure-specific parameter tuning, supporting the idea that chimeras are a generic brain-network phenomenon.
The low-coupling island ($\alpha\lesssim 0.1$, $\beta\lesssim 0.05$) and the band along the physical–aphysical boundary become candidate regions for experimental or clinical searches for seizure precursors.
The sharp drop in $\chi$ near $\alpha\approx 0.1$, where coupling becomes comparable to intrinsic dynamics, implies a regime threshold: below it, weak intra-areal coupling suffices to sustain chimera dynamics; above it, synchrony fades.
Because the hypothalamus and hippocampal formation show the strongest synchrony, the model singles out these areas as likely seizure foci, matching clinical observations and giving a concrete prediction for future connectome-based seizure models.
The physical–aphysical boundary's slope near $-1$ in $(\alpha,\beta)$ space, explained by mean intra- vs inter-cortical connection strengths, provides a simple topological criterion for where the model ceases to produce meaningful dynamics.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A direct spatial test not reported in the paper: map instantaneous phases of all 213 subregions inside the low-coupling island; the chimera interpretation requires a synchronized cluster and a desynchronized cluster to coexist within the same coarse area at the same time.
Because $\chi$ is computed across only 13 coarse areas, it may conflate true chimeras with plain inter-area differences in firing rate; recomputing the index on sliding spatial windows or on the 213 fine areas would separate those alternatives.
The model's assumption of identical nodes is a natural extension point: adding heterogeneity in the input current $I_j$, which the paper notes disrupts chimeras, should shrink or shift the low-coupling island; mapping that shift would give an experimentally controllable lever on seizure-like dynamics.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Henry,

You'll want to know this paper before you cite it. It takes the Hindmarsh-Rose network model from Santos et al. (2017) and runs it on the mesoscale mouse connectome (Oh et al. 2014), computing the Shanahan chimera-like index χ and metastability m over a two-parameter (α,β) sweep. The genuinely new result is a low-coupling island (α around 0.04, β around 0.02) with high χ, plus a linear aphysical boundary. The authors show the island is not a single-run fluke (Fig. 9), and the code is on GitHub. That part is solid and worth a look.

The soft spots are real. First, the abstract says 'large region' but the high-χ region is actually a thin band near the aphysical boundary plus a small island in the corner. That overstates the finding. Second, χ as defined in Eq. 2 is the time-averaged variance of order parameters across the 13 coarse areas. It will be high whenever communities have persistently different mean synchrony, which can arise purely from topological heterogeneity (different internal connection densities) without any genuine chimera dynamics. The authors don't include a surrogate-network or null baseline, and they don't validate χ against a spatial measure of coexisting coherent/incoherent domains beyond two illustrative time series. This is the load-bearing gap. Third, the error bars on the main landscape (Fig. 7) are absent; they show a variance image only for the small island (Fig. 8B and Fig. 10B), and the number of runs for the other sweeps is unclear (only the [0,1]×[0,1] sweep was repeated 10 times). The exclusion of the aphysical region is handled reasonably, but the boundary slope analysis is informal.

I think the stress-test note is half right. The concern that χ can be inflated by static inter-community differences is valid and needs a control. But the claim that χ doesn't detect coexisting synchrony and asynchrony is a bit too strong: Shanahan's index is explicitly a community-level chimera detector, and the 13 coarse areas are the communities. Still, without a null network, the low-coupling island could be an artefact of the measure on this particular connectome.

If I were refereeing this, I'd ask for three things: a degree-preserving randomized network control, error bars or at least a statement of runs per parameter point on the main sweep, and a direct spatial check (e.g., local order parameter maps) at representative points in the island and the boundary band. The paper is worth engaging with; the model is cheap, the code is available, and the island is a potentially interesting feature. It deserves peer review, though the central claim needs to be toned down and the evidence sharpened.

Reading group: maybe.

Referee Report

3 major / 6 minor

Summary. The paper studies a modified Hindmarsh-Rose neural-mass network whose coupling graph is the mesoscale mouse connectome (213 fine areas grouped into 13 coarse areas). The authors sweep two coupling parameters, α (intra-cortical) and β (inter-cortical), and measure the chimera-like index χ and metastability index m defined by Shanahan (2010). They report that the physical region of parameter space contains persistent chimera-like states, with high χ in a low-coupling island near (α,β) = (0.04,0.02) and along a band near the aphysical boundary, and they argue that some simulated traces qualitatively resemble epileptiform EEG. The central claim is that this network 'produces superficially epileptiform activity converging on persistent chimera states in a large region' of the (α,β) plane.

Significance. If the central claim is supported, the paper would be a useful contribution to the literature connecting chimera states to seizure dynamics, extending prior cat-brain work to a modern mouse connectome. Strengths include the clear description of the numerical implementation, the availability of the simulation code, the explicit parameter sweeps in Table 2, and the verification against the Santos et al. (2017) model. The paper is also honest about its qualitative EEG comparison and about treating connection strengths as constant. However, the main result depends entirely on the chimera-like index χ, which is not validated against a direct spatial definition of chimera states, and no null-network baseline is provided; these gaps are load-bearing for the headline claim.

major comments (3)

[Eq. (2) and Section 3.3] The chimera-like index χ is the time-averaged variance of community order parameters r_c(t). As the authors themselves show in Fig. 4, the 13 coarse areas differ substantially in size, degree, and mean connection strength, so static topological heterogeneity alone can produce persistently different mean synchrony levels across communities and hence a large χ without any community actually containing coexisting synchronized and desynchronized domains. The manuscript does not calibrate χ against a direct spatial measure of chimera states (e.g., local order-parameter fields or contiguous coherent/incoherent regions), and it does not include a null-network or surrogate control. Because χ is the only quantitative detector used for the claim of 'persistent chimera states in a large region' in the abstract, this is a load-bearing gap that should be addressed before the central claim can be accepted.
[Fig. 8 and Table 2] The low-coupling island is presented as a 'surprising' feature and the authors state that it is not a calculation error, but the evidence is only visual. The high-resolution sweep (α,β)∈(0,0.2)×(0,0.2) is not repeated in Table 2; only the [0,1]×[0,1] sweep is listed as having 10 runs. Fig. 8B shows a 'variance of the chimera-like index' but no error bars or confidence intervals are given, and the quantity being averaged over is not clearly stated (variance across runs, across time, or across communities). Without repeated runs or a null baseline, the island could be an artifact of the measure or of the particular initial conditions; the authors should provide error bars or repeated-run statistics for Fig. 8, as they did for the larger sweep in Fig. 10.
[Section 4.2 and Eq. (13)] The aphysical region is excluded from the analysis based on the criterion that neurons do not fire, and the slope of the boundary is explained using average connection strengths in Fig. 4. However, the threshold K_j in Eq. (13) is not estimated or tested, and the argument for the boundary slope remains qualitative. Since the definition of the physical region determines which parts of parameter space are available for the chimera claim, the authors should provide a quantitative test of Eq. (13) or at least a sensitivity analysis of the firing criterion (e.g., varying the threshold 'increased past 1' or the definition of a firing event). This would also help clarify whether the high-χ band near the boundary is a dynamical chimera regime or an artifact of proximity to the aphysical region.

minor comments (6)

[References] The reference [Ljungberg et al.(2009)] contains a corrupted author string '/quotesingle.ts1Arcangelo'; please fix it.
[Fig. 2 caption] 'crebellar cortex' should be 'cerebellar cortex'.
[Throughout] The term 'aphysical' is used consistently; if this is a deliberate term, please define it at first use, otherwise replace with 'unphysical' or 'non-physical'.
[Section 4.1 vs 4.3] The sentence 'The highly chimeric portion of the landscape appears to be mostly below the β=α line' appears in Section 4.1 before Fig. 7 is presented; consider moving it to Section 4.3 where the chimera landscape is discussed.
[Fig. 10C] The metastability index m is computed and displayed in Fig. 10C, but it is not discussed in the main text; a brief interpretation would help the reader understand what m adds to the chimera-like index results.
[Section 3.3] The code availability is mentioned only in a footnote; if the journal has a data-availability policy, consider adding a formal statement in the main text.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the chimera landscape is a direct simulation output under published model, connectome, and standard chimera-like indices.

full rationale

The paper does not fit any parameter to the quantity it reports. The modified Hindmarsh-Rose model, its parameters, the mouse connectome, and the chimera-like index chi and metastability index m are all taken from cited prior work (Santos et al. 2017; Oh et al. 2014; Shanahan 2010), and the reported chi landscape is a direct simulation output, not a fitted or renamed target. The definition of chi as the time-averaged variance of order parameters across communities is a standard operationalization from Shanahan (2010); the fact that chi might also be elevated by static inter-community heterogeneity is a validity concern, not a circular reduction of the central claim to its inputs, because the claim is about what the model produces under this measure. No self-citations are load-bearing, no uniqueness theorem is invoked, and no ansatz is smuggled in via citation. The physical-region restriction (neurons must fire) is a filtering criterion applied before reporting chimeras, and it does not by construction force high chi. Accordingly, no circular steps are identified.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the chosen network discretization, the validity of the chimera indices, and the ad hoc exclusion of the aphysical region. The model parameters themselves are inherited from Santos et al. (2017), so they are not introduced here.

free parameters (3)

Connection significance threshold p<0.01
Chosen by the authors to filter the Oh et al. (2014) connectome; affects which edges are retained. No justification beyond 'sufficient certainty' (Section 3.2).
Connection strength bin edges = [1e-4, 1e-2, 1]
The thresholds in Eq. 11 that map raw connection strengths to integer weights 0-3 are arbitrary and chosen to match Santos et al. (2017). Different bins would change G and the resulting chimera landscape.
Aphysical region criterion = neurons never fire (x_j never exceeds 1)
The definition of 'physical' excludes any parameter set where a neuron never fires. This is an ad hoc model-validity criterion that defines the region over which the chimera claim is made (Section 4.2).

assumptions (5)

domain assumption The modified Hindmarsh-Rose equations (Eqs. 6-10) are a valid neural-mass model of seizure-related dynamics.
The paper adopts the model from Santos et al. (2017) for cat brain and applies it to mouse without re-validation. The link to seizures is then inferred from the model's outputs.
domain assumption The mesoscale mouse connectome of Oh et al. (2014), discretized as in Eq. 11, is an accurate substrate for the dynamics.
The coarse-grained 13-area network is treated as the true structural connectivity; no sensitivity analysis to the discretization is performed.
domain assumption The chimera-like index chi and metastability index m (Eqs. 2-5) correctly identify chimera states in this system.
The indices are taken from Shanahan (2010) and assumed to detect coexisting synchrony/asynchrony. The paper validates only visually on selected runs, not across the parameter sweep.
domain assumption Chimera states persist on the simulation timescale.
The paper cites Wolfrum and Omel'chenko (2011) to argue chimeras are chaotic transients that will not die on a 4000-time-unit window, but no persistence analysis is performed.
ad hoc to paper The aphysical region can be excluded from the analysis.
The boundary between physical and aphysical is not derived; the exclusion is based on the observation that some neurons never fire (Section 4.2). This shapes the region where the chimera claim is made.

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Cite this review

Pith. "Pith review of Chimera States and Seizures in a Mouse Neuronal Model." pith.science (2026). https://pith.science/paper/EGRR3SM3

@misc{pith2026190807039,
  author       = {Pith},
  title        = {Pith review of: Chimera States and Seizures in a Mouse Neuronal Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EGRR3SM3}},
  note         = {Machine review of arXiv:1908.07039}
}

read the original abstract

Chimera states---the coexistence of synchrony and asynchrony in a nonlocally-coupled network of identical oscillators---are often used as a model framework for epileptic seizures. Here, we explore the dynamics of chimera states in a network of modified Hindmarsh-Rose neurons, configured to reflect the graph of the mesoscale mouse connectome. Our model produces superficially epileptiform activity converging on persistent chimera states in a large region of a two-parameter space governing connections (a) between subcortices within a cortex and (b) between cortices. Our findings contribute to a growing body of literature suggesting mathematical models can qualitatively reproduce epileptic seizure dynamics.

Figures

Figures reproduced from arXiv: 1908.07039 by the authors.

**Figure 2.** A. A matrix representation of the mouse connectome, with strengths as defined by Eq. 11. The [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** A breakdown of the network. A. & B. show [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (7 more)

**Figure 4.** Figure 4: The average connection strengths for each neuron [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: A typical EEG trace. The first row (“Wild type”) shows a normal awake adult mouse [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: A typical run of the Hindmarsh-Rose simulation. A. The mean membrane potential within each [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

**Figure 7.** Figure 7: The chimera-like landscape of parameter space on ( [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]

**Figure 8.** Figure 8: A. The chimera-like index χ of runs with (α, β) ∈ (0, 0.2) × (0, 0.2). As before, the chimera-like index is normalized to 1 7 . Note that the values of the index are much higher in this patch than in most of the rest of (α, β) ∈ (0, 1) × (0, 1) ( [PITH_FULL_IMAGE:figu…

**Figure 9.** Figure 9: A run of the Hindmarsh-Rose simulation in the chimeric island. A. The mean membrane potential [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]

**Figure 10.** Figure 10: Additional analyses of the parameter landscape. A. The number of runs (out of 10) which gave [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]

Discussion (0). Continue with ORCID to comment.

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Works this paper leans on

28 extracted references · 22 canonical work pages

[1]

M., Mirollo, R., Strogatz, S

[Abrams et al.(2008)Abrams, Mirollo, Strogatz & Wiley] Abrams, D. M., Mirollo, R., Strogatz, S. H. & Wiley, D. A

work page 2008
[10]

All parameter sets which are not yellow in A were excluded from the other analyses

which gave physical results. All parameter sets which are not yellow in A were excluded from the other analyses. B. The variance of the chimera-like index. C. The metastability index. D. The variance of the metastability index. E. The mean order parameter over time, averaged across runs. F. The variance between runs of the mean order parameter. 13 [Bansal...

work page 2019
[12]

Chimera-like states in modular neural networks,

“Chimera-like states in modular neural networks,” Scientiﬁc Reports 6, doi:10.1038/srep19845. [H¨ ohleinet al.(2019)H¨ ohlein, Kemeth & Krischer] H¨ ohlein, K., Kemeth, F. P. & Krischer, K

work page doi:10.1038/srep19845 2019
[13]

Lya- punov spectra and collective modes of chimera states in globally coupled Stuart-Landau oscillators,

“Lya- punov spectra and collective modes of chimera states in globally coupled Stuart-Landau oscillators,” Physical Review E 100, doi:10.1103/physreve.100.022217. [Knox et al.(2019)Knox, Harris, Graddis, Whitesell, Zeng, Harris, Shea-Brown & Mihalas] Knox, J. E., Harris, K. D., Graddis, N., Whitesell, J. D., Zeng, H., Harris, J. A., Shea-Brown, E. & Mihalas, S

doi:10.1103/physreve.100.022217 2019
[14]

High-resolution data-driven model of the mouse connectome,

“High-resolution data-driven model of the mouse connectome,” Network Neuroscience 3, 217–236. [Kruk et al.(2018)Kruk, Maistrenko & Koeppl] Kruk, N., Maistrenko, Y. & Koeppl, H

work page 2018
[15]

Self- propelled chimeras,

“Self- propelled chimeras,” Physical Review E 98, doi:10.1103/PhysRevE.98.032219. [Kuramoto & Battogtokh(2002)] Kuramoto, Y. & Battogtokh, D

work page doi:10.1103/physreve.98.032219 2002
[17]

Coupled oscillators for modeling and analysis of EEG/MEG oscillations,

“Coupled oscillators for modeling and analysis of EEG/MEG oscillations,” Biomedizinische Technik/Biomedical Engineering52, 83–89. [Ljungberg et al.(2009)Ljungberg, Sunnen, Lugo, Anderson & D /quotesingle.ts1Arcangelo] Ljungberg, M. C., Sunnen, C. N., Lugo, J. N., Anderson, A. E. & D /quotesingle.ts1Arcangelo, G

work page 2009
[20]

Chimera states in mechanical oscillator networks,

“Chimera states in mechanical oscillator networks,” Proceedings of the National Academy of Sciences 110, 10563–10567. 14 [Oh et al.(2014)Oh, Harris, Ng, Winslow, Cain, Mihalas, Wang, Lau, Kuan, Henry, Mortrud, Ouellette, Nguyen, Sorensen, Slaughterbeck, Wakeman, Li, Feng, Ho, Nicholas, Hirokawa, Bohn, Joines, Peng, Hawrylycz, Phillips, Hohmann, Wohnoutka,...

work page 2014

Show all 28 references

[21]

A mesoscale connectome of the mouse brain,

“A mesoscale connectome of the mouse brain,” Nature 508, 207–214. [Panaggio & Abrams(2015)] Panaggio, M. J. & Abrams, D. M

2015
[23]

Recurrence quantiﬁcation analysis of chimera states,

“Recurrence quantiﬁcation analysis of chimera states,”Physics Letters A 379, 2188–2192. [Santos et al.(2017)Santos, Szezech, Borges, Iarosz, Caldas, Batista, Viana & Kurths] Santos, M., Szezech, J., Borges, F., Iarosz, K., Caldas, I., Batista, A., Viana, R. & Kurths, J

2017
[24]

Chimera-like states in a neuronal network model of the cat brain,

“Chimera-like states in a neuronal network model of the cat brain,” Chaos, Solitons & Fractals 101, 86–91. [Shanahan(2010)] Shanahan, M

2010
[26]

Towards a large-scale model of patient-speciﬁc epileptic spike-wave discharges,

“Towards a large-scale model of patient-speciﬁc epileptic spike-wave discharges,” Biological Cybernetics 107, 83–94. [Westbrook(2013)] Westbrook, G. L

2013
[27]

(McGraw Hill Medical), pp

“50,” Seizures and Epilepsy , 5th ed. (McGraw Hill Medical), pp. 1116–1139. [Wolfrum & Omel’chenko(2011)] Wolfrum, M. & Omel’chenko, O. E

2011
[2002]

Coexistence of coherence and incoherence in nonlocally coupled phase oscillators,

“Coexistence of coherence and incoherence in nonlocally coupled phase oscillators,” Nonlinear Phenomena in Complex Systems 5, 380–385. [Leistritz et al.(2007)Leistritz, Putsche, Schwab, Hesse, S¨ uße, Haueisen & Witte] Leistritz, L., Putsche, P., Schwab, K., Hesse, W., S¨ uße,...

2007
[2004]

Chimera states for coupled oscilla- tors,

“Chimera states for coupled oscilla- tors,” Physical Review Letters 93, doi:10.1103/PhysRevLett.93.174102. [Andrzejak et al.(2016)Andrzejak, Rummel, Mormann & Schindler] Andrzejak, R. G., Rummel, C., Mor- mann, F. & Schindler, K

2016 doi
[2007]

The epileptic hippocampus revisited: Back to the future,

“The epileptic hippocampus revisited: Back to the future,”Epilepsy Currents 7, 116–118, doi:10.1111/j.1535-7511.2007.00194.x, URL https://doi.org/10.1111/j.1535-7511. 2007.00194.x. [Baier et al.(2012)Baier, Goodfellow, Taylor, Wang & Garry] Baier, G., Goodfellow, M., Taylor, P...

2012
[2008]

Solvable model for chimera states of coupled oscillators,

“Solvable model for chimera states of coupled oscillators,” Physical Review Letters 101, doi:10.1103/PhysRevLett.101.084103. [Abrams & Strogatz(2004)] Abrams, D. M. & Strogatz, S. H

2004 doi
[2009]

Rapamycin suppresses seizures and neuronal hypertrophy in a mouse model of cortical dysplasia,

“Rapamycin suppresses seizures and neuronal hypertrophy in a mouse model of cortical dysplasia,” Disease Models & Mechanisms 2, 389–398. [Maguire & Salpekar(2013)] Maguire, J. & Salpekar, J. A

2013
[2010]

Metastable chimera states in community-structured oscillator net- works,

“Metastable chimera states in community-structured oscillator net- works,” Chaos: An Interdisciplinary Journal of Nonlinear Science 20, doi:10.1063/1.3305451. [Taylor et al.(2012)Taylor, Goodfellow, Wang & Baier] Taylor, P. N., Goodfellow, M., Wang, Y. & Baier, G

2012 doi
[2011]

Chimera states are chaotic transients,

“Chimera states are chaotic transients,” Physical Review E 84, doi:10.1103/PhysRevE.84.015201. [Xie et al.(2014)Xie, Knobloch & Kao] Xie, J., Knobloch, E. & Kao, H.-C

2014 doi
[2012]

The importance of modeling epileptic seizure dynamics as spatio- temporal patterns,

“The importance of modeling epileptic seizure dynamics as spatio- temporal patterns,” Frontiers in Physiology 3, doi:10.3389/fphys.2012.00281. 12 0 1 0 1β A 0 1 B 0 1 C 0 1 α 0 1β D 0 1 α E 0 1 α F 1 2 3 4 5 6 7 8 9 10 0.00005 0.00010 0.00015 0.00020 0.00025 0.00030 0.0 0.2 0....

2012 arXiv
[2013]

Stress, seizures, and hypothala- mic–pituitary–adrenal axis targets for the treatment of epilepsy,

“Stress, seizures, and hypothala- mic–pituitary–adrenal axis targets for the treatment of epilepsy,” Epilepsy & Behavior 26, 352–362, doi:10.1016/j.yebeh.2012.09.040, URL https://doi.org/10.1016/j.yebeh.2012.09.040. [Martens et al.(2013)Martens, Thutupalli, Fourriere & Hallats...

2013 doi
[2014]

Heterogeneity of heterogeneities in neuronal networks,

“Heterogeneity of heterogeneities in neuronal networks,” Frontiers in Computational Neuroscience 8, doi:10.3389/fncom.2014.00161, URL https: //doi.org/10.3389/fncom.2014.00161. [Breakspear(2017)] Breakspear, M

2017
[2015]

Chimera states: coexistence of coherence and incoherence in networks of coupled oscillators,

“Chimera states: coexistence of coherence and incoherence in networks of coupled oscillators,” Nonlinearity 28, R67–R87. [Santos et al.(2015)Santos, Szezech, Batista, Caldas, Viana & Lopes] Santos, M., Szezech, J., Batista, A., Caldas, I., Viana, R. & Lopes, S

2015
[2016]

All together now: Analogies between chimera state collapses and epileptic seizures,

“All together now: Analogies between chimera state collapses and epileptic seizures,” Scientiﬁc Reports 6, doi:10.1038/srep23000. [Avoli(2007)] Avoli, M

2007 doi
[2017]

Dynamic models of large-scale brain activity,

“Dynamic models of large-scale brain activity,” Nature Neuro- science 20, 340–352. [Chouzouris et al.(2018)Chouzouris, Omelchenko, Zakharova, Hlinka, Jiruska & Sch¨ oll] Chouzouris, T., Omelchenko, I., Zakharova, A., Hlinka, J., Jiruska, P. & Sch¨ oll, E

2018
[2018]

Chimera states in brain networks: Empirical neural vs. modular fractal connectivity,

“Chimera states in brain networks: Empirical neural vs. modular fractal connectivity,” Chaos: An Interdisciplinary Journal of Nonlinear Science 28, doi:10.1063/1.5009812. [Hizanidis et al.(2016)Hizanidis, Kouvaris, Zamora-L´ opez, D´ ıaz-Guilera & Antonopoulos] Hizanidis, J., ...

2016 doi
[2019]

Cognitive chimera states in human brain networks,

“Cognitive chimera states in human brain networks,” Science Advances 5, eaau8535. [Baroni & Mazzoni(2014)] Baroni, F. & Mazzoni, A

2014

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

M., Mirollo, R., Strogatz, S

[Abrams et al.(2008)Abrams, Mirollo, Strogatz & Wiley] Abrams, D. M., Mirollo, R., Strogatz, S. H. & Wiley, D. A

work page 2008

[2] [10]

All parameter sets which are not yellow in A were excluded from the other analyses

which gave physical results. All parameter sets which are not yellow in A were excluded from the other analyses. B. The variance of the chimera-like index. C. The metastability index. D. The variance of the metastability index. E. The mean order parameter over time, averaged across runs. F. The variance between runs of the mean order parameter. 13 [Bansal...

work page 2019

[3] [12]

Chimera-like states in modular neural networks,

“Chimera-like states in modular neural networks,” Scientiﬁc Reports 6, doi:10.1038/srep19845. [H¨ ohleinet al.(2019)H¨ ohlein, Kemeth & Krischer] H¨ ohlein, K., Kemeth, F. P. & Krischer, K

work page doi:10.1038/srep19845 2019

[4] [13]

Lya- punov spectra and collective modes of chimera states in globally coupled Stuart-Landau oscillators,

“Lya- punov spectra and collective modes of chimera states in globally coupled Stuart-Landau oscillators,” Physical Review E 100, doi:10.1103/physreve.100.022217. [Knox et al.(2019)Knox, Harris, Graddis, Whitesell, Zeng, Harris, Shea-Brown & Mihalas] Knox, J. E., Harris, K. D., Graddis, N., Whitesell, J. D., Zeng, H., Harris, J. A., Shea-Brown, E. & Mihalas, S

doi:10.1103/physreve.100.022217 2019

[5] [14]

High-resolution data-driven model of the mouse connectome,

“High-resolution data-driven model of the mouse connectome,” Network Neuroscience 3, 217–236. [Kruk et al.(2018)Kruk, Maistrenko & Koeppl] Kruk, N., Maistrenko, Y. & Koeppl, H

work page 2018

[6] [15]

Self- propelled chimeras,

“Self- propelled chimeras,” Physical Review E 98, doi:10.1103/PhysRevE.98.032219. [Kuramoto & Battogtokh(2002)] Kuramoto, Y. & Battogtokh, D

work page doi:10.1103/physreve.98.032219 2002

[7] [17]

Coupled oscillators for modeling and analysis of EEG/MEG oscillations,

“Coupled oscillators for modeling and analysis of EEG/MEG oscillations,” Biomedizinische Technik/Biomedical Engineering52, 83–89. [Ljungberg et al.(2009)Ljungberg, Sunnen, Lugo, Anderson & D /quotesingle.ts1Arcangelo] Ljungberg, M. C., Sunnen, C. N., Lugo, J. N., Anderson, A. E. & D /quotesingle.ts1Arcangelo, G

work page 2009

[8] [20]

Chimera states in mechanical oscillator networks,

“Chimera states in mechanical oscillator networks,” Proceedings of the National Academy of Sciences 110, 10563–10567. 14 [Oh et al.(2014)Oh, Harris, Ng, Winslow, Cain, Mihalas, Wang, Lau, Kuan, Henry, Mortrud, Ouellette, Nguyen, Sorensen, Slaughterbeck, Wakeman, Li, Feng, Ho, Nicholas, Hirokawa, Bohn, Joines, Peng, Hawrylycz, Phillips, Hohmann, Wohnoutka,...

work page 2014