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

[111] Brill M, Schwab F:
T-Pattern Analysis and Spike Train Dissimilarity for the Analysis of Structure in Blinking Behavior. Physiology & Behavior, 113163 (2020).                                                                   (ISI-distance)

[110] Johnson LA, Wang J, Nebeck SD, Zhang J, Johnson MD, Vitek JL:
Direct activation of primary motor cortex during subthalamic but not pallidal deep brain stimulation. Journal of Neuroscience, 40(10), 2166-2177 (2020).                                              (SPIKE-Synchro)

[109] Kreuz T, Houghton C, Victor JD:
Spike Train Distance
Encycl Comp Neurosci (in press, 2020)                                              (ISI, SPIKE, SPIKE-Synchro)

[108] Tumulty JS, Royster M, Cruz L:
Columnar grouping preserves synchronization in neuronal networks with distance-dependent time delays
PRE 101, 022408 (2020)                                                                                                (SPIKE)

Yavari F, Amiri M, Rahatabada FN, Faloticoc E, Laschi C:
Spike train analysis in a digital neuromorphic system of cutaneous mechanoreceptor
Neurocomp 379, 343 (2020)                                                                                               (ISI)

[106] Soucy JR, Askaryan J, Diaz D, Koppes AN, Annabi N, Koppes RA:
Glial cells influence cardiac permittivity as evidenced through in vitro and in silico models
Biofabrication 12, 015014 (2020)                                                                                    (SPIKE)

[105] Amsalem O, Eyal G, Rogozinski N, Segev I:
An efficient analytical reduction of detailed nonlinear neuron models
Nature Comm 11, 288 (2020)                                                                      (ISI, SPIKE-Synchro)

[104] Neru A, Assisi C:
Theta oscillations gate the transmission of reliable sequences in the medial entorhinal cortex
BioArxiv, doi: (2019)                                           (SPIKE, PySpike)

[103] Garg S, Singh D:
Structural features recapitulate collective dynamics of inhibitory networks
BioArxiv, doi: (2019)            (SPIKE-Synchro, PySpike)

[102] Brouns T, Celikel T:
PASER for automated analysis of neural signals recorded in pulsating magnetic fields
BioArxiv, doi: (2019)                                                    (cSPIKE)

[101] Sihn D, Kim SP:
A Spike Train Distance Robust to Firing Rate Changes Based on the Earth Mover’s Distance
Front. Comput. Neurosci. 13:82 (2019)                                                             (SPIKE, RI-SPIKE)

[100] Melanitis N, Nikita KS:
Biologically-inspired image processing in computational retina models
Comp Biol Med 113, 103399 (2019)                                                                           (ISI, SPIKE)

[99] Lee S, Jang K:
Regularity of vehicle trips in urban areas
IEEE Intelligent Transportation Systems Conference (2019); DOI: 10.1109/ITSC.2019.8917025       (ISI)

[98] Tomlinson SB, Wong JN, Conrad EC, Kennedy BC, Marsh ED:
Reproducibility of interictal spike propagation in children with refractory epilepsy
Epilepsia 60, 898 (2019)                                                                                       (SPIKE-order)

[97] Bardin JB, Spreemann G, Hess K:
Topological exploration of artificial neuronal network dynamics
Network Neurosci 3, 725 (2019)                                                              (SPIKE, SPIKE-Synchro)

[96] Madar AD, Ewell LA, Jones MV:
Temporal pattern separation in hippocampal neurons through multiplexed neural codes
PLoS Comput Biol 15(4): e1006932 (2019)                                                                        (SPIKE)

[95] Ouyang Q, Wu J, Shao Z, Wu M, Cao Z:
A Python Code for Simulating Single Tactile Receptors and the Spiking Responses of Their Afferents
Front. Neuroinform. 13:27 (2019)                                                                                        (ISI)

[94] Unakafova VA, Gail A:
Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data
Front. Neuroinform. 13:57 (2019)                                                                                   (SPIKY)

[93] Lam D, Enright HA, Cadena J, Peters SK, Sales AP, Osburn JJ, Soscia DA, Kulp KS, Wheeler EK,  Fischer NO:
Tissue-specific extracellular matrix accelerates the formation of neural networks and communities in a neuron-glia co-culture on a multi-electrode array.
Scientific Reports, 9, 4159 (2019)                                                                                   (SPIKE)

[92] Bradley JA, Strock CJ:
Screening for Neurotoxicity with Microelectrode Array
CurrProtToxicol 79, e67 (2019)                                                                                           (ISI)

[91] Duarte R, Uhlmann M, van den Broek D, Fitz H, Petersson KM, Morrison A:
Encoding symbolic sequences with spiking neural reservoirs
International Joint Conference on Neural Networks (IJCNN) (2018)         (ISI, SPIKE, SPIKE-Synchro)

[90] Lama N, Hargreaves A, Stevens B, McGinnity TM:
Spike Train Synchrony Analysis of Neuronal Cultures
International Joint Conference on Neural Networks (IJCNN) 1-8 (2018)                           (ISI, SPIKE)

[89] Świetlik D, Białowąs J, Kusiak A, Cichońska D:
Memory and forgetting processes with the firing neuron model
Folia Morphol 77, 221 (2018)                                                                   (ISI, also time-resolved)

[88] Du Y, Liu J, Fu S:
Information Transmitting and Cognition with a Spiking Neural Network Model
Chin Phys Lett 35, 090502 (2018)                                                                                        (ISI)

[87] Bradley JA, Luithardt HH, Metea MR, Strock CJ:
In Vitro Screening for Seizure Liability Using Microelectrode Array Technology
Toxicol Sci 163, 240 (2018)                                                                                                (ISI)

[86] Naudé J, Didienne S, Takillah S, Prévost-Solié C, Maskos U, Faure P:
Acetylcholine-dependent phasic dopamine activity signals exploratory locomotion and choices.
BioRxiv, 242438.(2018)                                                                                                 (SPIKE)

[85] Lassus B, Naudé J, Faure P, Guedin D, Von Boxberg Y, La Cour CM, Millan MJ, Peyrin JM:
Glutamatergic and dopaminergic modulation of cortico-striatal circuits probed by dynamic calcium imaging of networks reconstructed in microfluidic chips.
Scientific reports, 8, 1 (2018)                                                                            (SPIKE-Synchro)

[84] Jouty J, Hilgen G, Sernagor E, Hennig MH:
Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina 
Front Cell Neurosci 12:481 (2018)                                                              (ISI, SPIKE, PySPIKE)

[83] Satuvuori E, Mulansky M, Daffertshofer A, Kreuz T:
Using spike train distances to identify the most discriminative neuronal subpopulation
JNeurosci Methods, 308, 354 [PDF] and arXiv [PDF] (2018)                                                 (SPIKE)

[82] Gardella C, Marre O, Mora T:
Blindfold learning of an accurate neural metric.
Proc Nat Ac Sci 201718710 (2018)                                                    (ISI, 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)

[80] 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)

[79] 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)

[78] 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)

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

[74] Madar AD, Ewell LA, Jones MV:
Pattern separation of spike trains by individual granule cells of the dentate gyrus.
bioaRxiv 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)

[72] 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)

[71] 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)

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

[69] 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)

[68] 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)

[66] Ravello CR, Escobar MJ, Palacios A, Perrinet LU:
Differential response of the retinal neural code with respect to the sparseness of natural images
Arxiv 1611:06834v1 (2016)                                                                                            (SPIKE)

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

[64] 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)

[63] 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)

[62] 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
ComputIntelNeurosci 5615618 (2016)                                                                              (SPIKE)

[60] Espinal A, Rostro-Gonzalez H, Carpio M, Guerra-Hernandez EI, Ornelas-Rodriguez M, Sotelo-Figuero MA:
Design of Spiking Central Pattern Generators for Multiple Locomotion Gaits in Hexapod Robots by Christiansen Grammar Evolution
Front Neurorobot 10:6 (2016)                                                                                         (SPIKE)

[59] 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)

[58] 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)

[57] 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)

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

[55] 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)

[54] 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)

[53] 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)

[52] 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)

[51] 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)

[50] 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)

[49] 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)

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

[47] 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)

[46] 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)

[45] 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)

[44] 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)

[43] 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)

[42] Bozanic N, Mulansky M, Kreuz T:
Scholarpedia 9(12), 32344 (2014)
[41] Thibeault CM, O'Brien MJ, Srinivasa N:
Analyzing large-scale spiking neural data with HRLAnalysis™.
Frontiers in neuroinformatics, 8, 17 (2014)                                                        (SPIKE-Software)

[40] 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)

[39] 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)

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

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

[36] 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)

[35] 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)

[34] 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)

[33] 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)

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

[31] 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)

[30] 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)
[29] 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)

[28] 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)

[27] Kreuz T, Chicharro D, Houghton C, Andrzejak RG, Mormann F:
Monitoring spike train synchrony.
J Neurophysiol 109, 1457 (2013) [PDF]                                                             (introduces SPIKE)

[26] Kreuz T:
Scholarpedia 7(12), 30652 (2012).

[25] 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)

[24] 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)

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

[22] 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)

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

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

[19] 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)
[18] 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)

[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] Kreuz T:
Measures of spike train synchrony.
Scholarpedia 6(10), 11934 (2011)

[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

[80] Celikoglu A:
Earthquake spatial dynamics analysis using event synchronization method.
Physics of the Earth and Planetary Interiors, 106524 (2020).

[79] Yavari F, Amiri M, Rahatabada FN, Faloticoc E, Laschi C:
Spike train analysis in a digital neuromorphic system of cutaneous mechanoreceptor
Neurocomp 379, 343 (2020)

[78] Bertini C, Mineo C, Moccia B:
Setting a methodology to detect main directions of synchronous heavy daily rainfall events for Lazio region using complex networks.
AIP Conf Proc 2116, 210003 (2019)

[77] Jang HJ, Chung H, Rowland JM, Richards BA, Kohl MM, Kwag J:
Distinct roles of parvalbumin and somatostatin interneurons in the synchronization of spike-times in the neocortex.
bioRxiv 671743 (2019)

[76] Ospeck M:
Are sleep spindles poised on supercritical Hopf bifurcations?
BioRxiv 512145 (2019)

[75] De Giorgis N, Puppo E, Alborno P, Camurri A:
Evaluating Movement Quality Through Intrapersonal Synchronization.
IEEE Trans Hum-Mach Syst 49, 304 (2019)

[74] Hassanibesheli F, Donner RV:
Network inference from the timing of events in coupled dynamical systems.
Chaos: Interdisc J Nonl Sci 29(8), 083125 (2019)

[73] Bakhshayesh H, Fitzgibbon SP, Janani AS, Grummett TS, Pope KJ:
Detecting synchrony in EEG: A comparative study of functional connectivity measures.
Comp Biol Med 105, 1 (2019)

[72] Odenweller A, Donner RV:
Disentangling synchrony from serial dependency in paired event time series.
arXiv preprint arXiv:1910.12343 (2019)

[71] Niewiadomski R, Kolykhalova K, Piana S, Alborno P, Volpe G, Camurri A:
Analysis of movement quality in full-body physical activities.
ACM Transactions on Interactive Intelligent Systems (TiiS) 9, 1 (2019)

[70] Kurths J, Agarwal A, Shukla R, Marwan N, Rathinasamy M, Caesar L, Krishnan R, Merz B:  Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach.
Nonl Proc Geophys 26, 251 (2019)

[69] Ozturk U, Malik N, Cheung K, Marwan N, Kurths J:
A network-based comparative study of extreme tropical and frontal storm rainfall over Japan.
Climate dynamics, 53, 521 (2019)

[68] Boers N, Goswami B, Rheinwalt A, Bookhagen B, Hoskins B, Kurths J:
Complex networks reveal global pattern of extreme-rainfall teleconnections.
Nature, 566, 373 (2019)

[67] Niewiadomski R, Chauvigne L, Mancini M, Camurri A:
Towards a model of nonverbal leadership in unstructured joint physical activity.
Proc 5th Intern Conf Movem Comp 1 (2018)

[66] Kada H, Teramae JN, Tokuda IT:
Highly Heterogeneous Excitatory Connections Require Less Amount of Noise to Sustain Firing Activities in Cortical Networks.
Front Comp Neurosci 12, 104 (2018)

[65] Cui T, Caravelli F, Ududec C:
Correlations and clustering in wholesale electricity markets.
Physica A: Stat Mech Appl 492, 1507 (2018)

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

[63] Suchkov D, Sharipzyanova L, Minlebaev M:
Horizontal synchronization of neuronal activity in the barrel cortex of the neonatal rat by spindle-burst oscillations.
Front Cell Neurosci 12, 5 (2018)

[62] Holzbecher A, Kempter R:
Interneuronal gap junctions increase synchrony and robustness of hippocampal ripple oscillations.
Eur J Neurosci 48, 3446 (2018)

[61] Gardella C, Marre O, Mora T:
Blindfold learning of an accurate neural metric.
Proc Nat Ac Sci 201718710 (2018)

[60] Ozturk U, Marwan N, Korup O, Saito H, Agarwal A, Grossman MJ, Zaiki M, Kurths, J:
Complex networks for tracking extreme rainfall during typhoons.
Chaos: Interdisc J Nonl Sci 28, 075301 (2018)

[59] Latchoumane CFV, Jackson L, Sendi MSE, Tehrani KF, Mortensen LJ, Stice SL, Ghovanloo M, Karumbaiah L:
Chronic electrical stimulation promotes the excitability and plasticity of ESC-derived neurons following glutamate-induced inhibition in vitro.
Sci Rep 8, 1 (2018)

[58] Conticello F, Cioffi F, Merz B, Lall U:
An event synchronization method to link heavy rainfall events and large‐scale atmospheric circulation features.
Int J Climat 38, 1421 (2018)

[57] Seshadri S, Klaus A, Winkowski DE, Kanold PO, Plenz D:
Altered avalanche dynamics in a developmental NMDAR hypofunction model of cognitive impairment. Transl Psych 8, 1 (2018)

[56] Agarwal A, Marwan N, Maheswaran R, Merz B, Kurths J:
Quantifying the roles of single stations within homogeneous regions using complex network analysis.
J Hydrol 563, 802 (2018)

[55] Mwaffo V, Keshavan J, Hedrick TL, Humbert S:
Detecting intermittent switching leadership in coupled dynamical systems.
Sci Rep 8, 10338 (2018)

[54] Zhi-qiang G, Hai-jing S, Su-hong HE, Guo-lin F:
Complex Network of extreme Precipitation in East Asia.
J Trop Meteo 426 (2017)

[53] Suhong H, Zhiqiang G, Fang Y, Guolin F:
Application of complex network method to the study of summer extreme precipitation in East Asia.
J Meteo 75, 894 (2017)

[52] Alborno P, De Giorgis N, Camurri A, Puppo E:
Limbs synchronisation as a measure of movement quality in karate.
Proc 4th Intern Conf Movem Comp 1 (2017)

[51] Anzalone SM, Varni G, Ivaldi S, Chetouani M:
Automated prediction of extraversion during human–humanoid interaction.
Internat J Soc Rob, 9, 385 (2017)

[50] Lima CH, AghaKouchak A, Lall U:
Classification of mechanisms, climatic context, areal scaling, and synchronization of floods: the hydroclimatology of floods in the Upper Paraná River basin, Brazil.
Earth Sys Dyn, 8, 1071 (2017)

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

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

[47] Campana C, Zubler F, Gibbs S, De Carli F, Proserpio P, Rubino A, Cossu M, Tassi L, Schindler K, Nobili L:
Suppression of interictal spikes during phasic rapid eye movement sleep: a quantitative stereo‐electroencephalography study.
J Sleep Res 26, 606 (2017)

[46] Asif-Malik A, Dautan D, Young AM, Gerdjikov TV:
Altered cortico-striatal crosstalk underlies object recognition memory deficits in the sub-chronic phencyclidine model of schizophrenia.
Brain (2017)StructFunct 222, 3179 (2017)

[45] Konapala G, Mishra A:
Review of complex networks application in hydroclimatic extremes with an implementation to characterize spatio-temporal drought propagation in continental USA.
J Hydrol 555, 600 (2017)

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)

[43] 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)

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

[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)                              (Directed)

[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)

[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)                                                                                             (Directed)

[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)

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

[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)

[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)

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

[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)

[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)

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

[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)

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

[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)

[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)                          (Directed)

[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)                                 (Directed)

[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)

[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)

[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)

[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)                                                                                         (Directed)

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

[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)                                                                                  (Directed)

[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)

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

[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)

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

[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)

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

[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)

[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)

[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)                                                                                     (Directed)

[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)                    (Directed)

[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)                                                                     (Directed)

[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)

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

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

[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)                                                       (Directed)

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

[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)

[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/Directed)