A universal animal communication tempo resonates with the receiver's brain

Daniel M. Abrams; Guy Amichay; Vijay Balasubramanian

REVIEW 3 major objections 4 minor 46 references

A universal animal communication tempo resonates with the receiver's brain

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Across eight orders of magnitude in body size, animals communicate isochronously at roughly 0.5–4 Hz, a band the paper traces to resonance in the receiver's neural circuits.

desk verdict A genuinely interesting cross-species synthesis and a clean but under-powered model; the universal-tempo claim needs a real sampling frame and the model's 2 Hz peak is an input, not a derivation. read the letter →

arxiv 2508.21530 v1 pith:3HPJMUXG submitted 2025-08-29 q-bio.NC physics.bio-phq-bio.PE

Guy Amichay , Vijay Balasubramanian , Daniel M. Abrams This is my paper · ORCID

classification q-bio.NCphysics.bio-phq-bio.PE

keywords animalcommunicationisochronytempodeltabandneuralresonanceKuramotooscillatorssynchronizationmeta-analysis

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

The paper claims that isochronous communication—repeated, metronome-like signals such as firefly flashes, cricket chirps, frog calls, bird displays, and ape duets—clusters in a narrow 0.5–4 Hz band across animals spanning eight orders of magnitude in body weight and using visual, acoustic, and gestural modalities. The authors argue this is not a production-side accident: signalers are physically capable of faster and slower tempos, yet many opt into this band. Instead, they locate the cause in the receiver's neural machinery, proposing that small circuits of typical neurons, with integration times of a few hundred milliseconds, respond most strongly to stimuli near 2 Hz. Simulations of coupled-oscillator circuits support the claim: entrainment peaks at the mean intrinsic neuron frequency, is robust to circuit wiring, and is broadened when the neuron population is more diverse. A correct result would imply that a shared neuronal biophysical constraint, not ecology or anatomy alone, sets the default tempo of animal communication.

What carries the argument

The engine of the argument is a 'receiver circuit' of Kuramoto phase oscillators—simple models in which each unit has a phase and a natural frequency—coupled through a directed graph and driven by an external periodic signal that acts like an additional node. The order parameter $R$, the magnitude of the average complex phase, measures how well the circuit entrains to the forcing. Two numerical probes carry the load: an exhaustive enumeration of all 1,665 non-isomorphic directed 5-node circuits with 10 edges, used to show that entrainment at 2 Hz does not depend on topology; and resonance curves over forcing frequency at fixed all-to-all coupling, used to show that the response peaks at the mean intrinsic frequency and broadens as the spread of neuron frequencies and the input strength increase. The supplementary Arnold-tongue calculation and $N=100$ simulations support the same single-peak picture.

What would settle it

A systematic, pre-registered survey of animal communication recordings that measures inter-onset-interval distributions without filtering for tempo or isochrony would settle the empirical claim: if species free to choose their tempos do not cluster between 0.5 and 4 Hz, the universal hotspot is an artifact. On the mechanism side, an electrophysiology experiment presenting periodic stimuli across frequencies to sensory cortex would falsify the resonance account if the largest evoked circuit response occurs outside the delta band.

Watch

Extended reading notes

Core claim

The central discovery claimed is a universal tempo hotspot: evolutionarily distant species, from flashing fireflies and chirping crickets to frogs, fish, birds, sea lions, apes, and humans, communicate isochronously at roughly 0.5–4 Hz, a band neuroscience calls the delta wave. The paper proposes that this band is favored because it matches the resonance of small receiver circuits made of typical neurons. In the model, each neuron is a phase oscillator with intrinsic frequency near 2 Hz, and a circuit driven by an external periodic signal shows an order-parameter peak at that frequency; the response survives across almost all tested wiring diagrams and is amplified and widened by neuronal heterogeneity. The authors interpret the peak as a neural resonance curve, with a single tongue at 2 Hz and no harmonic or subharmonic tongues, consistent with a biophysical timescale set by neuronal integration rather than by signal production mechanics.

Load-bearing premise

The load-bearing premise sits in the Data section: the 0.5–4 Hz clustering is real rather than an artifact of the authors' manually curated, non-exhaustive set of published examples, and as the paper says, selection bias cannot be fully excluded.

Editorial extensions

If this is right

Signals outside 0.5–4 Hz should either be rare or come with a special constraint, such as echolocation in bats or mechanical limits on very large or very small limbs for gestural signals.
Receiver circuits do not need fine-tuned wiring to respond best at this tempo, so the same preference can reappear in independently evolved nervous systems.
Greater diversity of neuron properties in a receiver circuit broadens its response, giving natural selection a concrete benefit for heterogeneous neuron populations.
Because the 0.5–4 Hz band is the low end of brain rhythms, external signals in this band may couple most easily into faster brain rhythms, providing a route by which a shared tempo could entrain cognition.

Reading between the lines

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

A testable prediction this mechanism leaves unstated: receivers should entrain or respond most strongly to artificial stimuli near 2 Hz even in species whose own signals use other tempos, because the resonance sits in the receiver rather than the signaler.
The curated dataset could be checked automatically: a broad analysis of animal acoustic and video repositories that measures inter-onset-interval distributions without a tempo filter would reveal whether the 0.5–4 Hz cluster survives unbiased sampling.
The model used phase oscillators without inhibition; whether the resonance survives in spiking neuron models with inhibitory connections is an open test of the claim's robustness.

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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

What you should know: this is a hypothesis-generating synthesis, not a settled universal law. The cross-species compilation is genuinely new as a compilation, and the simulations are clean, but the central claims outrun the evidence.

The paper is at its best in the opening fieldwork observation — fireflies and crickets in Thailand both signaling near 2.4 Hz — and in the breadth of the meta-analysis across insects, crustaceans, fish, amphibians, birds, and mammals. The authors are unusually honest: they explicitly flag selection bias, admit the dataset is nonexhaustive, and describe the manual estimation of inter-onset intervals from figures. The computational work is also solid in its own terms: exhaustive enumeration of all 1665 directed 5-node graphs shows entrainability is largely topology-independent, and the resonance curves with an Arnold tongue at 2 Hz are cleanly presented.

The soft spots are real and are mostly the ones the stress-test note identifies. First, the empirical hotspot is not demonstrated against any null. We don't know how many species were screened, how many rhythmic signals were rejected, or what a random sample of published animal rhythms would look like. The two counterexamples (bushcrickets at 11–14 Hz, bats at 10–14 Hz) are mentioned but not counted, so the frequency of outliers is unknown. Without a corpus-level analysis, "universal" rests on a curated list. Second, the model's resonance peak sits at the mean natural frequency μ, which the Methods fix at 2 Hz. That makes the match to the 0.5–4 Hz band partly by construction. The paper acknowledges as much, but the acknowledgment doesn't turn an input assumption into a derived prediction. The neural biophysics argument is plausible but not actually tested.

I don't think the circularity is fatal if the paper is read as a proof of concept: yes, a Kuramoto circuit with typical neuronal timescales resonates near 2 Hz, so a 0.5–4 Hz tempo is plausible for receivers. But the word "test" in the abstract overstates what the model does.

Who is this for? Researchers working on rhythm, animal communication, or neural resonance will find it a useful starting point and a good source of references. It deserves a serious referee, not a desk reject, because the synthesis is valuable and the hypothesis is worth sharpening. I would send it to review with the expectation of major revision: the empirical section needs a systematic sampling frame and a statistical test, and the model should be reframed as a plausibility demonstration rather than a test of the universality claim. The authors seem capable of making those changes.

Referee Report

3 major / 4 minor

Summary. The paper reports a meta-analysis of isochronous communication tempos across a wide range of animal taxa, claiming a universal concentration in the 0.5–4 Hz band, and proposes that this reflects resonance of small neural circuits in receivers. The authors support this proposal with Kuramoto-model simulations of N=5 oscillator circuits, showing that entrainment is largely independent of circuit topology and that resonance curves peak near the mean intrinsic frequency of the oscillators, which is set to 2 Hz. The paper also reports a field observation of co-located fireflies and crickets at similar tempos and includes an explicit discussion of the risk of selection bias in the meta-analysis.

Significance. If established, a universal 0.5–4 Hz communication tempo across taxa and modalities would be a striking discovery with implications for evolutionary biology, neuroscience, and the origins of human musical tempo. The paper has several strengths: it acknowledges the selection-bias problem explicitly, includes counterexamples outside the claimed band, and provides a genuinely exhaustive enumeration of all non-isomorphic directed graphs for the small circuits studied. The modeling idea that receiver-side neural resonance, rather than sender-side biomechanics, sets a common tempo is interesting and testable. However, the current evidence does not support the universal claim: the empirical dataset is a manually curated compilation without a quantitative null, and the model's resonance peak is placed by assumption rather than derived from neuronal biophysics. These load-bearing gaps must be addressed before the paper's central assertions can be accepted.

major comments (3)

[Results, Data and Methods, Data from previously published work]
[Methods, Computational experiments, Eq. (1) and Fig. 3C]
[Methods, Computational experiments, critical coupling setting]

minor comments (4)

[Results, Data section, paragraph 2]
[Methods, Field data]
[References]
[General]

Circularity Check

1 steps flagged · score 6.0 of 10

The model's 2 Hz resonance peak is inherited from the chosen mean oscillator frequency, so the 'prediction' of the 0.5–4 Hz hotspot reduces to an input assumption.

self definitional [Methods, 'Computational experiments' (Eq. 1, Kuramoto model); Fig. 3C caption]
"As Fig. 3C shows, we found that the response peaked at the mean oscillator frequency ... When we varied the graphs Aij we kept the mean frequency fixed at µ = 2 Hz. ... The dashed line in the leftmost plot is at 2 Hz—the mean of the natural frequencies of the circuits."

The simulated resonance at ~2 Hz is set by construction: natural frequencies in Eq. (1) are drawn from a Gaussian with mean µ = 2 Hz, and the paper states that the response peaked at the mean oscillator frequency. A driven Kuramoto population generically entrains or resonates near its natural frequency distribution, so placing the distribution at 2 Hz places the resonance at 2 Hz. The model does not derive the 2 Hz value from neuron biophysics; the few-hundred-millisecond integration times are mapped onto a 2 Hz mean as an input. Thus the model's agreement with the empirical 0.5–4 Hz hotspot is a restatement of the input rather than an independent prediction.

full rationale

The paper's empirical meta-analysis is a separate, openly acknowledged compilation with selection-bias caveats; that is a data-support limitation, not circularity. The circular step is confined to the modeling: the central 'prediction' that receiver circuits respond best at ~2 Hz is inherited from the chosen mean natural frequency µ = 2 Hz of the Kuramoto oscillators (Methods: 'we kept the mean frequency fixed at µ = 2 Hz'; Fig. 3C: peak at 'the mean of the natural frequencies of the circuits'). Because Eq. (1) is a driven oscillator system, resonance near the mean of the oscillator frequencies is a generic consequence, so the hotspot location is an input assumption. Some model findings (topology insensitivity, heterogeneity broadening, no harmonic tongues) are non-circular numerical results, but they do not rescue the frequency-location claim. Score 6: one 'prediction' reduces by construction, while the empirical claim retains independent content.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central conceptual move is to connect a biophysical timescale (neural integration time) to the observed tempo band. This connection is mediated by a free parameter: the mean oscillator frequency is set to 2 Hz, which is inside the target band. The model does not predict the 0.5-4 Hz band from first principles; it demonstrates that a circuit of neurons with 2 Hz intrinsic dynamics responds best at 2 Hz. The empirical meta-analysis is the independent part of the paper, but it is a curated sample subject to selection bias.

free parameters (4)

mean natural frequency mu = 2 Hz
The distribution of oscillator natural frequencies is centered at 2 Hz, chosen to match the observed communication band. Since the resonance peak appears at the mean frequency, this choice effectively predetermines the model output.
epsilon (distance below critical coupling) = 0.1
The coupling K is set to 90% of the theoretical critical coupling. This choice keeps the unforced system subcritical and influences the sharpness of the resonance, but is not fitted to data.
circuit size N = 5 (main), 100 (supplement)
The main exploration is limited to N = 5 with 10 directed edges. While the supplement checks N = 100, the organismal relevance of N = 5 is an assumption.
heterogeneity sigma = varied, not fit
The standard deviation of natural frequencies is a free parameter of the model explored in Fig. 3C. It is not fitted to biological data, so it is a modeling knob rather than a fit, but it controls the width of the resonance.

assumptions (4)

domain assumption Kuramoto oscillators with sinusoidal coupling capture the relevant dynamics of neural circuits for this question.
The paper models neurons as phase oscillators, stating that spiking can be regarded as oscillation; it cites prior uses of Kuramoto for neural circuits but does not test whether amplitude dynamics, inhibition, or spiking nonlinearities would change the resonance band.
domain assumption A circuit's responsiveness to a periodic stimulus is measured by the Kuramoto order parameter R computed against the forcing phase.
The paper defines R including the forcing as a sixth node and equates high R with entrainment or resonance. This is a reasonable operational definition, but other measures (e.g., spike rates, information transfer) could give different frequency preferences.
domain assumption Typical neural integration times correspond to natural frequencies near 2 Hz.
The paper cites Izhikevich, Dayan and Abbott, and Fairhall for 'characteristic biophysical integration times of a few hundred milliseconds,' which translates to about 1-5 Hz. This is the key biological bridge that links the model to the data band.
ad hoc to paper The operating point is just below the critical coupling (self-organized criticality).
The global coupling is set to K = 0.9 Kc, with a footnote invoking self-organized criticality. This choice is motivated by an analogy, not by measured circuit parameters in any specific species.

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Pith. "Pith review of A universal animal communication tempo resonates with the receiver's brain." pith.science (2026). https://pith.science/paper/3HPJMUXG

@misc{pith2026250821530,
  author       = {Pith},
  title        = {Pith review of: A universal animal communication tempo resonates with the receiver's brain},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3HPJMUXG}},
  note         = {Machine review of arXiv:2508.21530}
}

read the original abstract

During fieldwork in Thailand we observed nearly identical frequencies of co-located flashing fireflies and chirping crickets. Motivated by this, we perform a meta-analysis and show an abundance of evolutionarily distinct species that communicate isochronously at ~0.5-4 Hz, suggesting that this might be a frequency "hotspot." We hypothesize that this timescale may have a universal basis in the biophysics of the receiver's neurons. We test this by demonstrating that small receiver circuits constructed from elements representing typical neurons will be most responsive in the observed frequency range.

Figures

Figures reproduced from arXiv: 2508.21530 by the authors.

**Figure 2.** Schematic of the modeling methodology. Explanation of the questions we address with our modeling approach: we ask about the “inheritance” of frequencies from the individual to the circuit level (and if, and how, it depends on the specifics of the connectivity / architecture), and whether we can construct “neural resonance curves” by forcing our system with an external stimulus (since we measure circuit-level respons… view at source ↗

**Figure 3.** Modeling circuits of neurons. Circuits of Kuramoto oscillators with N = 5 oscillators driven by an external stimulus of 2 Hz. (A) The mean order parameter R (quantifying synchrony) of each circuit (with 10 edges, we ran this for all 1665 possible directed graphs, excluding isomorphic graphs). The data is sorted from the lowest to the highest R. The curve is a mean of 100 curves obtained from 100 realizations. The re… view at source ↗

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Works this paper leans on

46 extracted references · 46 canonical work pages

[13]

Childs, L. M. and Strogatz, S. H. (2008). Stability diagram for the forced kuramoto model. Chaos: An Interdisciplinary Journal of Nonlinear Science , 18(4). Cumin and Unsworth,

work page 2008
[15]

H., Peters, R

Dalziell, A. H., Peters, R. A., Cockburn, A., Dorland, A. D., Maisey, A. C., and Magrath, R. D. (2013). Dance choreography is coordinated with song repertoire in a complex avian display. Current Biology, 23(12):1132–1135. Dayan and Abbott,

work page 2013
[17]

De Arcangelis, L., Perrone-Capano, C., and Herrmann, H. J. (2006). Self-organized criticality model for brain plasticity. Physical review letters , 96(2):028107. D´ eaux et al.,

work page 2006
[18]

C., Piette, T., Gaunet, F., Legou, T., Arnal, L., and Giraud, A.-L

D´ eaux, E. C., Piette, T., Gaunet, F., Legou, T., Arnal, L., and Giraud, A.-L. (2024). Dog–human vocal interactions match dogs’ sensory-motor tuning. PLoS biology, 22(10):e3002789. Fairhall et al.,

work page 2024
[20]

P., Klein, J

Garrigan, P., Ratliff, C. P., Klein, J. M., Sterling, P., Brainard, D. H., and Balasubramanian, V. (2010). Design of a trichromatic cone array. PLoS computational biology , 6(2):e1000677. Greenfield,

work page 2010
[21]

Greenfield, M. D. (2005). Mechanisms and evolution of communal sexual displays in arthropods and anurans. Advances in the Study of Behavior , 35(05):1–62. Hersh et al.,

work page 2005
[25]

Izhikevich, E. M. (2007). Dynamical systems in neuroscience . MIT press, Cambridge. J¨ agers et al., 2021.J¨ agers, P., Wagner, L., Sch¨ utz, R., Mucke, M., Senen, B., Limmon, G. V., Herlitze, S., and Hellinger, J. (2021). Social signaling via bioluminescent blinks determines nearest neighbor distance in schools of flashlight fish anomalops katoptron. Sci...

work page 2007
[30]

Likharev, K. K. (2013). Essential Graduate Physics . Stony Brook University, Stony Brook. Ma et al.,

work page 2013

Show all 46 references

[31]

A., Garber, P

Ma, H., Wang, Z., Han, P., Fan, P., Chapman, C. A., Garber, P. A., and Fan, P. (2024). Small apes adjust rhythms to facilitate song coordination. Current Biology, 34(5):935–945. MacGillavry,

2024
[32]

MacGillavry, T. (2024). Riflebird courtship display plateaus at mean ioi of 0.3 s. personal communication. Martin et al.,

2024
[33]

L., Nguyen, D

Martin, O., Nguyen, C., Sarfati, R., Chowdhury, M., Iuzzolino, M. L., Nguyen, D. M. T., Layer, R. M., and Peleg, O. (2024). Embracing firefly flash pattern variability with data-driven species classification. Scientific Reports, 14(1):3432. MathWorks,

2024
[36]

J., Diesmos, A., Ahmad, N., and Das, I

Ord, T. J., Diesmos, A., Ahmad, N., and Das, I. (2023). Evolutionary loss of complexity in animal signals: cause and consequence. Evolution, 77(3):660–669. Prasertkul et al., ming. Prasertkul, T., Scheiner, A. J., Abrams, D. M., and Amichay, G. (forthcoming). Computer vision-b...

2023
[37]

Raimondi, T., Di Panfilo, G., Pasquali, M., Zarantonello, M., Favaro, L., Savini, T., Gamba, M., and Ravignani, A. (2023). Isochrony and rhythmic interaction in ape duetting. Proceedings of the Royal Society B , 290(1990):20222244. Ratliff et al.,

2023
[38]

P., Borghuis, B

Ratliff, C. P., Borghuis, B. G., Kao, Y.-H., Sterling, P., and Balasubramanian, V. (2010). Retina is structured to process an excess of darkness in natural scenes. Proceedings of the National Academy of Sciences, 107(40):17368–17373. Ravignani and Madison,

2010
[40]

C., Tchernichovski, O., Poeppel, D., and Jacoby, N

Roeske, T. C., Tchernichovski, O., Poeppel, D., and Jacoby, N. (2020). Categorical rhythms are shared between songbirds and humans. Current Biology, 30(18):3544–3555. Rubinov et al.,

2020
[41]

Rubinov, M., Sporns, O., Thivierge, J.-P., and Breakspear, M. (2011). Neurobiologically realistic determinants of self-organized criticality in networks of spiking neurons. PLoS computational biology , 7(6):e1002038. Sakaguchi,

2011
[42]

Sakaguchi, H. (1988). Cooperative phenomena in coupled oscillator systems under external fields. Progress of theoretical physics , 79(1):39–46. Strogatz,

1988
[44]

Strogatz, S. H. (2024). Nonlinear dynamics and chaos with student solutions manual: With applications to physics, biology, chemistry, and engineering, 3rd Edition . CRC press, Boca Raton. Zalta et al.,

2024
[45]

A universal animal communication tempo resonates with the receiver’s brain

Zalta, A., Large, E. W., Sch¨ on, D., and Morillon, B. (2024). Neural dynamics of predictive timing and motor engagement in music listening. Science Advances, 10(10):eadi2525. September 5, 2025 10/12 Supporting Information: “A universal animal communication tempo resonates wit...

2024
[100]

Results obtained from 1 realization (initial seed)

We vary the fraction of input neurons (with 20%, 50% and 80% in separate plots from left to right). Results obtained from 1 realization (initial seed). (A) All-to-all network. (B) A random binary network, with each node assigned 60 random connections to avoid sparsity. . Septe...

2025
[1975]

Kuramoto, Y. (1975). Self-entrainment of a population of coupled non-linear oscillators. In International Symposium on Mathematical Problems in Theoretical Physics: January 23–29, 1975, Kyoto University, Kyoto/Japan, pages 420–422. Springer. Large et al.,

1975
[1981]

E., Case, J

Buck, J., Buck, E., Hanson, F. E., Case, J. F., Mets, L., and Atta, G. J. (1981). Control of flashing in fireflies: Iv. free run pacemaking in a synchronic pteroptyx. Journal of comparative physiology , 144:277–286. Burchardt et al.,

1981
[1988]

Bak, P., Tang, C., and Wiesenfeld, K. (1988). Self-organized criticality. Physical review A , 38(1):364. Breakspear et al.,

1988
[1994]

Large, E. W. and Kolen, J. F. (1994). Resonance and the perception of musical meter. Connection science, 6(2-3):177–208. Large and Snyder,

1994
[1998]

Backwell, P., Jennions, M., Passmore, N., and Christy, J. (1998). Synchronized courtship in fiddler crabs. Nature, 391(6662):31–32. September 5, 2025 7/12 Bak et al.,

1998
[2000]

Strogatz, S. H. (2000). From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators. Physica D: Nonlinear Phenomena , 143(1-4):1–20. Strogatz,

2000
[2001]

L., Lewen, G

Fairhall, A. L., Lewen, G. D., Bialek, W., and de Ruyter van Steveninck, R. R. (2001). Efficiency and ambiguity in an adaptive neural code. Nature, 412(6849):787–792. Garrigan et al.,

2001
[2004]

and Draguhn, A

Buzsaki, G. and Draguhn, A. (2004). Neuronal oscillations in cortical networks. science, 304(5679):1926–1929. Buzs´ aki et al.,

2004
[2005]

and Abbott, L

Dayan, P. and Abbott, L. F. (2005). Theoretical neuroscience: computational and mathematical modeling of neural systems . MIT press, Cambridge. De Arcangelis et al.,

2005
[2006]

Buzsaki, G. (2006). Rhythms of the brain. Buzsaki and Draguhn,

2006
[2007]

and Unsworth, C

Cumin, D. and Unsworth, C. (2007). Generalising the kuramoto model for the study of neuronal synchronisation in the brain. Physica D: Nonlinear Phenomena , 226(2):181–196. Dalziell et al.,

2007
[2008]

M., Faghih, R., Girvan, M., Ott, E., and Platig, J

Antonsen, T. M., Faghih, R., Girvan, M., Ott, E., and Platig, J. (2008). External periodic driving of large systems of globally coupled phase oscillators. Chaos: An Interdisciplinary Journal of Nonlinear Science, 18(3). Backhouse et al.,

2008
[2009]

Large, E. W. and Snyder, J. S. (2009). Pulse and meter as neural resonance. Annals of the New York Academy of Sciences , 1169(1):46–57. Likharev,

2009
[2010]

Breakspear, M., Heitmann, S., and Daffertshofer, A. (2010). Generative models of cortical oscillations: neurobiological implications of the kuramoto model. Frontiers in human neuroscience , 4:190. Buck et al.,

2010
[2011]

G., and Aihara, K

Aihara, I., Takeda, R., Mizumoto, T., Otsuka, T., Takahashi, T., Okuno, H. G., and Aihara, K. (2011). Complex and transitive synchronization in a frustrated system of calling frogs. Physical Review E, 83(3):031913. Antonsen et al.,

2011
[2013]

Buzs´ aki, G., Logothetis, N., and Singer, W. (2013). Scaling brain size, keeping timing: evolutionary preservation of brain rhythms. Neuron, 80(3):751–764. Childs and Strogatz,

2013
[2014]

and Gross, T

Hesse, J. and Gross, T. (2014). Self-organized criticality as a fundamental property of neural systems. Frontiers in systems neuroscience , 8:166. September 5, 2025 8/12 Ito et al.,

2014
[2015]

W., Herrera, J

Large, E. W., Herrera, J. A., and Velasco, M. J. (2015). Neural networks for beat perception in musical rhythm. Frontiers in systems neuroscience , 9:159. Large and Kolen,

2015
[2016]

A., Thompson, N

Murphy, M. A., Thompson, N. L., and Schul, J. (2016). Keeping up with the neighbor: a novel mechanism of call synchrony in neoconocephalus ensiger katydids. Journal of Comparative Physiology A , 202:225–234. Ord et al.,

2016
[2017]

and Madison, G

Ravignani, A. and Madison, G. (2017). The paradox of isochrony in the evolution of human rhythm. Frontiers in psychology , 8:1820. September 5, 2025 9/12 Roeske et al.,

2017
[2019]

S., Norton, P., Behr, O., Scharff, C., and Kn¨ ornschild, M

Burchardt, L. S., Norton, P., Behr, O., Scharff, C., and Kn¨ ornschild, M. (2019). General isochronous rhythm in echolocation calls and social vocalizations of the bat saccopteryx bilineata. Royal Society Open Science , 6(1):181076. Burchardt et al.,

2019
[2020]

MathWorks, T. (2020). Matlab version: 9.13.0 (r2020a). Murphy et al.,

2020
[2021]

S., Picciulin, M., Parmentier, E., and Bolgan, M

Burchardt, L. S., Picciulin, M., Parmentier, E., and Bolgan, M. (2021). A primer on rhythm quantification for fish sounds: a mediterranean case study. Royal Society Open Science , 8(9):210494. Buzsaki,

2021
[2022]

I., Ishida, N., Oshima, K., Magami, K., and Takahashi, H

Ito, Y., Shiramatsu, T. I., Ishida, N., Oshima, K., Magami, K., and Takahashi, H. (2022). Spontaneous beat synchronization in rats: Neural dynamics and motor entrainment. Science Advances, 8(45):eabo7019. Izhikevich,

2022
[2023]

A., Ravignani, A., and Burchardt, L

Hersh, T. A., Ravignani, A., and Burchardt, L. S. (2023). Robust rhythm reporting will advance ecological and evolutionary research. Methods in Ecology and Evolution , 14(6):1398–1407. Hesse and Gross,

2023
[2024]

A., Robinson, B

Backhouse, F., Welbergen, J. A., Robinson, B. W., and Dalziell, A. H. (2024). Performative manipulation of the environment by displaying albert’s lyrebirds. The American Naturalist , 204(2):000–000. Backwell et al.,

2024

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [13]

Childs, L. M. and Strogatz, S. H. (2008). Stability diagram for the forced kuramoto model. Chaos: An Interdisciplinary Journal of Nonlinear Science , 18(4). Cumin and Unsworth,

work page 2008

[2] [15]

H., Peters, R

Dalziell, A. H., Peters, R. A., Cockburn, A., Dorland, A. D., Maisey, A. C., and Magrath, R. D. (2013). Dance choreography is coordinated with song repertoire in a complex avian display. Current Biology, 23(12):1132–1135. Dayan and Abbott,

work page 2013

[3] [17]

De Arcangelis, L., Perrone-Capano, C., and Herrmann, H. J. (2006). Self-organized criticality model for brain plasticity. Physical review letters , 96(2):028107. D´ eaux et al.,

work page 2006

[4] [18]

C., Piette, T., Gaunet, F., Legou, T., Arnal, L., and Giraud, A.-L

D´ eaux, E. C., Piette, T., Gaunet, F., Legou, T., Arnal, L., and Giraud, A.-L. (2024). Dog–human vocal interactions match dogs’ sensory-motor tuning. PLoS biology, 22(10):e3002789. Fairhall et al.,

work page 2024

[5] [20]

P., Klein, J

Garrigan, P., Ratliff, C. P., Klein, J. M., Sterling, P., Brainard, D. H., and Balasubramanian, V. (2010). Design of a trichromatic cone array. PLoS computational biology , 6(2):e1000677. Greenfield,

work page 2010

[6] [21]

Greenfield, M. D. (2005). Mechanisms and evolution of communal sexual displays in arthropods and anurans. Advances in the Study of Behavior , 35(05):1–62. Hersh et al.,

work page 2005

[7] [25]

Izhikevich, E. M. (2007). Dynamical systems in neuroscience . MIT press, Cambridge. J¨ agers et al., 2021.J¨ agers, P., Wagner, L., Sch¨ utz, R., Mucke, M., Senen, B., Limmon, G. V., Herlitze, S., and Hellinger, J. (2021). Social signaling via bioluminescent blinks determines nearest neighbor distance in schools of flashlight fish anomalops katoptron. Sci...

work page 2007

[8] [30]

Likharev, K. K. (2013). Essential Graduate Physics . Stony Brook University, Stony Brook. Ma et al.,

work page 2013