Articles that apply ISI, SPIKE, SPIKE-Synchro or SPIKE-order

[78] Satuvuori E, Mulansky M, Daffertshofer A, Kreuz T:
Using spike train distances to identify the most discriminative neuronal subpopulation
Submitted to JNeurosci Methods, already available at the arXiv [PDF] (2018).                        (SPIKE)

[77] Kreuz T, Houghton C, Victor JD:
Spike Train Distance
Encycl Comp Neurosci (submitted, 2018).                                           (ISI,SPIKE, SPIKE-Synchro)

[76] Gardella C, Marre O, Mora T:
Blindfold learning of an accurate neural metric.
Proceedings of the National Academy of Sciences. 201718710 (2018).           (SPIKE, SPIKE-Synchro)

Satuvuori E, Kreuz T:
Which spike train distance is most suitable for distinguishing rate and temporal coding?
JNeurosci Methods 299, 22 [PDF] and arXiv [PDF] (2018).                                            (ISI, SPIKE)

[74] Ciba M, Isomura T, Jimbo Y, Bahmer A, Thielemann C:
Spike-contrast: A novel time scale independent and multivariate measure of spike train synchrony
JNeurosci Methods 293, 136 (2018).                                                                               (SPIKE)

[73] Yi Z, Zhang Y:
A spike train distance-based method to evaluate the response of mechanoreceptive afferents.
Neural Computing and Applications. 1-12 (2018).                                                        (ISI, SPIKE)

[72] Williams MJ, Whitaker RM, Allen SM:
There and back again: Detecting regularity in human encounter communities.
IEEE Transactions on Mobile Computing. 16:1744 (2017).                                                        (ISI)

Sun AY, Xia Y, Caldwell T, Hao Z:
Patterns of Precipitation and Soil Moisture Extremes in Texas, US: A Complex Network Analysis. Advances in Water Resources. 112, 203 (2017).                                                   (SPIKE-Synchro)

Aguirre LA, Portes LL, Letellier C:
Observability and synchronization of neuron models.
Chaos: An Interdisciplinary Journal of Nonlinear Science. 27(10):103103 (2017).                    (SPIKE)

[69] Zhu J, Liu X:
Measuring spike timing distance in the Hindmarsh–Rose neurons
Cogn Neurodyn. (2017).                                         (ISI)

[68] Madar AD, Ewell LA, Jones MV:
Pattern separation of spike trains by individual granule cells of the dentate gyrus.
bioRxiv. 107706 (2017).                                                                                                (SPIKE)

Malvestio I, Kreuz T, Andrzejak RG:
Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains
Physical Review E 96, 022203 [PDF] (2017).                                                              (ISI, SPIKE)

[66] Qi D, Xiao Z, Liu S, Jiao Y:
Spike Trains Synchrony with Changed Neuronal Networks Parameters in a Hippocampus CA3 Small-World Network Model.
Information Science and Control Engineering Proc. 1721 (2017)                                                (ISI)

[65] Palazzolo G, Moroni M, Soloperto A, Aletti G, Naldi G, Vassalli M, Nieus T, Difato F:
Fast wide-volume functional imaging of engineered in vitro brain tissues.
Scientific Reports 7 (2017).                                                                               (SPIKE-Synchro)

[64] Kreuz T, Satuvuori E, Mulansky M:
Scholarpedia, 12(7):42441 (2017).                                                 (SPIKE-Synchro, SPIKE-order)

[63] Satuvuori E, Mulansky M, Bozanic N, Malvestio I, Zeldenrust F, Lenk K, Kreuz T:
Measures of spike train synchrony for data with multiple time-scales
JNeurosci Methods 287, 25 [PDF] and arXiv [PDF] (2017).
                                                       (Introduces adaptive versions of ISI, SPIKE, SPIKE-Synchro)

[62] Kreuz T, Satuvuori E, Pofahl M, Mulansky M:
Leaders and followers: Quantifying consistency in spatio-temporal propagation patterns
New J. Phys., 19, 043028 [PDF] and arXiv [PDF ] (2017).    (SPIKE-Synchro, introduces SPIKE-order)

Yi Z, Zhang Y:
Recognizing tactile surface roughness with a biomimetic fingertip: A soft neuromorphic approach. Neurocomputing 244, 102 (2017)                                                                             (ISI, SPIKE)

[60] Kuroda K, Hasegawa M:
Method for Estimating Neural Network Topology Based on SPIKE-Distance
LNCS 9886, 91 (2016).                                                                                                  (SPIKE)

[59] Mulansky M, Kreuz T:
PySpike - A Python library for analyzing spike train synchrony
Software X (in press) and arXiv [PDF] (2016)    (Python source codes for ISI, SPIKE, SPIKE-Synchro)

[58] Zapata-Fonseca L, Dotov D, Fossion R, Froese T:
Time-Series Analysis of Embodied Interaction: Movement Variability and Complexity Matching As Dyadic Properties.
Frontiers in Psychology, 7 (2016)                                                                                    (SPIKE)

[57] Koutsou A, Kanev J, Economidou M, Christodoulou C:
Integrator or coincidence detector---what shapes the relation of stimulus synchrony and the operational mode of a neuron?
Mathematical Biosciences and Engineering 13,521 (2016)                                                    (SPIKE)

Espinal A, Rostro-Gonzalez H, Carpio M, Guerra-Hernandez EI, Ornelas-Rodriguez M, Puga-Soberanes HJ, Sotelo-Figuero MA, Melin P:
Quadrupedal robot locomotion: a biologically inspired approach and its hardware implementation
Preprint (2016)                                                                                                            (SPIKE)

[55] Vlachos I, Deniz T, Aertsen A, Kumar A:
Recovery of dynamics and function in spiking neural networks with closed-loop control
PLoS Comput Biol 12.2, e1004720 (2016)                                                                        (SPIKE)

[54] Dura-Bernal S, Li K, Neymotin SA, Francis JT, Principe, JC, Lytton WW:
Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm
Frontiers in Neurocience 10:28 (2016)                                                      (SPIKE, SPIKE-Synchro)

[53] Rodrigues AC, Cerdeira HA, Machado BS:
The influence of hubs in the structure of a neuronal network during an epileptic seizure
Eur. Phys. J. Special Topics 225, 75 (2016)                                                                       (SPIKE)

[52] Chen YL, Yu LC, Chen Y:
Reliability of weak signals detection in neurons with noise
Sci China Tech Sci 59, 411 (2016)                                                                                       (ISI)

[51] Qu J, Wang R, Du Y, Yan C:
An Improved Method of Measuring Multiple Spike Train Synchrony.
Ch. 105, R. Wang and X. Pan (eds.), Advances in Cognitive Neurodynamics (V), Springer Science+Business Media Singapore (2016)                                                                            (ISI)

[50] Mulansky M, Bozanic N, Sburlea A, Kreuz T:
A guide to time-resolved and parameter-free measures of spike train synchrony.
IEEE Proceeding on Event-based Control, Communication, and Signal Processing (EBCCSP), 1-8 and arXiv [PDF] (2015)                              (overview and math. properties ISI, SPIKE, SPIKE-Synchro)
[49] Kreuz T, Mulansky M, Bozanic N:
SPIKY: A graphical user interface for monitoring spike train synchrony.
JNeurophysiol 113, 3432 (2015) [PDF]                                       (introduces SPIKE-Synchro, SPIKY)

[48] Hino H, Takano K, Murata N:
mmpp: A Package for Calculating Similarity and Distance Metrics for Simple and Marked Temporal Point Processes.
R Journal 7, 237 (2015)                                                                           (R source codes for ISI)

[47] Bockhorst T, Homberg U:
Amplitude and dynamics of polarization-plane signaling in the central complex of the locust brain.
Journal of Neurophysiology 113, 3291 (2015)                                                        (Variation of ISI)

[46] Takano K, Hino H, Yoshikawa Y, Murata N:
Patchworking multiple pairwise distances for learning with distance matrices.
Springer International Publishing. International Conference on Latent Variable Analysis and Signal Separation 287 (2015)                                                                                                       (ISI)

[45] Bockhorst T, Homberg U:
Compass Cells in the Brain of an Insect Are Sensitive to Novel Events in the Visual World
PLoS ONE 10(12):e0144501 (2015)                                                                     (Variation of ISI)

[44] Qu J, Wang R, Du Y:
Measuring effects of different noises in a model using ISI-distance methods.
Int. J. Biomath. 08, 1550043 (2015)                                                                                     (ISI)

[43] Chew G, Ang KK, So RQ, Xu Z, Guan C:
Combining Firing Rate and Spike-Train Synchrony Features in the Decoding of Motor Cortical Activity
IEEE Engineering in Medicine and Biology Society (EMBC), 1091 (2015)                  (ISI,SPIKE,SPIKY)

[42] Eisenman LN, Emnett, CM, Mohan J, Zorumski CF, Mennerick S:
Quantification of bursting and synchrony in cultured hippocampal neurons
JNeurophysiol, 114,1059 (2015)                                                                                      (SPIKE)

[41] Du Y, Wang R, Cao J:
Parameter-dependent synchronization of coupled neurons in cold receptor model.
International Journal of Non-Linear Mechanics 70, 95 (2015)                                                    (ISI)

[40] Hoang H, Yamashita O, Tokuda IT, Sato M, Kawato M, Toyama K:
Segmental Bayesian estimation of gap-junctional and inhibitory conductance of inferior olive neurons from spike trains with complicated dynamics
Front. Comput. Neurosci. 9:56 (2015).                                                                             (SPIKE)

[39] Rabinowitch TC, Knafo-Noam A:
Synchronous rhythmic interaction enhances children’s perceived similarity and closeness towards each other.
PLoS ONE 10(4): e0120878 (2015).                                 (old SPIKE, Inter-personal synchronization)

[38] Diego Andilla F, Hamprecht FA:
Sparse Space-Time Deconvolution for Calcium Image Analysis
Advances in Neural Information Processing Systems 27, 64-72 (NIPS 2014).                          (SPIKE)

[37] Cutts CS, Eglen SJ:
Detecting pairwise correlations in spike trains: An objective comparison of methods and application to the study of retinal waves.
J Neurosci 34, 14288 (2014).                (comparison of correlation measures, but also includes SPIKE)

[36] Konstantoudaki X, Papoutsi A, Chalkiadaki K, Poirazi P, Sidiropoulou K:
Modulatory effects of inhibition on persistent activity in a cortical microcircuit model
Front. Neural Circuits 8: 1 (2014)                                                                              (old SPIKE)

[35] Andrzejak RG, Mormann F, Kreuz T:
Detecting determinism from point processes.
Physical Review E 90, 062906 (2014) [PDF]                                                               (ISI, SPIKE)

[34] Sacre P, Sepulchre R:
Sensitivity Analysis of Oscillator Models in the Space of Phase-Response Curves: Oscillators As Open Systems.
Control Systems, IEEE 34, 50 (2014)                                                    (SPIKE, also time-resolved)

[33] Du Y, Wang R, Cao J:
Noise and Synchronization Analysis of the Cold-Receptor Neuronal Network Model.
Discrete Dynamics in Nature and Society (Hindawi) 173894 (2014)                                           (ISI)

[32] Xu A, Du Y, Wang R, Cao J:
Interaction between different cells in olfactory bulb and synchronous kinematic analysis.
Discrete Dynamics in Nature and Society (Hindawi) 808792 (2014)                                           (ISI)

[31] Wang J, Liu S, Li X:
Quantification of synchronization phenomena in two reciprocally gap-junction coupled bursting pancreatic beta-cells.
Chaos, Solitons & Fractals 68, 65 (2014)                                                                               (ISI)

[30] Rusu CV, Florian RV:
A new class of metrics for spike trains
Neural Comput 26, 306 (2014)                                  (ISI, SPIKE, includes performance comparison)

[29] Dipoppa M, Gutkin BS:
Correlations in background activity control persistent state stability and allow execution of working memory tasks.
Front Comput Neurosci. 7: 139 (2013).                                   (SPIKE, including selective averaging)
[28] Kreuz T, Chicharro D, Houghton C, Andrzejak RG, Mormann F:
Monitoring spike train synchrony.
J Neurophysiol 109, 1457 (2013) [PDF]                                                             (introduces SPIKE)

[27] Qi D, Xiao Z:
Spike Trains Synchrony With Different Coupling Strengths in a Hippocampus CA3 Small-World Network Model.
Proceedings of the 2013 6th International Conference on Biomedical Engineering and Informatics (BMEI 2013).                                                                                                 (ISI, also time-resolved)
[26] Papoutsi A, Sidiropoulo K, Cutsuridis V and Poirazi P:
Induction and modulation of persistent activity in a layer VPFC microcircuit model.
Frontiers in Neural Circuits 7, 161 (2013)                                                                    (old SPIKE)

[25] Chen Y, Zhang H, Wang H, Yu L, Chen Y:
The Role of Coincidence-Detector Neurons in the Reliability and Precision of Subthreshold Signal Detection in Noise.
PLoS ONE 8(2): e56822 (2013)                                                                (ISI, also time-resolved)

[24] Williams MJ, Whitaker RM, Allen SM:
Measuring individual regularity in human visiting patterns.
Proceedings of the ASE International Conf. on Social Computing, 117 (2012) (multivariate ISI-diversity)

[23] Goulet J, van Hemmen JL, Jung SN, Chagnaud BP, Scholze B, Engelmann J:
Temporal precision and reliability in the velocity regime of a hair-cell sensory system: the mechanosensory lateral line of goldfish, Carassius auratus.
J Neurophysiol 107, 2581 (2012)                                                                                         (ISI)

[22] Mitra A, Manitius A, Sauer T:
Prediction of Single Neuron Spiking Activity using an Optimized Nonlinear Dynamic Model.
IEEE EMBS 2543 (2012)                                                                                            (old SPIKE)

[21] Michmizos KP, Sakas D, Nikita KS:
Parameter identification for a local field potential driven model of the Parkinsonian subthalamic nucleus spike activity.
Neural Networks 36, 146 (2012)                                                                         (variation of ISI)

[20] Jalili M:
Collective behavior of interacting locally synchronized oscillations in neuronal networks.
Commun Nonlinear Sci Numer Simulat 17, 3922 (2012)                               (ISI, also time-resolved)

[19] Wildie M, Shanahan M:
Establishing communication between neuronal populations through competitive entrainment.
Front Comp Neurosci 5, 62 (2012)                                                         (multivariate ISI-diversity)

[18] Qu J, Wang R, Du Y, Cao J:
Synchronization study in ring-like and grid-like neuronal networks.
Cogn Neurodyn 6, 21 (2012)                                                                      (ISI, also multivariate)
[17] Lyttle D, Fellous JM:
A new similarity measure for spike trains: Sensitivity to bursts and periods of inhibition.
J Neurosci Methods 199, 296 (2011)       (comparison of measures, includes ISI, shows ISI is a metric)

[16] Spencer MC, Downes JH, Xydas D, Hammond MW, Becerra VM, Whalley BJ, Warwick K, Nasuto SJ:
Spatio-temporal dependencies in functional connectivity in rodent cortical cultures.
J Behavioral Robotics 2, 156 (2012)                                                                           (old SPIKE)

[15] Andrzejak RG, Kreuz T:
Characterizing unidirectional couplings between point processes and flows.
European Physics Letters 96, 50012 (2011) [PDF]                                                                   (ISI)

[14] Kreuz T, Chicharro D, Greschner M, Andrzejak RG:
Time-resolved and time-scale adaptive measures of spike train synchrony.
J Neurosci Methods 195, 92 (2011) [PDF]                                                     (introduces SPIKE-old)

[13] Njap F, Claussen JC, Moser A, Hofmann UG:
Comparing Realistic Subthalamic Nucleus Neuron Models.
AIP Conference Proceedings 1371, 102 (2010)                                                                       (ISI)

[12] Engelmann J, Gertz S, Goulet J, Schuh A, von der Emde G:
Coding of Stimuli by Ampullary Afferents in Gnathonemus petersii.
J Neurophysiol  104, 1955 (2010)                                                                                        (ISI)
[11] Dodla R and Wilson CJ:
Quantification of Clustering in Joint Interspike Interval Scattergrams of Spike Trains.
Biophysical Journal 98, 2535 (2010)                                                                    (variation of ISI)

[10] Xiao Z, Tian X:
Neuronal Ensemble Coding of Spike Trains in the Hippocampus CA3 via Small-world Network
J Computers 5, 448 (2010)                                                                      (ISI, also time-resolved)

[9] Ibarz JM, Foffani G, Cid E, Inostroza M and de la Prida LM:
Emergent Dynamics of Fast Ripples in the Epileptic Hippocampus.
J Neurosci, 30, 16249 (2010)                                                                             (multivariate ISI)

[8] Haas JS*, Kreuz T*, Torcini A, Politi A, Abarbanel HDI:
Rate maintenance and resonance in the entorhinal cortex.
Eur J Neurosci  32, 1930 (2010) [PDF]                                                                                  (ISI)
[7] Du Y, Lu Q:
Noise effects on temperature encoding of neuronal spike trains in a cold receptor.
Chin. Phys. Lett. 27, 020503 (2010)                                                          (ISI, also time-resolved)

[6] Du Y, Lu Q, Wang R:
Using interspike intervals to quantify noise effects on spike trains in temperature encoding neurons. Cognitive Neurodynamics 4, 199 (2010)                                                    (ISI, also time-resolved)

[5] Dodla R and Wilson CJ:
Asynchronous response of coupled pacemaker neurons.
Phys Rev Lett 102, 068102 (2009)                                                                                       (ISI)

[4] Pfeiffer K, French AS:
GABAergic excitation of spider mechanoreceptors increases information capacity by increasing entropy rather than decreasing jitter .
J Neurosci 29, 10989 (2009)                                                                                               (ISI)
Kreuz T, Chicharro D, Andrzejak RG, Haas JS, Abarbanel HDI:
Measuring multiple spike train synchrony.
J Neurosci Methods 183, 287 (2009) [PDF]                                            (introduces multivariate ISI)

[2] Escobar MJ, Masson GS, Vieville T, Kornprobst P:
Action recognition using a bio-inspired feedforward spiking network.
Int J Comput Vis 82, 284 (2009)                                                                                          (ISI)

[1] Kreuz T, Haas JS, Morelli A, Abarbanel HDI, Politi A:
Measuring spike train synchrony.
J Neurosci Methods 165, 151 (2007) [PDF]                                                             (introduces ISI)

A PhD thesis outside of neuroscience:

Williams MJ:
Periodic patterns in human mobility
PhD Thesis, Cardiff University                                                              (multivariate ISI-diversity)

Some preprints and posters:

T Alderson. Modeling the effects of dopamine on the pyloric circuit. Bioinformatics. Preprint (2013) (Supervisors: Peter Andras, Jennifer Hallinan, Newcastle University)                               (old SPIKE)

JS Steyn. Understanding and Modelling the Impact of Dopamine on Gap-junction Coupled Motor Neurons. Poster Newcastle University. (Supervisors: Peter Andras, Jennifer Hallinan)              (SPIKE)

Three posters by M Chary and E Kaplan, Mount Sinai Hospital, New York, NY, USA           (ISI, SPIKE)

Articles that apply event synchronization or delay asymmetry

[44] Mwaffo V, Butail S, Porfiri M:
Analysis of pairwise interactions in a maximum likelihood sense to identify leaders in a group.
Front. Robot. AI 4: 35 (2017).                                                                                               (Event-Synchro)

Alborno P, De Giorgis N, Camurri A, Puppo E:
Limbs synchronisation as a measure of movement quality in karate.
In Proceedings of the 4th International Conference on Movement Computing (2017)     (Event-Synchro)

[42] Grabow C, Macinko J, Silver D, Porfiri M:
Detecting causality in policy diffusion processes.
Chaos 26, 083113 (2016).                                                                       (Event-Synchro/Direct)

[41] Dardard F, Gnecco G, Glowinski D:
Automatic Classification of Leading Interactions in a String Quartet.
ACM Transactions on Interactive Intelligent Systems (TiiS) 6, 5 (2016).          (Event-Synchro/Direct)

[40] Rheinwalt A, Boers N, Marwan N, Kurths J, Hoffmann P, Gerstengarbe FW, Werner P:
Non-linear time series analysis of precipitation events using regional climate networks for Germany. Climate Dynamics 46, 1065 (2016)                                                                     (Event-Synchro)

[39] Butail S, Mwaffo V, Porfiri M:
Model-free information-theoretic approach to infer leadership in pairs of zebrafish.
Physical Review E 93, 042411 (2016)                                                        (Event-Synchro/Direct)

[38] Bustamante MG, Cruz FW, Vuille M, Apaéstegui J, Strikis N, Panizo G, Novello FV, Deininger M, Sifeddine A, Cheng H, Moquet JS:
Holocene changes in monsoon precipitation in the Andes of NE Peru based on δ 18 O speleothem records.
Quaternary Science Reviews 146, 274 (2016)                                                        (Event-Synchro)

[37] Iqbal T, Rack S, Riek LD:
Movement Coordination in Human-Robot Teams: A Dynamical Systems Approach
IEEE Transactions on Robotics 32, 3 (2016)                                                           (Event-Synchro)

[36] Kolykhalova K, Camurri A, Volpe G, Sanguineti M, Puppo E, Niewiadomski R:
A Multimodal Dataset for the Analysis of Movement Qualities in Karate Martial Art
IEEE Intelligent Technologies for Interactive Entertainment (INTETAIN)  (2015)           (Event-Synchro)

[35] Tung JK, Gutekunst CA, Gross RE:
Inhibitory luminopsins: genetically-encoded bioluminescent opsins for versatile, scalable, and hardware-independent optogenetic inhibition.
Sci Rep. 5: 14366 (2015)                                                                                   (Event-Synchro)

[34] Iqbal T, Riek LD:
A Method for Automatic Detection of Psychomotor Entrainment.
IEEE Transactions on affective computing 6 (2015)                                                 (Event-Synchro)

[33] Chew G, Ang KK, So RQ, Xu Z, Guan C:
Combining Firing Rate and Spike-Train Synchrony Features in the Decoding of Motor Cortical Activity
IEEE Engineering in Medicine and Biology Society (EMBC), 1091 (2015)                     (Event-Synchro)

[32] Boers N, Bookhagen B, Marengo J, Marwan N, von Storch JS, Kurths J:
Extreme Rainfall of the South American Monsoon System: A Dataset Comparison Using Complex Networks
JClimate 28, 1031 (2015)                                                                                  (Event-Synchro)

[31] Ascoli A, Lanza V, Corinto F, Tetzlaff R:
Synchronization conditions in simple memristor neural networks
J Franklin Institute 352, 3196 (2015)                                                                   (Event-Synchro)

[30] Boers N, Bookhagen B, Marwan N, Kurths J:
Spatiotemporal characteristics and synchronization of extreme rainfall in South America with
focus on the Andes Mountain range
Clim-Dyn (2015)                                                                                              (Event-Synchro)

[29] Marwan N, Kurths J:
Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems
Chaos 25, 097609 (2015)                                                                                   (Event-Synchro)

[28] Rheinwalt A, Boers N, Marwan N, Kurths J, Hoffmann P, Gerstengarbe FW, Werner P:
Non‑linear time series analysis of precipitation events using regional climate networks for
Clim-Dyn (2015)                                                                                              (Event-Synchro)

[27] Iqbal T, Gonzales MJ, Riek LD:
Joint Action Perception to Enable Fluent Human-Robot Teamwork.
IEEE Int. Symposium on Robot and Human Interactive Communication (2015)  (Event-Synchro/Direct)

[26] Rahbar F, Anzalone S, Varni G, Zibetti E, Ivaldi S, Chetouani M:
Predicting extraversion from non-verbal features during a face-to-face human-robot interaction.
International Conference on Social Robotics, Paris, France. pp.10. (2015)        (Event-Synchro/Direct)

[25] Rosário RS, Cardoso PT, Muñoz MA, Montoya P, Miranda JGV:
Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG
Physica A 439, 7 (2015)                                                                                    (Event-Synchro)

[24] Boers N, Donner RV, Bookhagen B, Kurths J:
Complex network analysis helps to identify impacts of the El Niño Southern Oscillation on moisture divergence in South America
Clim-Dyn (2014)                                                                                              (Event-Synchro)

[23] Boers N, Rheinwalt A, Bookhagen B, Barbosa HMJ, Marwan N, Marengo JA, Kurths J:
The South American rainfall dipole: A complex network analysis of extreme events
Geophys. Res. Lett. 41, 7397 (2014)                                                                    (Event-Synchro)

[22] Boers N, Bookhagen B, Barbosa HMJ, Marwan N, Kurths J, Marengo JA:
Prediction of extreme floods in the eastern Central Andes based on a complex networks approach
Nature Communications 5, 5199 (2014)                                                     (Event-Synchro/Direct)

[21] Rehfeld K, Kurths J:
Similarity estimators for irregular and age-uncertain time series
Clim. Past. 10, 107 (2014)                                                                                 (Event-Synchro)

[20] Su-Hong H, Tai-Chen F, Yan-Chun G, Yan-Hua H, Cheng-Guo W, Zhi-Qiang G:
Predicting extreme rainfall over eastern Asia by using complex networks
Chin Phys B 23, 059202 (2014)                                                                (Event-Synchro/Direct)

[19] Pfeiffer T, Draguhn A, Reichinnek S, Both M:
Optimized temporally deconvolved Ca2+ imaging allows identification of spatiotemporal activity patterns of CA1 hippocampal ensembles
Neuroimage 94, 239 (2014)                                                                               (Event-Synchro)

[18] Mörtl A, Lorenz T, Hirche S:
Rhythm Patterns Interaction - Synchronization Behavior for Human-Robot Joint Action.
PLoS ONE 9(4): e95195 (2014)                                                                           (Event-Synchro)

[17] Singh RD, Gibbons SJ, Saravanaperumal SA, Du P, Hennig GW, Eisenman ST, Mazzone A, Hayashi Y, Cao C, Stoltz GJ, Ordog T, Rock JR, Harfe BD, Szurszewski JH, Farrugia G:
Ano1, a Ca2+-activated Cl−channel, coordinates contractility in mouse intestine by Ca2+ transient
coordination between interstitial cells of Cajal
J Physiol 592.18, 4051 (2014)                                                                            (Event-Synchro)

[16] Ascoli A, Lanza V, Corinto F, Tetzlaff R:
Emergence of synchronization in bio-inspired memristor-coupled oscillatory cells
NOLTA IEICE 5, 292 (2014)                                                                                (Event-Synchro)

[15] Ascoli A, Tetzlaff R, Lanza V, Corinto F, Gilli M:
Memristor plasticity enables emergence of synchronization in neuromorphic networks
IEEE Circuits and Systems (ISCAS) 2261, (2014)                                                   (Event-Synchro)

[14] Rehfeld K, Kurths J:
Similarity estimators for irregular and age-uncertain time series
Clim. Past. 10, 107 (2014)                                                                                 (Event-Synchro)

[13] Boers N, Bookhagen B, Marwan N, Kurths J, Marengo J:
Complex networks identify spatial patterns of extreme rainfall events of the South American Monsoon System
Geophys. Res. Lett. 40, 4386 (2013)                                                                    (Event-Synchro)

[12] Wang CT, Lee CT, Wang XJ, Lo CC:
Top-Down Modulation on Perceptual Decision with Balanced Inhibition through Feedforward and Feedback Inhibitory Neurons
PLOS One 8, e62379 (2013)                                                                               (Event-Synchro)

[11] Malik N, Bookhagen B, Marwan N, Kurths J:
Analysis of spatial and temporal extreme monsoonal rainfall over South Asia using complex networks.
Clim Dyn 39:971–987 (2012)                                                                   (Event-Synchro/Direct)

[10] Varni G, Volpe G, Mazzarino B:
Towards a Social Retrieval of Music Content
Proc IEEE Conf on Social Computing, Privacy, Security, Risk and Trust (2011)  (Event-Synchro/Direct)

[9] Varni G, Volpe G, Camurri A:
A system for real-time multi-modal analysis of nonverbal affective social interaction in user-centric media
IEEE Trans on Multimedia, 12, 576 (2010).                                                              (Event-Direct)

[8] Yu HH, Rosa MGP:
A simple method for creating wide-field visual stimulus for electrophysiology: Mapping and analyzing receptive fields using a hemispheric display.
J Vision 10, 15 (2010).                                                                                      (Event-Synchro)

[7] Malik N, Marwan N, Kurths J:
Spatial structures and directionalities in monsoonal precipitation over south Asia.
Nonlinear Process Geophys 17:371–381 (2010).                                          (Event-Synchro/Direct)

[6] Kreuz T, Chicharro D, Andrzejak RG, Haas JS, Abarbanel HDI:
Measuring multiple spike train synchrony.
J Neurosci Methods 183, 287 (2009) [PDF]                                                           (Event-Synchro)

[5] Varni G, Camurri A, Coletta P, Volpe G:
Toward a Real-time Automated Measure of Empathy and Dominance
IEEE Computational Science and Engineering (2009)                                    (Event-Synchro/Direct)

[4] Kreuz T, Haas JS, Morelli A, Abarbanel HDI, Politi A:
Measuring spike train synchrony.
J Neurosci Methods 165, 151 (2007) [PDF]                                                            (Event-Synchro)

[3] Goldobin DS, Pikovsky A:
Antireliability of noise-driven neurons.
Phys. Rev. E 73:061906 (2006).                                                             (modified Event-Synchro)

[2] Zhou WX, Sornette D:
Evidence of a worldwide stock market log-periodic anti-bubble since mid-2000.
Physica A: Statistical Mechanics and its Applications 330, 543 (2003).                       (Event-Synchro)

[1] Quian Quiroga R, Kreuz T, and Grassberger P:
Event Synchronization: A simple and fast method to measure synchronicity and time delay patterns.
Phys Rev E 66, 041904 (2002) [PDF]                                          (introduces Event-Synchro/Direct)