Pith. sign in

REVIEW 2 major objections 4 minor 58 references

Quantifying spike train synchrony and directionality: Measures and Applications

T0 review · 2 major / 4 minor · reviewed 2026-08-04 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A review of parameter-free, time-resolved spike train synchrony measures and two latency-correction algorithms that handle overlapping global events, with the fast extrapolation shift nearly matching simulated annealing.

desk verdict Useful consolidation of the author's own spike-train synchrony measures, but the exposition of the latency-correcting extrapolation in Eq. 33 has a real arithmetic error that needs fixing. read the letter →

arxiv 2510.07140 v2 pith:PMJE77FM submitted 2025-10-08 q-bio.NC physics.data-an

Thomas Kreuz This is my paper · ORCID
classification q-bio.NCphysics.data-an MSC 92C20
keywords spiketrainsynchronyISI-DistanceSPIKE-DistanceSPIKE-SynchronizationSPIKE-Orderlatencycorrectionsynfirepatterntimedifferencematrix
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 review makes the case that a single family of parameter-free, time-resolved measures—the ISI-Distance, SPIKE-Distance, SPIKE-Synchronization, and SPIKE-Order—can capture both the degree and the direction of synchrony in sets of spike trains, and that these measures can serve as the basis for correcting systematic latency differences between spike trains. The central new claim is that two recently proposed latency-correction algorithms succeed even when global spiking events overlap, a regime where naive spike matching produces spurious coincidences. The faster algorithm, which extrapolates the spike time difference matrix from its unaffected inner diagonals, is reported to reach almost the same alignment quality as a simulated-annealing approach at a fraction of the computational cost. If correct, these tools give experimental neuroscientists a way to separate true synchrony from propagation delays and to rank neurons from leader to follower.

What carries the argument

The central object is the spike time difference matrix (STDM), built from pairwise averaged time differences between matched spikes after adaptive coincidence detection, together with its symmetric cost matrix. The adaptive coincidence window, half the minimum of the four neighboring interspike intervals, is what makes the whole framework parameter-free: each spike is matched to at most one spike in another train, and coincidences are defined locally. For overlap, the extrapolation formula δ^{(n,m)} = Σ_k [δ^{(n,k)} + δ^{(k,m)}] replaces overlap-corrupted outer matrix entries by sums of inner entries, assuming that latencies are additive.

What would settle it

Generate a set of spike trains with overlapping global events in which latencies are deliberately non-additive, e.g., propagation delays that differ between events or depend on event index; run the Extrapolation direct shift and reduced-matrix simulated annealing. If the extrapolation end cost is substantially worse than simulated annealing's on a dataset where the two were reported to tie, the transitivity assumption is falsified in that regime.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that synchrony and directionality in spike trains can be quantified without any time-scale parameter and in a time-resolved way, and that the same framework extends naturally from measurement to correction. The paper argues that SPIKE-Synchronization, built on adaptive coincidence detection, yields an unambiguous 0-or-1 spike matching that is local and scale-free; SPIKE-Order and Spike Train Order turn this matching into leader-follower assignments and a Synfire Indicator. The newer latency-correction algorithms then use the spike time difference matrix derived from matched spikes, and the paper's key technical claim is that when global events overl

Load-bearing premise

The extrapolation step assumes that the time difference between any two spike trains is the sum of the differences through an intermediate spike train, which is exact only when every spike train differs from every other by a single global constant delay.

Editorial extensions

If this is right

  • If the measures behave as stated, researchers can compare synchrony across datasets without choosing a time scale, and can localize synchrony changes in time.
  • If the extrapolation direct shift matches simulated annealing, latency correction becomes feasible for very large spike train sets where annealing's high computational cost is prohibitive.
  • If the iterative rematching scheme works, then overlapping global events, previously a failure mode, can be disentangled and aligned close to the true latencies.
  • The Synfire Indicator and the sorted leader-follower order provide a quantitative test for synfire-like propagation in any discrete event data.

Reading between the lines

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

  • The transitivity assumption behind the extrapolation is likely to break down when different events have different propagation speeds or when spikes are missing; a natural extension would be to test the algorithm against ground-truth non-additive latency models and measure how much the end cost degrades.
  • The paper describes the framework as universal for discrete datasets, so a testable extension is to apply the same latency-correction machinery to climate, communication, or social-event time series and check whether alignment improves known event correspondences.
  • The paper notes an open interplay between spike train sorting and latency correction; an implication is that the two operations should perhaps be iterated jointly rather than sequentially, since changing the order changes which spike pairs are used to estimate latencies.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 4 minor

Summary. This article is a review of the author's SPIKE-family measures of spike train synchrony and directionality. The first part introduces the ISI-Distance and SPIKE-Distance, their time-resolved profiles, and multivariate extensions. The second part describes SPIKE-Synchronization and the Spike Train Order/SPIKE-Order framework, including the Synfire Indicator and the leader-to-follower sorting procedure. The third part turns to latency correction, presenting direct-shift and simulated-annealing algorithms for non-overlapping events and the reduced-matrix Extrapolation and reduced-matrix simulated annealing for overlapping events. The presentation is unified and illustrated on synthetic data, with examples of real-data applications. The central technical claims are the definitions and normalization properties of the measures and the effectiveness of the two latency-correction algorithms.

Significance. SPIKE-Synchronization, SPIKE-Distance, and related measures are widely used, and a single self-contained account of the definitions, profiles, and algorithmic extensions has practical value. The manuscript benefits from explicit formulas for each measure, references to open-source implementations (SPIKY, PySpike, cSPIKE), and a clear discussion of leader-follower sorting. The latency-correction section is the least standard part, but its presentation is undermined by the error in Eq. (33); until that is corrected, the algorithmic contribution cannot be evaluated from the manuscript alone. If the formula is repaired, the paper would be a useful reference for practitioners.

major comments (2)
  1. [§4.2, Eq. (33)] The extrapolation formula is not correct as printed. Under the additive-latency model δ(a,b)=L_a−L_b, each summand δ(n,k)+δ(k,m) equals δ(n,m), so the sum over k=m+1,...,n−1 returns (n−m−1)·δ(n,m), not δ(n,m). The condition 'm−n>d' is also inconsistent with the summation bounds: for n>m one would need n−m>d; for n<m the sum over k=m+1,...,n−1 is empty. The formula should be normalized, e.g. δ(n,m)= [1/(n−m−1)] Σ_k [δ(n,k)+δ(k,m)] with n−m>d, or the intended convention should be stated explicitly. As written, the Extrapolation direct shift is not fully specified, and the claim in §4.2 that it achieves almost the same performance as reduced-matrix simulated annealing cannot be checked.
  2. [§4.2, Eq. (33)] The extrapolation relies on transitivity of pairwise spike time differences, which is exact only for global additive latencies. Real data with jitter, missing or extra spikes, or nonstationary propagation violate transitivity, and the outer entries being extrapolated are precisely the ones most affected by overlap. The manuscript should state this limitation explicitly and should provide evidence on how the extrapolation degrades when the assumption is violated, e.g., by comparing extrapolated outer entries with direct overlap-free estimates in the simulations of [14]. This is not a reason to abandon the method, but it is necessary for judging the 'almost the same performance' claim.
minor comments (4)
  1. [§3.2, Eq. (17)] The summation in Eq. (17) is written as Σ_{m,n}, which is ambiguous and would include both n and m. It should be Σ_{m≠n} or Σ_{m=1,m≠n}^N to match the text: averaging over all N−1 bivariate coincidence indicators involving spike i.
  2. [§3.3, Eqs. (24) and (27)] The equivalence of the two definitions of the Synfire Indicator is asserted but not shown. A one-line rearrangement of the double sum over spike trains would make the equality transparent.
  3. [Throughout] Several typos should be corrected: 'propogates' in the abstract, 'reduxed matrix' in §4.2, 'electrophyiological' in §2.3, and 'next on' in the description of Fig. 8b.
  4. [§4.2] The iterative scheme in Fig. 9 is described qualitatively. Specifying the stopping criterion and the parameter choices (stop diagonal d, annealing temperature/cooling schedule) would improve reproducibility, although the reader is referred to [14] for details.

Circularity Check

0 steps flagged · score 2.0 of 10

No load-bearing circularity; the paper is a self-review of the author's own measures and algorithms, but the central claims do not reduce by construction to their inputs.

full rationale

This is a review/application paper rather than a derivation. The synchrony measures are defined constructively (e.g., Eqs. 1-19), and their stated properties such as SPIKE-Synchronization being 0 iff no coincidences follow from the definitions, not from fitting or from circular prediction. The latency-correction algorithms are presented by reviewing the author's prior work [13,14], including the claim that the Extrapolation direct shift achieves almost the same performance as reduced matrix simulated annealing; this is an empirical report on artificial and real data, not a first-principles prediction, and no fitted parameter is renamed as an independent outcome. The transitivity extrapolation in Eq. 33 is the closest thing to a load-bearing step, but it is an explicitly assumed modeling property for the overlap case; the apparent missing normalization factor (the unnormalized sum over k inflates delta by n-m-1) is an algebraic/correctness or typographical concern, not circular equivalence. The abundant self-citations are normal in a review of the author's own measures and are counterbalanced by many independent applications and public software packages (SPIKY, PySpike, cSPIKE), so they are not load-bearing in the sense required for a circularity finding.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The synchrony measures themselves are deliberately parameter-free. The only user-set parameter in the new algorithms is the stop diagonal d, with simulated annealing parameters from [13]. The mathematical content rests on standard assumptions about spike time order and on domain assumptions about coincidence detection and transitivity of latencies. No new physical or conceptual entities are introduced.

free parameters (2)
  • stop diagonal d
    User-chosen parameter in latency correction (Section 4.2) that controls how much of the spike-time difference matrix is trusted. It is not fit to data but affects the extrapolation and simulated annealing behavior.
  • simulated annealing temperature and cooling schedule
    Algorithmic parameters from [13], not specified in this paper, that affect convergence of the latency-correction optimization. They are hand-selected rather than data-fitted.
assumptions (4)
  • standard math Spike times are finite strictly increasing sequences within an interval [0,T].
    Needed for the definitions of previous/following spike and interspike intervals in Section 2. Boundary cases are deferred to [15].
  • domain assumption Adaptive coincidence detection with the half-minimum neighbor interval yields an unambiguous spike matching (Eqs. 15-16).
    The paper asserts that the minimum function and '<' guarantee at most one coincident spike per spike. This depends on local coincidence windows not overlapping; ties and boundary cases can undermine it.
  • ad hoc to paper Pairwise spike time differences are transitive, so outer matrix entries can be extrapolated via Eq. 33.
    This is load-bearing for the Extrapolation latency-correction algorithm. It assumes latencies behave as global shifts, which is exactly what the overlap problem undermines.
  • domain assumption Maximizing the Synfire Indicator over spike-train permutations yields a meaningful leader-follower order (Eq. 28).
    Assumes a consistent global ordering exists in the data. Real recordings with mixed propagation patterns may not have a single global order.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Quantifying spike train synchrony and directionality: Measures and Applications." pith.science (2026). https://pith.science/paper/PMJE77FM

@misc{pith2026251007140,
  author       = {Pith},
  title        = {Pith review of: Quantifying spike train synchrony and directionality: Measures and Applications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PMJE77FM}},
  note         = {Machine review of arXiv:2510.07140}
}
read the original abstract

By introducing the twin concepts of reliability and precision along with the corresponding measures, Mainen and Sejnowski's seminal 1995 paper "Reliability of spike timing in neocortical neurons" (Mainen and Sejnowski, 1995) paved the way for a new kind of quantitative spike train analysis. In subsequent years a host of new methods was introduced that measured both the synchrony among neuronal spike trains and the directional component, e.g. how activity propogates between neurons. This development culminated with a new class of measures that are both time scale independent and time resolved. These include the two spike train distances ISI- and SPIKE-Distance as well as the coincidence detector SPIKE-Synchronization and its directional companion SPIKE-Order. This article will not only review all of these measures but also include two recently proposed algorithms for latency correction which build on SPIKE-order and aim to optimize the spike time alignment of sparse spike trains with well-defined global spiking events. For the sake of clarity, all these methods will be illustrated on artificially generated data but in each case exemplary applications to real neuronal data will be described as well.

Figures

Figures reproduced from arXiv: 2510.07140 by the authors.

Figure 1
Figure 1. Schematic illustration of how the ISI-Distance [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Artificial example dataset of 50 spike trains (A) and the correspond [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Instantaneous clustering for artificially generated spike trains (A, C) whose clustering behavior changes every 500 ms: from four di [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: A. Motivation for adaptive coincidence detection. Depending on [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Using the Spike Train Order framework to sort spike trains from [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Spike Train Order for an artificial dataset consisting of 6 spike trains arranged into nine reliable events. The first two events are in order, the last four in [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Latency correction performed via first row direct shift on a regularly spaced synfire chain with both missing and extra spikes as well as some jitter. A. [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Illustrating the effect of event overlap using a perfect synfire chain with three global events without (subpanel a) and with (subpanel b) overlap. Both subpanels follow the same structure: A. Spike train sets. Coincidence windows of the i-th spike in the (n)-th spike …
Figure 9
Figure 9. Figure 9: Iterative scheme improves on a simple direct shift when applied to a simulated synfire chain with overlapping incomplete events and some background [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

58 extracted references · 33 canonical work pages

  1. [14]

    Mariani, F

    A. Mariani, F. Senocrate, J. Mikiel- Hunter, D. McAlpine, B. Beiderbeck, M. Pecka, K. Lin, T. Kreuz, Latency cor- rection in sparse neuronal spike trains, J Neurosci Methods 416 (2025) 110378. doi:https://doi.org/10.1016/j.jneumeth.2025.110378

    arXiv 2025

  2. [1]

    Mainen, T

    Z. Mainen, T. J. Sejnowski, Relia- bility of spike timing in neocortical neurons, Science 268 (1995) 1503. doi:https://doi.org/10.1126/science.7770778

    work page doi:10.1126/science.7770778 1995

  3. [2]

    Chicharro, T

    D. Chicharro, T. Kreuz, R. G. Andrze- jak, What can spike train distances tell us about the neural code?, J Neu- rosci Methods 199 (2011) 146–165. doi:https://doi.org/10.1016/j.jneumeth.2011.05.002

    work page doi:10.1016/j.jneumeth.2011.05.002 2011

  4. [3]

    Quian Quiroga, S

    R. Quian Quiroga, S. Panzeri, Princi- ples of neural coding, CRC Taylor and Francis, Boca Raton, FL, USA, 2013. doi:https://doi.org/10.1201/b14756

    work page doi:10.1201/b14756 2013

  5. [4]

    J. D. Victor, K. P. Purpura, Nature and precision of temporal coding in visual cortex: A metric- space analysis, J Neurophysiol 76 (1996) 1310. doi:https://doi.org/10.1152/jn.1996.76.2.1310

    doi:10.1152/jn.1996.76.2.1310 1996

  6. [5]

    M. C. W. van Rossum, A novel spike distance, Neural Comput 13 (2001) 751. doi:https://doi.org/10.1162/089976601300014321

    work page doi:10.1162/089976601300014321 2001

  7. [6]

    J. Haas, J. White, Frequency selectivity of layer ii stellate cells in the medial entorhi- nal cortex, J. Neurophysiol. 88 (2002) 2422. doi:https://doi.org/10.1152/jn.00598.2002

    arXiv 2002

  8. [7]

    Schreiber, J

    S. Schreiber, J. M. Fellous, J. H. Whitmer, P. H. E. Tiesinga, T. J. Sejnowski, A new correlation-based measure of spike tim- ing reliability, Neurocomputing 52 (2003)

    2003

Show all 58 references
  1. [8]

    J. D. Hunter, G. Milton, Amplitude and frequency dependence of spike tim- ing: implications for dynamic regula- tion, J Neurophysiol 90 (2003) 387. doi:https://doi.org/10.1152/jn.00074.2003. 2http://mariomulansky.github.io/PySpike 3http://www.thomaskreuz.org/source-codes/cSPIKE 15

    2003

  2. [9]

    Kreuz, J

    T. Kreuz, J. S. Haas, A. Morelli, H. D. I. Abarbanel, A. Politi, Measuring spike train synchrony, J Neurosci Methods 165 (2007) 151. doi:https://doi.org/10.1016/j.jneumeth.2007.05.031

    2007 doi

  3. [10]

    Kreuz, D

    T. Kreuz, D. Chicharro, C. Houghton, R. G. Andrzejak, F. Mormann, Monitoring spike train synchrony, J Neurophysiol 109 (2013) 1457. doi:https://doi.org/10.1152/jn.00873.2012

    2013

  4. [11]

    Kreuz, M

    T. Kreuz, M. Mulansky, N. Bozanic, SPIKY: A graphical user interface for monitoring spike train synchrony, J Neurophysiol 113 (2015) 3432. doi:https://doi.org/10.1152/jn.00848.2014

    2015

  5. [12]

    Kreuz, E

    T. Kreuz, E. Satuvuori, M. Pofahl, M. Mulan- sky, Leaders and followers: Quantifying consis- tency in spatio-temporal propagation patterns, New Journal of Physics 19 (2017) 043028. doi:https://doi.org/10.1088/1367-2630/aa68c3

    2017 doi

  6. [13]

    Kreuz, F

    T. Kreuz, F. Senocrate, G. Cecchini, C. Checcucci, A. L. A. Mascaro, E. Conti, A. Scaglione, F. S. Pavone, Latency cor- rection in sparse neuronal spike trains, J Neurosci Methods 381 (2022) 109703. doi:https://doi.org/10.1016/j.jneumeth.2022.109703

    2022

  7. [15]

    Satuvuori, M

    E. Satuvuori, M. Mulansky, N. Bozanic, I. Malvestio, F. Zeldenrust, K. Lenk, T. Kreuz, Measures of spike train syn- chrony for data with multiple time scales, J Neurosci Methods 287 (2017) 25–38. doi:https://doi.org/10.1016/j.jneumeth.2017.05.028

    2017 doi

  8. [16]

    Kreuz, D

    T. Kreuz, D. Chicharro, R. G. Andrzejak, J. S. Haas, H. D. I. Abarbanel, Mea- suring multiple spike train synchrony, J Neurosci Methods 183 (2009) 287. doi:https://doi.org/10.1016/j.jneumeth.2009.06.039

    2009 doi

  9. [17]

    Satuvuori, I

    E. Satuvuori, I. Malvestio, T. Kreuz, Measures of spike train synchrony and directionality, in: G. Naldi, T. Nieus (Eds.), Mathematical and Theoretical Neuroscience, V ol. 24 of Springer INdAM Series, Springer Cham, Berlin, 2017, p. 201. doi:https://doi.org/10.1007/978-3-319- 68297-6

    2017 doi

  10. [18]

    Satuvuori, T

    E. Satuvuori, T. Kreuz, Which spike train distance is most suitable for dis- tinguishing rate and temporal coding?, J Neurosci Methods 299 (2018) 22. doi:https://doi.org/10.1016/j.jneumeth.2018.02.009

    2018 doi

  11. [19]

    W. M. Usrey, R. C. Reid, Syn- chronous activity in the visual system., Annu. Rev. Physiol. 61 (1999) 435. doi:https://dx.doi.org/10.1146/annurev.physiol.61.1.435

    1999 doi

  12. [20]

    P. H. E. Tiesinga, J. M. Fellous, T. J. Sejnowski, Regulation of spike timing in visual cortical cir- cuits, Nature Reviews Neuroscience 9 (2008)

    2008

  13. [21]

    Shlens, F

    J. Shlens, F. Rieke, E. L. Chichilnisky, Synchronized firing in the retina, Curr Opin Neurobiol 18 (2008) 396. doi:https://doi.org/10.1016/j.conb.2008.09.010

    2008 doi

  14. [22]

    Kreuz, D

    T. Kreuz, D. Chicharro, M. Greschner, R. G. Andrzejak, Time-resolved and time- scale adaptive measures of spike train syn- chrony, J Neurosci Methods 195 (2011) 92. doi:https://doi.org/10.1016/j.jneumeth.2010.11.020

    2011 doi

  15. [23]

    Mulansky, N

    M. Mulansky, N. Bozanic, A. Sburlea, T. Kreuz, A guide to time-resolved and parameter-free measures of spike train synchrony, 2015 International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP) (2015) 1– 8doi:https://doi.org/10.1109/EBCCSP.2015.7300693

    2015

  16. [24]

    Doorn, M

    N. Doorn, M. J. van Putten, M. Frega, Au- tomated inference of disease mechanisms in patient-hipsc-derived neuronal networks, Communications Biology 8 (1) (2025) 768. doi:https://doi.org/10.1038/s42003-025-08209- 2

    2025 doi

  17. [25]

    Moradi, M

    F. Moradi, M. van den Berg, M. Mirje- breili, L. Kosten, M. Verhoye, M. Amiri, G. A. Keliris, Early classification of alzheimer’s disease phenotype based on hippocampal electrophysiology in the 16 tgf344-ad rat model, iScience 26 (8) (2023). doi:https://doi.org/10.1016/j.isci....

    2023

  18. [26]

    Melanitis, K

    N. Melanitis, K. S. Nikita, Biologically- inspired image processing in computa- tional retina models, Computers in Bi- ology and Medicine 113 (2019) 103399. doi:https://doi.org/10.1016/j.compbiomed.2019.103399

    2019

  19. [27]

    N. Lama, A. Hargreaves, B. Stevens, T. M. McGinnity, Spike train synchrony anal- ysis of neuronal cultures, in: 2018 In- ternational Joint Conference on Neural Networks (IJCNN), IEEE, 2018, pp. 1–8. doi:https://doi.org/10.1109/IJCNN.2018.8489728

    2018

  20. [28]

    L.-H. N. Lee, C. Y . Ngan, C.-K. Yang, R.- W. Wang, H.-J. Lai, C.-H. Chen, Y .-C. Yang, C.-C. Kuo, Motor cortex stimulation amelio- rates parkinsonian locomotor deficits: effec- tual and mechanistic differences from subthala- mic modulation, npj Parkinson’s Disease 11 (1) (202...

    2025 doi

  21. [29]

    Goshi, D

    N. Goshi, D. Lam, C. Bogguri, V . K. George, A. Sebastian, J. Cadena, N. F. Leon, N. R. Hum, D. R. Weilhammer, N. O. Fischer, et al., Direct effects of prolonged tnf-αand il-6 exposure on neural activity in human ipsc- derived neuron-astrocyte co-cultures, Frontiers in Cellula...

    2025

  22. [30]

    Jouty, G

    J. Jouty, G. Hilgen, E. Sernagor, M. H. Hennig, Non-parametric physiological classification of retinal ganglion cells in the mouse retina, Fron- tiers in Cellular Neuroscience 12 (2018) 481. doi:https://doi.org/10.3389/fncel.2018.00481

    2018

  23. [31]

    Nishizono, N

    R. Nishizono, N. Saijo, M. Kashino, Highly reproducible eyeblink timing during for- mula car driving, iScience 26 (6) (2023). doi:https://doi.org/10.1016/j.isci.2023.106803

    2023

  24. [32]

    D. W. Gauthier, N. James, B. D. Auer- bach, Altered auditory feature discrimina- tion in a rat model of fragile x syndrome, PLoS Biology 23 (7) (2025) e3003248. doi:https://doi.org/10.1101/2025.02.18.638956

    2025 doi

  25. [33]

    J. C. Nocon, H. J. Gritton, N. M. James, R. A. Mount, Z. Qu, X. Han, K. Sen, Par- valbumin neurons enhance temporal coding and reduce cortical noise in complex auditory scenes, Communications Biology 6 (1) (2023)

    2023

  26. [34]

    Quian Quiroga, T

    R. Quian Quiroga, T. Kreuz, P. Grassberger, Event synchronization: A simple and fast method to measure synchronicity and time delay patterns, Phys Rev E 66 (2002) 041904. doi:https://doi.org/10.1103/PhysRevE.66.041904

    2002 doi

  27. [35]

    K. Shan, C. Tian, Z. Zheng, M. Zheng, K. Xu, Short-term plasticity promotes synchronization of coupled chaotic oscillators in excitatory– inhibitory networks, Chaos: An Interdisci- plinary Journal of Nonlinear Science 35 (7) (2025). doi:https://doi.org/10.1063/5.0243837

    2025 doi

  28. [36]

    Rostro-Gonzalez, E

    H. Rostro-Gonzalez, E. I. Guerra-Hernandez, P. Batres-Mendoza, A. A. Garcia-Granada, M. Cano-Lara, A. Espinal, Enhancing legged robot locomotion through smooth transitions using spiking central pattern generators, Biomimetics 10 (6) (2025) 381. doi:https://doi.org/10.3390/biom...

    2025 doi

  29. [37]

    X. Bai, C. Yu, J. Zhai, Topological data analysis of the firings of a network of stochastic spiking neurons, Frontiers in Neural Circuits 17 (2024) 1308629. doi:https://doi.org/10.3389/fncir.2023.130862

    2024

  30. [38]

    Seifert, P

    M. Seifert, P. A. Roberts, G. Kafetzis, D. Osorio, T. Baden, Birds multiplex spectral and temporal visual informa- tion via retinal on-and off-channels, Na- ture Communications 14 (1) (2023) 5308. doi:https://doi.org/10.1038/s41467-023-41032- z

    2023 doi

  31. [39]

    S. B. Tomlinson, J. N. Wong, E. C. Conrad, B. C. Kennedy, E. D. Marsh, Reproducibility of interictal spike propagation in children with re- fractory epilepsy, Epilepsia 60 (5) (2019) 898–

    2019

  32. [40]

    Cecchini, A

    G. Cecchini, A. Scaglione, A. L. Alle- gra Mascaro, C. Checcucci, E. Conti, I. Adam, D. Fanelli, R. Livi, F. S. Pavone, 17 T. Kreuz, Cortical propagation tracks func- tional recovery after stroke, PLoS Com- putational Biology 17 (5) (2021) e1008963. doi:https://doi.org/10.1371...

    2021 doi

  33. [41]

    Beiderbeck, Reading between the lines of the duplex theory, Ph.D

    B. Beiderbeck, Reading between the lines of the duplex theory, Ph.D. thesis, Graduate School of Systemic Neuro- sciences, Ludwig-Maximilians-Universität München (LMU), Germany (2022). doi:https://doi.org/10.5282/edoc.30131

    2022 doi

  34. [42]

    Sihn, S.-P

    D. Sihn, S.-P. Kim, A spike train dis- tance robust to firing rate changes based on the earth mover’s distance, Frontiers in Computational Neuroscience 13 (2019) 82. doi:https://doi.org/10.3389/fncom.2019.00082

    2019

  35. [43]

    M. G. Leguia, R. G. Andrzejak, C. Rummel, J. M. Fan, E. A. Mirro, T. K. Tcheng, V . R. Rao, M. O. Baud, Seizure cycles in focal epilepsy, JAMA Neurology 78 (4) (2021) 454–463. doi:https://doi.org/10.1001/jamaneurol.2020.5370

    2021

  36. [44]

    Schröter, F

    M. Schröter, F. Cardes, C.-V . H. Bui, L. D. Dodi, T. Gänswein, J. Bartram, L. Sadi- raj, P. Hornauer, S. Kumar, M. Pascual- Garcia, et al., Advances in large-scale electrophysiology with high-density micro- electrode arrays, Lab on a Chip (2025). doi:https://doi.org/10.1039/d...

    2025 doi

  37. [45]

    Kumar, S

    A. Kumar, S. Rotter, A. Aertsen, Spiking ac- tivity propagation in neuronal networks: rec- onciling different perspectives on neural cod- ing, Nature Rev Neurosci 11 (2010) 615–627. doi:https://doi.org/10.1038/nrn2886

    2010 doi

  38. [46]

    Levakova, M

    M. Levakova, M. Tamborrino, S. Ditlevsen, P. Lansky, A review of the methods for neuronal response latency estima- tion, Biosystems 136 (2015) 23–34. doi:https://doi.org/10.1016/j.biosystems.2015.04.008

    2015 doi

  39. [47]

    A. Y . Sun, Y . Xia, T. G. Caldwell, Z. Hao, Patterns of precipitation and soil moisture extremes in Texas, US: A complex network analysis, Advances in Water Resources 112 (2018) 203–213. doi:https://doi.org/10.1016/j.advwatres.2017.12.019

    2018 doi

  40. [48]

    F. R. Conticello, F. Cioffi, U. Lall, B. Merz, Synchronization and delay between cir- culation patterns and high streamflow events in Germany, Water Resources Re- search 56 (4) (2020) e2019WR025598. doi:https://doi.org/10.1029/2019WR025598

    2020 doi

  41. [49]

    Mwaffo, J

    V . Mwaffo, J. Keshavan, T. L. Hedrick, S. Humbert, Detecting intermittent switch- ing leadership in coupled dynamical sys- tems, Scientific reports 8 (1) (2018) 10338. doi:https://doi.org/10.1038/s41598-018-28285- 1

    2018 doi

  42. [50]

    Varni, G

    G. Varni, G. V olpe, A. Camurri, A sys- tem for real-time multimodal analysis of nonverbal affective social interac- tion in user-centric media, IEEE Trans- actions on Multimedia 12 (2010) 576. doi:https://doi.org/10.1109/TMM.2010.2052592

    2010

  43. [51]

    L. Wang, G. Tan, C. Zang, Identifying the spatiotemporal organization of high-traffic events in a mobile communication system us- ing event synchronization and complex net- works, Chaos: An Interdisciplinary Jour- nal of Nonlinear Science 32 (9) (2022). doi:https://doi.org/10....

    2022 doi

  44. [52]

    Grabow, J

    C. Grabow, J. Macinko, D. Silver, M. Por- firi, Detecting causality in policy diffusion processes, Chaos: An Interdisciplinary Jour- nal of Nonlinear Science 26 (8) (2016). doi:https://doi.org/10.1063/1.4961067

    2016 doi

  45. [53]

    Mulansky, T

    M. Mulansky, T. Kreuz, Pyspike - A python library for analyzing spike train synchrony, Software X 5 (2016) 183. doi:https://doi.org/10.1016/j.softx.2016.07.006

    2016 doi

  46. [54]

    Satuvuori, M

    E. Satuvuori, M. Mulansky, A. Daffertshoder, T. Kreuz, Using spike train distances to identify the most discriminative neuronal subpopu- lation, J Neurosci Methods 308 (2018) 354. doi:https://doi.org/10.1016/j.jneumeth.2018.09.008. 18

    2018 doi

  47. [97]

    doi:https://doi.org/10.1038/nrn2315

    doi

  48. [751]

    doi:https://doi.org/10.1038/s42003-023- 05126-0

    doi

  49. [910]

    doi:https://doi.org/10.1111/epi.14720

    doi

  50. [925]

    doi:https://doi.org/10.1016/S0925- 2312(02)00838-X

    doi

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.