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Publications and online resources (since 2009) based on data sets shared on

The list is mainly the result of internet searches we sometimes perform. Please let us know of items that should be added to this list via the contact form. Current counts:

A. Movies: 3; B. Archive preprints: 26; C. Peer reviewed journal publications: 105: D. Book chapters: 5; E. Conference publications: 50; F. Theses: 14; G. Technical Reports: 3

A.  Movies (Youtube etc.) using data from

3) Theta waves in the hippocampus of a navigating rat

2) What it is like to be a cat? 

1) PCA-based saliency map


B.  Archive Preprints


26) Neural Activity Classification with Machine Learning Models Trained on Interspike Interval Series Data
Ivan Lazarevich, Ilya Prokin, Boris Gutkin, Jan 2020 fcx-1.

25) On the use of calcium deconvolution algorithms in practical contexts
Mathew H. Evans, Rasmus S. Petersen, Mark D. Humphries
bioRxiv, Dec 2019 cai-1.

24) Biologically Plausible Sequence Learning with Spiking Neural Networks
Zuozhu Liu, Thiparat Chotibut, Christopher Hillar, Shaowei Lin, 25 Nov 2019 pvc-3.

23) Targeted photostimulation uncovers circuit motifs supporting short-term memory
Kayvon Daie, Karel Svoboda & Shaul Druckmann
bioRxiv, April 2019
bioRxiv doi: cai-1.

22) The population dynamics of a canonical cognitive circuit
Rishidev Chaudhuri, Berk Gercek, Biraj Pandey, Adrien Peyrache, and Ila Fiete
bioRxiv preprint: Jan 2019 th-1.

21) Large-scale neuron cell classification of single-channel and multi-channel extracellular recordings in the anterior lateral motor cortex
Rohan Parikh
Oct 2018 alm-1.

20) Hierarchical Selective Recruitment in Linear-Threshold Brain Networks -- Part I: Single-Layer Dynamics and Selective Inhibition
Erfan Nozari, Jorge Cortés
Sep 2018 hc-1.

19) Neural activity classification with machine learning models trained on interspike interval series data Lazarevich, Ivan, et al., October 2018. fcx-1.

18) Omitted variable bias in GLMs of neural spiking activity
Stevenson, Ian H, August 2018 DREAM, hc-3, pvc-11.

17) State-space analysis of an Ising model reveals contributions of pairwise interactions to sparseness, fluctuation, and stimulus coding of monkey V1 neurons
Gaudreault, Jimmy, and Hideaki Shimazaki, July 2018. pvc-11.

16) Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience
Mackevicius, Emily L., et al., June 2018 hc-3, hc-5

15) Generative Adversarial Networks Conditioned on Brain Activity Reconstruct Seen Images
St-Yves, Ghislain, and Thomas Naselaris, April 2018 vim-1.

14) Spike-timing patterns conform to gamma distribution with regional and cell type-specific characteristics.
Meng Li, Kun Xie, Hui Kuang, Jun Liu, Deheng Wang, Grace E. Fox, Xiaojian Li, Yuhui Li, Fang Zhao, He Cui, and Joe Z. Tsien., jun 2017. pvc-2.

13) Resolving neuronal population code and coordination with gradient boosted trees.
Guillaume Viejo, Thomas Cortier, and Adrien Peyrache., jun 2017. th-1.


12) Direct Estimation of Firing Rates from Calcium Imaging Data

Ganmor, E., Krumin, M., Rossi, L. F., Carandini, M., & Simoncelli, E. P. 

arXiv preprint arXiv:1601.00364, (2016). 



11) Lack of evidence for cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus

Scheffer-Teixeira, R., & Tort, A. B.  

bioRxiv, 045963, (2016).



10) Simultaneous fMRI and eye gaze recordings during prolonged natural stimulation--a studyforrest extension
Hanke, M., Adelhöfer, N., Kottke, D., Iacovella, V., Sengupta, A., Kaule, F. R., ... & Stadler, J. 

bioRxiv, 046581, (2016).  
vim-1, vim-3

9) The leaky integrator with recurrent inhibition is a predictor
Voss, H. U. 
arXiv preprint arXiv:1601.07534, (2016). 



8) Scoring Sequences of Hippocampal Activity using Hidden Markov Models

Ackermann, E., & Kemere, C. 



7) Using persistent homology to reveal hidden information in neural data

Spreemann, G., Dunn, B., Botnan, M. B., & Baas, N. A. 


arXiv preprint arXiv:1510.06629.

6) An extension of the FRI framework for calcium transient detection

Reynolds, S., Copeland, C. S., Schultz, S. R., & Dragotti, P. L. 


bioRxiv, 029751


5) Unsupervised Video Analysis Based on a Spatiotemporal Saliency Detector 

Zhang, Q., Wang, Y., & Li, B. 


arXiv preprint arXiv:1503.06917


4) Sensitivity to the visual field origin of natural image patches in human low-level visual cortex

Mannion, D. J. (2015).

PeerJ 3:e1038; DOI 10.7717/peerj.1038

3) Chaotic Neuronal Oscillations in Spontaneous CorticalSubcortical Networks 

Pengsheng Zheng


2) Supervised learning sets benchmark for robust spike rate inference from calcium imaging signals

Lucas Theis, Philipp Berens, Emmanouil Froudarakis, Jacob Reimer, Miroslav Román Rosón, Tom Baden, Thomas Euler, Andreas Tolias, Matthias Bethge

1) Realization of multiple neural activity resolutions via multidimensional ensemble empirical mode decomposition of calcium imaging data: a proof of concept

Samuel Akwei-Sekyere



C.  Peer reviewed journal publications

105) Multiscale relevance and informative encoding in neuronal spike trains
Ryan John Cubero, Matteo Marsili, Yasser Roudi
Journal of Computational Neuroscience, Jan, 2020 th-1.

104) nsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience
Emily L Mackevicius, Andrew H Bahle, Alex H Williams, Shijie Gu, Natalia I Denisenko, Mark S Goldman, Michale S Fee
eLife 2019;8:e38471 hc-3, hc-5.

103) A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex
Bjoring MC, Meliza CD
PLoS Comput Biol 15(1): e1006723; 2019
https://doi. org/10.1371/journal.pcbi.1006723 aa-2.

102) Reconstructing neuronal circuitry from parallel spike trains
Kobayashi, Ryota; Kurita, Shuhei; Kurth, Anno; Kitano, Katsunori; Mizuseki, Kenji; Diesmann, Markus; Richmond, Barry J.; Shinomoto, Shigeru
Nature Communications (2019), 10 2019-10-02 hc-3.

101) Learning with Precise Spike Times: A New Decoding Algorithm for Liquid State Machines
Neural Computation, Volume 31 | Issue 9 | September 2019, p.1825-1852 pvc-11.

100) Hippocampal reactivation extends for several hours following novel experience
Bapun Giri , Hiroyuki Miyawaki , Kenji Mizuseki, Sen Cheng and Kamran Diba
The Journal of Neuroscience, 2019 hc-11.

99) Using deep learning to probe the neural code for images in primary visual cortex
Kindel, W. F., Christensen, E. D., & Zylberberg, J.
Journal of Vision, 19(4):29, 1–12; 2019 pvc-8.

98) The quest for interpretable models of neural population activity
Matthew R Whiteway and Daniel A Butts
Current Opinion in Neurobiology 2019, 58:86–93 pvc-11.

97) Spike Estimation From Fluorescence Signals Using High-Resolution Property of Group Delay/em>
Jilt Sebastian, Mari Ganesh Kumar, Venkata Subramanian Viraraghavan, Mriganka Sur, Hema A. Murthy

96) Improved spike inference accuracy by estimating the peak amplitude of unitary [Ca2+ ] transients in weakly GCaMP6f-expressing hippocampal pyramidal cells.
Timea Eltes, Miklos Szoboszlay, Katalin Kerti-Szigeti, Zoltan Nusser
J Physiol. 2019 Jun;597(11):2925-2947 hc-11.

95) A Spike Train Distance Robust to Firing Rate Changes Based on the Earth Mover’s Distance
Duho Sihn and Sung-Phil Kim
Front. Comput. Neurosci. 13:82. doi: 10.3389/fncom.2019.00082 (2019)
URL. dream.

94) An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting
Marie Bernert and Blaise Yvert
International Journal of Neural Systems, Vol. 29, No. 8 (2019) 1850059 hc-1.

93) Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes.
Hu, Sile, et al.
Cell Reports, vol. 25, no. 10, Dec. 2018, p. 2635–2642.e5. Crossref, doi:10.1016/j.celrep.2018.11.033. hc-11.

92) Network Estimation from Point Process Data.
Benjamin Mark, Garvesh Raskutti, Rebecca Willett
IEEE Transactions on Information Theory, Oct 2018, pp. 1–1. Crossref, doi:10.1109/TIT.2018.2875766. fcx-1.

91) Describing Complex Cells in Primary Visual Cortex: A Comparison of Context and Multifilter LN Models.
Westö, Johan, and Patrick J. C. May.
Journal of Neurophysiology, vol. 120, no. 2, Aug. 2018, pp. 703–19. Crossref, doi:10.1152/jn.00916.2017. pvc-2.

90) Homeostatic Plasticity and External Input Shape Neural Network Dynamics
Johannes Zierenberg, Jens Wilting, and Viola Priesemann
Physical Review X, vol. 8, no. 3, July 2018. Crossref, doi:10.1103/PhysRevX.8.031018. pvc-3, hc-2.

89) Uncovering Temporal Structure in Hippocampal Output Patterns.
Kourosh Maboudi, Etienne Ackermann, Laurel Watkins de Jong, Brad E Pfeiffer, David Foster, Kamran Diba, Caleb Kemere
ELife, vol. 7, June 2018. Crossref, doi:10.7554/eLife.34467. hc-6.

88) Foreground-Background Segmentation Revealed during Natural Image Viewing.
Paolo Papale, Andrea Leo, Luca Cecchetti, Giacomo Handjaras, Kendrick N. Kay, Pietro Pietrini and Emiliano Ricciardi
Eneuro, vol. 5, no. 3, June 2018, p. ENEURO.0075-18.2018. Crossref, doi:10.1523/ENEURO.0075-18.2018. vim-1.

87) Phase-Tuned Neuronal Firing Encodes Human Contextual Representations for Navigational Goals.
Andrew J Watrous, Jonathan Miller, Salman E Qasim, Itzhak Fried, and Joshua Jacobs
ELife, vol. 7, June 2018. Crossref, doi:10.7554/eLife.32554. pfc-2.

86) Community-Based Benchmarking Improves Spike Rate Inference from Two-Photon Calcium Imaging Data.
Berens, Philipp, et al.
PLOS Computational Biology, edited by Daniel Bush, vol. 14, no. 5, May 2018, p. e1006157. Crossref, doi:10.1371/journal.pcbi.1006157 cai-1.

85) On Information Metrics for Spatial Coding.
Bryan C.Souzaa, Rodrigo Pavãoa, Hindiael Belchiorb, Adriano B.L. Tort
” Neuroscience, vol. 375, Apr. 2018, pp. 62–73. Crossref, doi:10.1016/j.neuroscience.2018.01.066. hc-3.

84) Brain-state invariant thalamo-cortical coordination revealed by non-linear encoders
Guillaume Viejo, Thomas Cortier, Adrien Peyrache
PLOS Computational Biology, edited by Artur Luczak, vol. 14, no. 3, Mar. 2018, p. e1006041. Crossref, doi:10.1371/journal.pcbi.1006041. th-1.

83) Event-Driven Processing for Hardware-Efficient Neural Spike Sorting.
Yan Liu, João L Pereira, and Timothy G Constandinou
Journal of Neural Engineering, vol. 15, no. 1, Feb. 2018, p. 016016. Crossref, doi:10.1088/1741-2552/aa9124. hc-1.

82) Multivariate cross-frequency coupling via generalized eigendecomposition.
Michael X Cohen.
eLife, 6, 2017a. pfc-2.

81) Nucleotide-time alignment for molecular recorders.
Thaddeus R. Cybulski, Edward S. Boyden, George M. Church, Keith E. J. Tyo, and Konrad P. Kording.
PLOS Computational Biology, 13(5):e1005483, may 2017. dream.

80) Entorhinal-CA3 dual-input control of spike timing in the hippocampus by theta-gamma coupling
Antonio Fern ́andez-Ruiz, Azahara Oliva, Gerg ̋o A. Nagy, Andrew P. Maurer, Antal Ber ́enyi, and Gy ̈orgy Buzs akig.
Neuron, 93(5):1213–1226.e5, mar 2017. hc-3.

79) Fast online deconvolution of calcium imaging data.
Johannes Friedrich, Pengcheng Zhou, and Liam Paninski. PLOS Computational Biology, 13(3):e1005423, mar 2017. cai-1.

78) Mechanisms underlying a thalamocortical transformation during active tactile sensation.
Diego Adrian Gutnisky, Jianing Yu, Samuel Andrew Hires, Minh-Son To, Michael Ross Bale, Karel Svoboda, and David Golomb.
PLOS Computational Biology, 13(6): e1005576, jun 2017. ssc-7.

77) A comparative study for feature integration strategies in dynamic saliency estimation.
Yasin Kavak, Erkut Erdem, and Aykut Erdem.
Signal Processing: Image Communication, 51: 13–25, feb 2017. eye-1.

76) Low activity microstates during sleep.
Hiroyuki Miyawaki, Yazan N. Billeh, and Kamran Diba.
Sleep, 40(6), apr 2017. th-1.

76) An integrated calcium imaging processing toolbox for the analysis of neuronal population dynamics
Sebasti ́an A. Romano, Ver ́onica P ́erez-Schuster, Adrien Jouary, Jonathan Boulanger-Weill, Alessia Cao, Thomas Pietri, and Germ ́an Sumbre PLOS Computational Biology, 13(6):e1005526, jun 2017 pcv-10, cai-1, ssc-1.

75) On cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus.
Robson Scheffer-Teixeira and Adriano BL Tort. eLife, 5, dec 2016. hc-3.

74) Asymmetry of the temporal code for space by hippocampal place cells.
Bryan C. Souza and Adriano B. L. Tort.
Scientific Reports, 7(1), aug 2017. hc-3.

73) Testing the odds of inherent vs. observed overdispersion in neural spike counts.
Wahiba Taouali, Giacomo Benvenuti, Pascal Wallisch, Fr ́ed ́eric Chavane, and Laurent U. Perrinet
Journal of Neurophysiology, 115(1):434–444, oct 2015. lgn-1.

72) Kernel analysis for estimating the connectivity of a network with event sequences.
Taro Tezuka and Christophe Claramunt
Journal of Artificial Intelligence and Soft Computing Research, 7(1), jan 2017. 1515/jaiscr-2017-0002. pvc-3.

71) Realistic Spiking Neuron Statistics in a Population are Described by a Single Parametric Distribution
Lauren E. Crow, Cheng Ly
SIAM Undergraduate Research Online, vol. 9, 2016. Crossref, doi:10.1137/15S014289. mt-1.

70) A spatial-constrained multi-target regression model for human brain activity prediction.
Zhenfu Wen and Yuanqing Li. Applied Informatics, 3(1):10, Nov 2016. ISSN 2196-0089. vim-1.

69) Regular cycles of forward and backward signal propagation in prefrontal cortex and in consciousness.
Paul J. Werbos and Joshua J. J. Davis.
Frontiers in Systems Neuroscience, 10, nov 2016. pfc-2.

68) Sharp oracle inequalities and slope heuristic for specification probabilities estimation in discrete random fields

Lerasle, M., & Takahashi, D. Y. 

Bernoulli, 22(1), 325-344, 2016. 

67) Verification of multichannel electrode array integrity by use of cross-channel correlations

Swindale, N. V., & Spacek, M. A.
Journal of Neuroscience Methods, 263, 95-102,  2016. 
66) A neural coding scheme reproducing foraging trajectories

Gutiérrez, E. D. and Cabrera, J. L.
Nature Sci. Rep. 5, 18009; doi: 10.1038/srep18009

65) Testing the odds of inherent vs. observed overdispersion in neural spike counts

J Neurophysiol. 2016 Jan 1;115(1):434-44. doi: 10.1152/jn.00194.2015. Epub 2015 Oct 7
64) An Asynchronous Recurrent Network of Cellular Automaton-Based Neurons and Its Reproduction of Spiking Neural Network Activities.
Matsubara, T., & Torikai, H.
IEEE Transactions on Neural Networks and Learning Systems, 2015,  27 (4) 836-852
dataset ?
63) Understanding spike-triggered covariance using Wiener theory for receptive field identification

Sandler, R. A., & Marmarelis, V. Z.

Journal of Vision, 2015, 15(9), 16-16.
62) Phase-clustering bias in phase–amplitude cross-frequency coupling and its removal

van Driel, J., Cox, R., & Cohen, M. X.

Journal of Neuroscience Methods, 2015, 254, 60-72.

61) Robust Discovery of Temporal Structure in Multi-neuron Recordings Using Hopfield Networks

Christopher Hillar, Felix Effenberger (2015)

Procedia Computer Science, Volume 53, 2015, Pages 365–374


60) Interspike Intervals Reveal Functionally Distinct Cell Populations in the Medial Entorhinal Cortex

Latuske, P., Toader, O., & Allen, K. (2015).  

Journal of Neuroscience, 35(31), 10963-10976.


59) Model order and identifiability of non-linear biological systems in stable oscillation

Wigren, T. (2015). 

IEEE/ACM Transactions on Computational Biology and Bioinformatics

58) Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data

Lynch, E. P., & Houghton, C. J. (2015).  

Frontiers in Neuroinformatics, 9.

57) Tools for Probing Local Circuits: High-Density Silicon Probes Combined with Optogenetics

Buzsáki, G., Stark, E., Berényi, A., Khodagholy, D., Kipke, D. R., Yoon, E., & Wise, K. D. (2015).  

Neuron, 86(1), 92-105.

56) On the spike train variability characterized by variance-to-mean power relationship

Koyama, S. (2015).  

Neural computation 27:1530-1548


55) The transfer function of neuron spike

Palmieri, I., Monteiro, L. H., & Miranda, M. D. (2015). 

Neural Networks, 68, 89-95.


54) Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream

Güçlü, U., & van Gerven, M. A. (2015). 

Journal of Neuroscience35(27), 10005-10014.


53) Saliency detection in MPEG and HEVC video using intra-frame and inter-frame distances

Shanableh, T. (2015).  

Signal, Image and Video Processing, 1-7.

52) A multistage mathematical approach to automated clustering of high-dimensional noisy data

Friedman, A., Keselman, M. D., Gibb, L. G., & Graybiel, A. M. (2015).  

Proceedings of the National Academy of Sciences112(14), 4477-4482.

51) Data-driven HRF estimation for encoding and decoding models

Pedregosa, F., Eickenberg, M., Ciuciu, P., Thirion, B., & Gramfort, A. (2015).  

NeuroImage,104, 209-220.

50) Compressive multiplexing of correlated signals

Ahmed, A., & Romberg, J. (2015). 

Information Theory, IEEE Transactions on61(1), 479-498.


49) Bayes optimal template matching for spike sorting–combining fisher discriminant analysis with optimal filtering

Franke, F., Quiroga, R. Q., Hierlemann, A., & Obermayer, K. (2015).  

Journal of Computational Neuroscience, 38(3), 439-459.

48) Independent theta phase coding accounts for CA1 population sequences and enables flexible remapping

Chadwick, Angus, Mark CW van Rossum, Matthew F. Nolan. 

eLife 4 (2015): e03542.

47) Sloppiness in Spontaneously Active Neuronal Networks

Dagmara PanasHayder AminAlessandro MaccioneOliver MuthmannMark van RossumLuca Berdondini,Matthias H. Hennig

Journal of Neuroscience, 3 June 2015, 35(22):8480-8492doi:10.1523/JNEUROSCI.4421-14.2015

46) Prospective errors determine motor learning  

Takiyama, K., Hirashima, M., Nozaki, D.

Nature Communications, 2015, 6/01 


45) Models of vocal learning in the songbird: Historical frameworks and the stabilizing critic

Nick, T. A. (2014).  

Developmental Neurobiology doi: 10.1002/dneu.22189

44) Region-based artificial visual attention in space and time

Tünnermann, J., & Mertsching, B. (2014).  

Cognitive Computation, 6(1), 125-143.


43) A non-parametric Bayesian approach for clustering and tracking non-stationarities of neural spikes

Shalchyan, V., & Farina, D. (2014).

Journal of Neuroscience Methods, 223, 85-91. 

42) Toward Statistical Modeling of Saccadic Eye-Movement and Visual Saliency.

Sun, X., Yao, H., Ji, R., & Liu, X. M. (2014).  

IEEE Transactions on Image Processing, 23(11), 4649-4662.

41) Unsupervised Feature Learning Improves Prediction of Human Brain Activity in Response to Natural Images

Güçlü U, van Gerven MAJ (2014)  

PLoS Computional Biology 10(8): e1003724. doi:10.1371/journal.pcbi.1003724

40) Neural Associations of the Early Retinotopic Cortex with the Lateral Occipital Complex during Visual Perception

Zhang D, Wen X, Liang B, Liu B, Liu M, Huang R (2014)  

PLoS ONE 9(9): e108557. doi:10.1371/journal.pone.0108557

39) Neuronal Spike Train Entropy Estimation by History Clustering

Watters, N., & Reeke, G. N. (2014).  

Neural Computation, 26(9), 1840-1872.

38) Ecological sampling of gaze shifts.

Boccignone, G., & Ferraro, M. (2014). 

IEEE Transactions on Cybernetics, 44(2), 266-279.

37) Estimating the correlation between bursty spike trains and local field potentials

Li, Z., Ouyang, G., Yao, L., & Li, X. (2014).  

Neural Networks, 57, 63-72.


36) Spike avalanches in vivo suggest a driven, slightly subcritical brain state
Priesemann Viola, Wibral Michael, Valderrama Mario, Pr∂pper Robert, Le Van Quyen Michel, Geisel Theo, Triesch Jochen, Nikolic Danko, Munk Matthias Hans Joachim
Frontiers in Systems Neuroscience, Vol. 8 (2014).

35) Spatially distributed local fields in the hippocampus encode rat position
G. Agarwal, I. H. Stevenson, A. Berényi, K. Mizuseki, G. Buzsáki, F. T. Sommer
Science 344 (2014): 626-630.

34) Neurosharing: large-scale data sets (spike, LFP) recorded from the hippocampal-entorhinal system in behaving rats
Kenji Mizuseki, Kamran Diba, Eva Pastalkova, Jeff Teeters, Anton Sirota, György Buzsáki


33) Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling
Carlson, D.E.; Vogelstein, J.T.; Qisong Wu; Wenzhao Lian; Mingyuan Zhou; Stoetzner, C.R.; Kipke, D.; Weber, D.; Dunson, D.B.; Carin, L.,
Biomedical Engineering, IEEE Transactions on , vol.61, no.1, pp.41,54, Jan. 2014
doi: 10.1109/TBME.2013.2275751

32) Quantitative analysis of human-model agreement in visual saliency modeling: a comparative study

Borji, A., Sihite, D. N., & Itti, L. (2013).  

IEEE Transactions on Image Processing, 22(1), 55-69.

31) Gaze shift behavior on video as composite information foraging

Boccignone, G., & Ferraro, M. (2013).  

Signal Processing: Image Communication,28(8), 949-966.


30) The Database for Reaching Experiments and Models
Walker B, Kording K (2013). PLoS ONE 8(11): e78747. doi:10.1371/journal.pone.0078747

29) Sorting Electrophysiological Data via Dictionary Learning & Mixture Modeling
Carin, L.; Wu, Q.; Carlson, D.; Lian, W.; Stoetzner, C.; Kipke, D.; Weber, D.; Vogelstein, J.; Dunson, D.
Biomedical Engineering, IEEE Transactions on , vol.PP, no.99, pp.1,1, 0 (Accepted for publication, 2013)

28) Inferring nonlinear neuronal computation based on physiologically plausible inputs
McFarland JM, Cui Y, Butts DA
PLoS Computational Biology 9(7): e1003142. (2013)

27) Region-Based Artificial Visual Attention in Space and Time
Tünnermann, J., & Mertsching, B.
Cognitive Computation, DOI 10.1007/s12559-013-9220-5  (2013).

26) Detecting cell assemblies in large neuronal populations
Vítor Lopes-dos-Santos, Sidarta Ribeiro, Adriano B.L. Tort
Journal of Neuroscience Methods, ISSN 0165-0270, April 29, 2013

25) Population-wide distributions of neural activity during perceptual decision-making
Adrien Wohrer, Mark D. Humphries, Christian K. Machens
Progress in Neurobiology.  Volume 103, April 2013, Pages 156–193

24) Computational Models of Human Visual Attention and Their Implementations: A Survey
Akisato Kimura, Ryo Yonetani, Takatsugu Hirayama
IEICE Trans. Inf. & Syst., Vol.E96-D, No.3 March 2013

23) A Simple Algorithm for Averaging Spike Trains
Hannah Julienne, Conor Houghton
Journal of Mathematical Neuroscience (2013) 3:3 DOI 10.1186/2190-8567-3-3

22) Theta-associated high-frequency oscillations (110–160 Hz) in the hippocampus and neocortex
Adriano B.L. Tort, Robson Scheffer-Teixeira, Bryan C. Souza, Andreas Draguhn, Jurij Brankack
Progress in Neurobiology 100 (2013) 1–14

21) On High-Frequency Field Oscillations (>100 Hz) and the Spectral Leakage of Spiking Activity
Robson Scheffer-Teixeira, Hindiael Belchior, Richardson N. Leao, Sidarta Ribeiro, and Adriano B. L. Tort
The Journal of Neuroscience, January 23, 2013; 33(4):1535–1539

20) Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons
Ian H. Stevenson, Brian M. London, Emily R. Oby, Nicholas A. Sachs, Jacob Reimer, Bernhard Englitz, Stephen V. David, Shihab A. Shamma, Timothy J. Blanche, Kenji Mizuseki, Amin Zandvakili, Nicholas G. Hatsopoulos, Lee E. Miller, Konrad P. Kording
PLoS Comput Biol 8(11): e1002775. doi:10.1371/journal.pcbi.1002775 (2012)

19) Action Potential Waveform Variability Limits Multi-Unit Separation in Freely Behaving Rats

Peter Stratton, Allen Cheung, Janet Wiles, Eugene Kiyatkin, Pankaj Sah, Francois Windels
Plos One 7 (6) e38482 (2012)

18) Spike sorting of heterogeneous neuron types by multimodality-weighted PCA and explicit robust variational Bayes

Takekawa Takashi, Isomura Yoshikazu, Fukai Tomoki
Frontiers in Neuroinformatics (2012) Vol 6, No. 5.; doi: 10.3389/fninf.2012.00005

17) State-of-the-art in Visual Attention Modeling

Ali Borji and Laurent Itti
IEEE Transactions on Pattern Analysis and Machine Intelligence

16) Spatiotemporal receptive fields of cells in V1 are optimally shaped for stimulus velocity estimation
Giacomo Cocci, Davide Barbieri, Alessandro Sarti
J. Opt. Soc. Am. A 29, 130-138 (2012)

15) Measuring the Quality of Neuronal Identification in Ensemble Recordings
Samuel A. Neymotin, William W. Lytton, Andrey V. Olypher, and André A. Fenton
The Journal of Neuroscience, 9 November 2011, 31(45): 16398-16409;
doi: 10.1523/​JNEUROSCI.4053-11.2011

14) A non-parametric method for automatic neural spike clustering based on the non-uniform distribution of the data

Z Tiganj and M Mboup
Journal of Neural Engineering (2011) 8:066014 (13pp), doi:10.1088/1741-2560/8/6/066014

13) Kalman filter mixture model for spike sorting of non-stationary data
A. Calabrese and L. Paninski
Journal of Neuroscience Methods (2011) 196:159-169

12) 1/f Neural Noise Reduction and Spike Feature Extraction using a Subset of Informative Samples
Annals of Biomedical Engineering 39 (2011): 1264-1277
Z. Yang, L. Hoang, Q. Zhao, E. Keefer, W. Liu

11) Spike-Train Communities: Finding Groups of Similar Spike Trains
Mark D. Humphries
Journal of Neuroscience 9 February 2011, 31 (6):2321-2336

10) Eye Movements Show Optimal Average Anticipation with Natural Dynamic Scenes

Cognitive Computation, 2011
Eleonora Vig, Michael Dorr, Thomas Martinetz, Erhardt Barth
DOI: 10.1007/s12559-010-9061-4


9) Gaussian process modulated renewal processes

V. Rao and Y. W. Teh
Advances In Neural Information Processing Systems, 2011


8) On the analysis of multi-channel neural spike data

B. Chen, D. Carlson and L. Carin
Advances in Neural Information Processing Systems, 2011

7) A stochastic model of human visual attention with a dynamic Bayesian network
A. Kimura, D. Pang, T. Takeuchi, K. Miyazato, K. Kashino, J. Yamato
IEEE Transactions on pattern analysis and machine intelligence, Submitted 2010. [PDF]

6) Generation of Spatiotemporally Correlated Spike Trains and Local Field Potentials Using a Multivariate Autoregressive Process
Diego A. Gutnisky and Kresimir Josic
J Neurophysiol 103: 2912-2930, 2010. doi:10.1152/jn.00518.2009

5) A Continuous Entropy Rate Estimator for Spike Trains Using a K-Means-Based Context Tree
Tiger W. Lin and George N. Reeke
Neural Computation
Vol. 22, No. 4, April 2010, Pages 998-1024 (doi:10.1162/neco.2009.11-08-912)

4) Accurate spike sorting for multi-unit recordings
Takashi Takekawa , Yoshikazu Isomura, Tomoki Fukai
European Journal of Neuroscience
Vol. 31, No. 2, January 2010, pages 263 - 272

3) Of bits and wows: A Bayesian theory of surprise with applications to attention
Pierre Baldi and Laurent Itti
Neural Networks
Volume 23, Issue 5, June 2010, Pages 649-666

2) ePPR: a new strategy for the characterization of sensory cells from input/output data
Joaquin Rapela, Gidon Felsen, Jon Touryan, Jerry M. Mendel, Norberto M. Grzywacz
Network: Computation in Neural Systems.
Vol. 21, No. 1-2, Pages 35-90

1) A new spike detection algorithm for extracellular neural recordings
Shahjahan Shahid, Jacqueline Walker, Leslie S Smith
Journal of IEEE Transactions on Biomedical Engineering; June 2009


D.  Book chapters

5) MATLAB for Brain and Cognitive Scientists (MIT Press).
Mike X Cohen. The MIT Press, 2017b. ISBN 0262035820. hc-2.

4) Signal Processing in Neuroscience
Xiaoli Li
Springer, 2016. ISBN 9789811018220. hc-2.


3) Spike Train Pattern Discovery Using Interval Structure Alignment

Tezuka, T.

In Neural Information Processing, 2015 (pp. 241-249). Springer International Publishing. 


2) Dynamic Saliency Models and Human Attention: A Comparative Study on Videos
Nicolas Riche, Matei Mancas, Dubravko Culibrk, Vladimir Crnojevic, Bernard Gosselin, Thierry Dutoit
Lecture Notes in Computer Science Volume 7726, 2013, pp 586-598
Springer Berlin Heidelberg

1) From Saliency to Eye Gaze: Embodied Visual Selection for a Pan-Tilt-Based Robotic Head
Matei Mancas, Fiora Pirri, Matia Pizzoli
In: Advances in Visual Computing, Lecture Notes in Computer Science; Vol. 6938, pp. 135-146. 2011
Doi: 10.1007/978-3-642-24028-7_13
Springer Berlin / Heidelberg


E.  Conference presentations or abstracts

50) Adversarial Training of Neural Encoding Models on Population Spike Trains
Poornima Ramesh, Mohamad Atayi, Jakob H. Macke
2019 Conference on Cognitive Computational Neuroscience 13-16 September 2019 Berlin, Germany pvc-11.

49) Mutually Regressive Point Processes
Ifigeneia Apostolopoulou, Scott Linderman, Kyle Miller, Artur Dubrawski
33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada. pvc-3.

48) Neural Response Analysis for Brain-Machine Interfaces
Eline Stenwig, Mladen Veletic, Ilangko Balasingham
IEEE Symposium on Medical Information and Communication Technology (ISMICT), Oslo, Norway, 2019, pp. 1-6. th-1.

47) Estimation of effective connectivity in the microcircuits of the mouse barrel cortex using dynamic causal modeling of calcium imaging
Kyesam Jung, Jiyoung Kang, Hae‐Jeong Park
BMC Neuroscience 2018, 19(Suppl 2):P87; 27th Annual Computational Neuroscience Meeting (CNS*2018) ssc-1.

46) Automatic objective thresholding to detect neuronal action potentials.
Jarno M. A. Tanskanen, Fikret E. Kapucu, Inkeri Vornanen, and Jari A. K. Hyttinen.
In 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, aug 2016. alm-1.


45) Face Recognition using TSF Model and DWT based Multilevel Illumination Normalization

Madhusoodanan, M., & Cheriyan, J.



44) Neural Decoding Technique to Reconstruct Stimulus from the Evoked fMRI Voxel Responses.

Liby, M. B., & Cheriyan, J., 2016



43) On the threshold based neuronal spike detection, and an objective criterion for setting the threshold. 

Tanskanen, J., Kapucu, F. E., & Hyttinen

In Neural Engineering (NER), 2015 7th International IEEE/EMBS Conference on (pp. 1016-1019). IEEE.


42) Connectivity estimation of neural networks using a spike train kernel

Tezuka, T., & Claramunt, C.

In Neural Networks (IJCNN), 2015 International Joint Conference on (pp. 1-7). IEEE. 


41) Telling cause from effect in deterministic linear dynamical systems

Shajarisales, N., Janzing, D., Schoelkopf, B., & Besserve, M. (2015).

Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 2015. JMLR: W&CP volume 37.


40) On the threshold based neuronal spike detection, and an objective criterion for setting the threshold

Tanskanen, J., Kapucu, F. E., & Hyttinen, J. A. (2015, April).  

Neural Engineering (NER), 2015 7th International IEEE/EMBS Conference on (pp. 1016-1019). IEEE.


39) Unsupervised spike sorting based on discriminative subspace learning.

Keshtkaran, M. R., & Yang, Z. (2014, August).  

Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE (pp. 3784-3788). IEEE.

38) Real-time data compression of neural spikes

Bihr, U., Xu, H., Bulach, C., Lorenz, M., Anders, J., & Ortmanns, M. (2014, June).  

New Circuits and Systems Conference (NEWCAS), 2014 IEEE 12th International (pp. 436-439). IEEE.

37) Reduction of spatial data redundancy in implantable multi-channel neural recording microsystems

Yazdani, N., Rashidi, A., Sodagar, A. M., & Mohebbi, M. (2014, October).  

Biomedical Circuits and Systems Conference (BioCAS), 2014 IEEE (pp. 208-211). IEEE.

36) Color information in a model of saliency

Hamel, S., Guyader, N., Pellerin, D., & Houzet, D. (2014, September).  

Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European (pp. 226-230). IEEE.


35) Comparison of visual saliency models for compressed video
Sayed Hossein Khatoonabadi, Ivan V. Bajic ́, Yufeng Shan
Accepted for presentation at IEEE ICIP 2014

34) Histograms of motion field orientations as a gist Descriptor for the Prediction of Eye Movements

Carbone, A., & Baccino, T. 

In Proceedings of the 2nd Workshop on Recognition and Action for Scene Understanding (REACTS) 2013, 1-14


33) Neural representation and identification of reaching targets by spike trains in motor cortex

Xu, Z., Keng, A. K., Guan, C., & Hoa, H. T. (2013, April).  

Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2013 IEEE Symposium on (pp. 130-137). IEEE.

32) Dynamic saliency models and human attention: a comparative study on videos

Riche, N., Mancas, M., Culibrk, D., Crnojevic, V., Gosselin, B., & Dutoit, T. (2013).  

Computer Vision–ACCV 2012 (pp. 586-598). Springer Berlin Heidelberg.

31) Video Saliency Detection Algorithm Based on Phase and Amplitude Joint Spectrum Difference

Yin, H., Tan, J., Pan, C., & Guan, S. (2013).  

Advances in Multimedia Information Processing–PCM 2013 (pp. 180-189). Springer International Publishing.

30) A self exciting point process model for neural spike sequences, and its rate estimation

Monk, S., & Leib, H. (2013, May).  

Electrical and Computer Engineering (CCECE), 2013 26th Annual IEEE Canadian Conference on (pp. 1-6). IEEE.


29) Predicting where we look from spatiotemporal gaps
Ryo Yonetani, Hiroaki Kawashima, Takashi Matsuyama
Proceeding ICMI '13 Proceedings of the 15th ACM on International conference on multimodal interaction

28) Compressive Multiplexing of Correlated Signals
A. Ahmed and J. Romberg
In Proceedings of CoRR. 2013

27) Overview of eye tracking datasets
Stefan Winkler and Ramanathan Subramanian
Proc. 5th International Workshop on Quality of Multimedia Experience (QoMEX), Klagenfurt, Austria, July 3-5, 2013.

26) Neural decoding of movement targets by unsorted spike trains
Zhiming Xu, Kai Keng Ang, and Cuntai Guan
38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 26-31, 2013.

25) Data-driven approach to dynamic visual attention modelling

Culibrk, D., Sladojevic, S., Riche, N., Mancas, M., & Crnojevic, V. (2012, June)

In SPIE Photonics Europe (pp. 84360N-84360N). International Society for Optics and Photonics


24) Evaluation study of compressed sensing for neural spike recordings

Bulach, C., Bihr, U., & Ortmanns, M. (2012, August).  

Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE(pp. 3507-3510). IEEE.

23) Evaluating the influence of the bit error rate on the information of neural spike signals

Bulach, C., Bihr, U., & Ortmanns, M. (2012, December).  

Electronics, Circuits and Systems (ICECS), 2012 19th IEEE International Conference on (pp. 21-24). IEEE.


22) Compressive Multiplexers for Correlated Signals
Ali Ahmed and Justin Romberg
Signals, Systems and Computers (ASILOMAR), 2012 Conference Record of the Forty Sixth Asilomar Conference on
DOI: 10.1109/ACSSC.2012.6489159 (2012), Page(s): 963-967

21) Automatic classification of audio data using nonlinear neural response models
Jörg-Hendrik Bach, Arne-Freerk Meyer, Duncan McElfresh, Jörn Anemüller
International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Kyoto, Japan, March 25 - 30, 2012.

20) Adaptive Object Tracking by Learning Background Context
Ali Borji, Simone Frintrop, Dicky N. Sihite, Laurent Itti
CVPR 2012, Egocentric Vision workshop.

19) Compressive sampling of correlated signals
Ahmed, A. and Romberg, J.
Signals, Systems and Computers (ASILOMAR) 2011 Conference, pp.1188-1192
doi: 10.1109/ACSSC.2011.6190203

18) The space of neurons
James Gillespie and Conor Houghton
Mathematical Neuroscience 2011, Edinburgh, Scotland, April 11-13, 2011.

17) Gaussian process modulated renewal processes
Vinayak Rao
Advances in Neural Information Processing Systems 24 (NIPS, 2011 conference).

16) On the Analysis of Multi-Channel Neural Spike Data
Bo Chen, David Carlson, Lawrence Carin
Advances in Neural Information Processing Systems 24 (NIPS, 2011 conference).

15) Fast orientation tuning in mammalian V1 neurons under high-frequency natural image stimulation
Iyer, A.V. and Grzywacz, N.M.
Annual Joint Symposium On Neural Computation 2011, UC San Diego, Institute for Neural Computation

14) Human-motion saliency in multi-motion scenes and in close interaction
M. Mancas, F. Pirri, M. Pizzoli
Proceeding of the 9th International Gesture Workshop on Gesture in Embodied Communication and Human-Computer Interaction, Athens, Greece, May 2011

13) Hardware Accelerated Visual Attention Algorithm
P. Akselrod, F. Zhao, I. Derekli, C. Farabet, B. Martini, Y. LeCun, Eugenio Culurciello
Proc. Conference on Information Sciences and Systems (CISS), IEEE, Baltimore, 2011

12) Towards guiding principles in workflow design to facilitate collaborative projects involving massively parallel electrophysiological data
Michael Denker, Andrew Davison, Markus Diesmann, Sonja Grün
Twentieth Annual Computational Neuroscience Meeting: CNS*2011
BMC Neuroscience 2011, 12 (Suppl 1):P131

11) Hardware Accelerated Visual Attention Algorithm
P. Akselrod, F. Zhao, I. Derekli, C. Farabet, B. Martini, Y. LeCun, E. Culurciello
Proc. Conference on Information Sciences and Systems (CISS’11), IEEE, Baltimore, 2011

10) Re-testing the energy model: identifying features and nonlinearities of complex cells
Tim Lochmann, Joseph N. Stember,  Tim Blanche, Daniel A. Butts
COSYNE 2010, Poster

9) A dataset and evaluation methodology for visual saliency in videos
J. Li, Y. Tiang, T. Huang, W. Gao
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo, IEEE Press

8) Cepstrum of Bispectrum Spike Detection applied to Extracellular Signals with Concurrent Intracellular Signals
Shahjahan Shahid and Leslie S. Smith
BMC Neuroscience Vol. 10, Supplement 1, P59, DOI 10.1186/1471-2202-10-S1-P59
Eighteenth Annual Computational Neuroscience Meeting: CNS*2009

7) Assessing the effectiveness of Cepstrum of Bispectrum based spike detection on simultaneously recorded intra- and extra- cellularly recorded data
Leslie S. Smith and Shahjahan Shahid
Poster presentation, SfN09.

6) Real-time estimation of human visual attention with dynamic Bayesian network and MCMC-based particle filter
Kouji Miyazato, Akisato Kimura, Shigeru Takagi, Junji Yamato
2009 IEEE International Conference on Multimedia and Expo (ICME 2009)

5) Real time estimation of human visual attention with MCMC-based particle filter
Kouji Miyazato, Akisato Kimura, Shigeru Takagi, Junji Yamato, and Kunio Kashino
MIRU2009, The 12th International Conference on Image Recognition
Meeting on Image Recognition and Understanding

4) Dependent Dirichlet Process Spike Sorting
Jan Gasthaus, Frank Wood, Dilan Go ru r, Yee Whye Teh
2009 Neural Information Processing Systems conference (NIPS 2009)

3) Spike-train communities: Finding groups of similar spike trains
M.D. Humphries, Univ. Sheffield, Sheffield, United Kingdom.
Program No. 322.11. 2009 Neuroscience Meeting Planner. Chicago, IL: Society
for Neuroscience, 2009. Online

2) Accurate spike sorting of multiunit recording data based on the robust variational Bayesian clustering
Takashi Takekawa, Yoshikazu Isomura, Tomoki Fukai
SfN 2008

1) Introduction of an automatic spike sorter, Clust016; its features and performance evaluation.

Hidekazu Kaneko, Hiroshi Tamura
2008 abstract

F.  Masters and Doctoral Theses

14) Properties and applications of convex neural codes
Alexander Boris Kunin
A Dissertation in Mathematics Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy The Pennsylvania State University August, 2019 ret-1.

13) A hybrid versatile method for state estimation and feature extraction from the trajectory of animal behavior
Shuhei Yamazaki
Doctoral Thesis, Department of Biological Sciences Graduate School of Science Osaka University July 2019 hc-3.

12) Uncovering Temporal Structure in Neural Data with Statistical Machine Learning Models
Cameron Higgins
JWadham College University of Oxford A thesis submitted for the degree of Doctor of Philosophy Trinity, 2019 . pfc-7.

11) Latent variable models for hippocampal sequence analysis
Etienne Rudolph Ackermann
A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Molecular and Cellular Biology Baylor College of Medicine, Houston, Texas June 2019 hc-6.


10) Study and prediction of visual attention with deep learning net- works in view of assessment of patients with neurodegenerative diseases
Souad Chaabouni
Université de Bordeaux; Université de Sfax (Tunisie), 2017. Français eye-1.

9) Modelling Neuronal Activity with Jittered Generalised Linear Models
Kristian Aga. Master Thesis, Norwegian University of Science and Technology, March 2018.


8) Modelling neuronal activity using lasso regularized logistic regression.
Haris Fawad. Master’s thesis Norwegian University of Science and Technology, June 2017. alm-1.

7) Extracellular Potentials in the Hippocampus
Antonio Fern ́andez Ruiz PhD thesis, University of Madrid, Spain, 2016
Springer, ISBN: 978-3-319-41039-5 hc-3.

6) Estudo dos modos de atividade de c ́elulas de lugar no hipocampo de ratos com livre movimentac ̧ ̃ao

5) Biophysical mechanisms and sources of extracellular potentials in the hippocampus

Fernández Ruiz, A. (2016).

Doctoral Thesis, Universidad Complutense de Madrid, Spain


4) Stochastic Inference and Bayesian Nonparametric Models in Electrophysiological Time Series 

Carlson, D. (2015). 

Doctoral dissertation, Duke University

3) Spike Field Coherence (SFC) for Ripples in Rat Hippocampus

Singla, P. (2015).

Honors Thesis, University of Connecticut


2) Single cell and population coding principles in the songbird auditory cortex

Calabrese, A. (2015).  

Doctoral dissertation, Columbia University


1) Spike Sorting Using Time-Varying Dirichlet Process Mixture Models
Jan A. Gasthaus
MSc in Intelligent Systems
University College London submitted September 5th, 2008
supervised by Frank Wood and Yee Whye Teh

G.  Lecture slides

3) The variation of spike time
Conor Houghton
Mathematical Neuroscience Laboratory; School of Mathematics, Trinity College Dublin; Galway, January 2012



2) Spike sorting on silicon probes
John Schulman

1) Architectures for Compressive Sampling of Ensembles of Correlated Signals
Justin Romberg and Ali Ahmed
Georgia Tech, School of ECE Mathematics and Image Analysis
January 17, 2012; Paris, France


G. Technical reports

3) D4.7.2 - Theoretical Neuroscience – Results for SGA1 Period 2
Human Brain Project SGA1, March 2018 pvc-5

2) epHDF: A proposed standard for storing neuroscience electrophysiology data in HDF5
Jeffrey L Teeters, Friedrich T. Sommer (2013)


1) HDFds – Conventions to facilitate data sharing using HDF5
Jeffrey L Teeters, Friedrich T. Sommer (2013)

I.  Online resources

Finding spikes in electrophysiological data from neurons
Selina Dasol Kim. 2017.
Wolfram High School Summer Camp project ac-2.


Spike sorting software which uses data hosted at to test spike sorting algorithms.


Description of an analysis that was developed and tested using a data set hosted at



H.  Courses referencing data

7) Neural Signal Processing  (UCSD Cogs 118C)
Univ. of California at San Diego
Instructor: Eran Mukamel
Taught 2017


6) CoSMo (Summer School in Computational Sensory-Motor Neuroscience)
Northwestern University
Instructor: Konrad Körding
Taught 2017

5) Intro to Data Science for Biology
UMASS Boston
Instructor: Jarrett Byrnes
Taught 2016


4) Fundamentals of Computational Neuroscience
Harvard University, Neurobiology 105
Instructors: Alexander Mathis and Ashesh Dhawale
Course description at:
Course syllabus at:

3) Advanced Topics in Machine Learning
CalTech, CS/CNS/EE 253
Instructors: Andreas Krause and Daniel Golovin
Taught 2010

2) Statistical analysis of neural data
Columbia University
Instructor: Liam Paninski
Taught 2009, 2013, 2015 and 2017


1) Modeling and mining of neuroscience data
UC Berkeley, Helen Wills Neuroscience Institute, Redwood Center for Theoretical Neuroscience
Summer 2011, 2012, 2013, 2015, 2016, 2017

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