Skip to content. | Skip to navigation

Personal tools
You are here: Home Publications


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.

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

1) Telling cause from effect in deterministic linear dynamical systems

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

arXiv preprint arXiv:1503.01299. 


C.  Peer reviewed journal publications

61) 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.


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

Wigren, T. (2015). 

IEEE/ACM Transactions on Computational Biology and Bioinformatics

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

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

Frontiers in Neuroinformatics, 9.

58) 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.

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

Koyama, S. (2015).  

Neural computation 27:1530-1548


56) The transfer function of neuron spike

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

Neural Networks, 68, 89-95.


55) 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.


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

Shanableh, T. (2015).  

Signal, Image and Video Processing, 1-7.

53) 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.

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

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

NeuroImage,104, 209-220.

51) Compressive multiplexing of correlated signals

Ahmed, A., & Romberg, J. (2015). 

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


50) 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

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


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


38) 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.


37) 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.

36) 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.

35) 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.

34) 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.


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

32) 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.

31) 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.

30) 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.

29) 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.


28) 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

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

26) 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.

25) 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.

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

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


H. Technical reports produced by personnel

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
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


J.  Courses referencing data

3) CS/CNS/EE 253: Advanced Topics in Machine Learning
Taught at CalTech, Spring 2010.

2) Statistical analysis of neural data
Spring 2009, Columbia University
Instructor: Liam Paninski


1) Modeling and mining of neuroscience data

UC Berkeley, Helen Wills Neuroscience Institute, Redwood Center for Theoretical Neuroscience

Summer 2011, 2012, 2013, 2015



Document Actions