Publications
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 CRCNS.org
3) Theta waves in the hippocampus of a navigating rat
2) What it is like to be a cat?
B. Archive Preprints
26) Neural Activity Classification with Machine Learning Models Trained on Interspike Interval Series Data
Ivan Lazarevich, Ilya Prokin, Boris Gutkin
arxiv.org, Jan 2020
https://arxiv.org/pdf/1810.03855.pdf. 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
https://doi.org/10.1101/871137. cai-1.
24) Biologically Plausible Sequence Learning with Spiking Neural Networks
Zuozhu Liu, Thiparat Chotibut, Christopher Hillar, Shaowei Lin
arxiv.org, 25 Nov 2019
https://arxiv.org/abs/1911.10943. pvc-3.
23) Targeted photostimulation uncovers circuit motifs supporting short-term memory
Kayvon Daie, Karel Svoboda & Shaul Druckmann
bioRxiv, April 2019
bioRxiv doi: http://dx.doi.org/10.1101/623785. 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
https://doi.org/10.1101/516021. 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
https://doi.org/10.1101/445700. 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
https://arxiv.org/abs/1809.01674. hc-1.
19) Neural activity classification with machine learning models trained on interspike interval series data
Lazarevich, Ivan, et al.
arxiv.org, October 2018.
http://arxiv.org/abs/1810.03855. fcx-1.
18) Omitted variable bias in GLMs of neural spiking activity
Stevenson, Ian H
biorxiv.org, August 2018
http://biorxiv.org/lookup/doi/10.1101/317511. 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
arXiv.org, July 2018.
http://arxiv.org/abs/1807.08900. pvc-11.
16) Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience
Mackevicius, Emily L., et al.
biorxiv.org, June 2018
http://biorxiv.org/lookup/doi/10.1101/273128. hc-3, hc-5
15) Generative Adversarial Networks Conditioned on Brain Activity Reconstruct Seen Images
St-Yves, Ghislain, and Thomas Naselaris
biorxiv.org, April 2018
http://biorxiv.org/lookup/doi/10.1101/304774. 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.
bioRxiv.org, jun 2017.
https://doi.org/10.1101/145813. pvc-2.
13) Resolving neuronal population code and coordination with gradient boosted trees.
Guillaume Viejo, Thomas Cortier, and Adrien Peyrache.
biorxiv.org, jun 2017.
https://doi.org/10.1101/148643. 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).
cai-1
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).
hc-3
|
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
arXiv preprint arXiv:1601.07534, (2016).
|
hc-5
8) Scoring Sequences of Hippocampal Activity using Hidden Markov Models
Ackermann, E., & Kemere, C.
2016
https://pdfs.semanticscholar.org/33ee/dc42dee6e9606d0148d4e84b89dbf07d3f43.pdf
hc-3
7) Using persistent homology to reveal hidden information in neural data
Spreemann, G., Dunn, B., Botnan, M. B., & Baas, N. A.
2015
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.
2015
bioRxiv, 029751
cai-1
5) Unsupervised Video Analysis Based on a Spatiotemporal Saliency Detector
Zhang, Q., Wang, Y., & Li, B.
2015
arXiv preprint arXiv:1503.06917
eye-1
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
http://arxiv.org/pdf/1507.05970.pdf
hc-4
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
http://dx.doi.org/10.1101/010777
1) Realization of multiple neural activity resolutions via multidimensional ensemble empirical mode decomposition of calcium imaging data: a proof of concept
Samuel Akwei-Sekyere
https://peerj.com/preprints/1441.pdf
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
https://doi.org/10.1007/s10827-020-00740-x. 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
https://doi.org/10.7554/eLife.38471. 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
http://hdl.handle.net/2433/244205. 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
https://doi.org/10.1162/neco_a_01218. 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
https://doi.org/10.1523/JNEUROSCI.1950-18.2018. 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
https://doi.org/10.1167/19.4.29. 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
https://doi.org/10.1016/j.conb.2019.07.004. 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
IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 67, NO. 11, JUNE 1, 2019
https://www.surlab.org/wp-content/uploads/2019/07/Sebastian-et-al-IEEE-Trans-Signal-Proc-2019.pdf. cai-1.
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
https://doi.org/10.1113/JP277681. 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
https://doi.org/10.1142/S0129065718500594. 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.
https://linkinghub.elsevier.com/retrieve/pii/S2211124718317960. 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.
https://ieeexplore.ieee.org/document/8490856/. 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.
https://www.physiology.org/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.
https://link.aps.org/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.
https://elifesciences.org/articles/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.
https://doi.org/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.
https://elifesciences.org/articles/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
https://dx.plos.org/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.
https://doi.org/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.
https://dx.plos.org/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.
https://doi.org/10.1088/1741-2552/aa9124. hc-1.
82) Multivariate cross-frequency coupling via generalized eigendecomposition.
Michael X Cohen.
eLife, 6, 2017a.
https://doi.org/10.7554/eLife.21792. 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.
https://doi.org/10.1371/journal.pcbi.1005483. 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.
https://doi.org/10.1016/j.neuron.2017.02.017. 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.
https://doi.org/10.1371/journal.pcbi.1005423. 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.
https://doi.org/10.1371/journal.pcbi.1005576 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.
https://doi.org/10.1016/j.image.2016.11.003. eye-1.
76) Low activity microstates during sleep.
Hiroyuki Miyawaki, Yazan N. Billeh, and Kamran Diba.
Sleep, 40(6), apr 2017.
https://doi.org/10.1093/sleep/zsx066 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
https://doi.org/10.1371/journal.pcbi.1005526. 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.
https://doi.org/10.7554/elife.20515. 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.
https://doi.org/10.1038/s41598-017-08609-3. 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.
https://doi.org/10.1152/jn.00194.2015. 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.
https://doi.org/10. 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.
http://www.siam.org/students/siuro/vol9/S01428.pdf. 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.
https://doi.org/10.1186/s40535-016-0026-x. 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.
https://doi.org/10.3389/fnsys.2016.00097. pfc-2.
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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.
hc-2
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
hc-2
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65) Testing the odds of inherent vs. observed overdispersion in neural spike counts
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
https://doi.org/10.1016/j.procs.2015.07.313
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 Neuroscience, 35(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 Sciences, 112(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 on, 61(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
, , , , , ,Matthias H. Hennig
Journal of Neuroscience, 3 June 2015, 35(22):8480-8492; doi: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).
DOI=10.3389/fnsys.2014.00108
http://journal.frontiersin.org/Journal/10.3389/fnsys.2014.00108/abstract
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.
http://www.sciencemag.org/content/344/6184/626.abstract
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
http://f1000research.com/articles/3-98/v2
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
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6571240
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
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.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)
doi:10.1109/TBME.2013.2275751
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6571240&isnumber=4359967
28) Inferring nonlinear neuronal computation based on physiologically plausible inputs
McFarland JM, Cui Y, Butts DA
PLoS Computational Biology 9(7): e1003142. (2013)
http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003143
27) Region-Based Artificial Visual Attention in Space and Time
Tünnermann, J., & Mertsching, B.
Cognitive Computation, DOI 10.1007/s12559-013-9220-5 (2013).
http://link.springer.com/article/10.1007/s12559-013-9220-5
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
http://dx.doi.org/10.1016/
http://www.sciencedirect.com/science/article/pii/S0165027013001489
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
http://www.sciencedirect.com/science/article/pii/S0301008212001505
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
http://repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/171749/1/IEICE_E96-D_3_562.pdf
http://search.ieice.org/bin/summary.php?id=e96-d_3_562&category=D&lang=E&year=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
http://www.mathematical-neuroscience.com/content/pdf/2190-8567-3-3.pdf
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
http://repositorio.ufrn.br:8080/jspui/bitstream/1/6198/1/AdrianoBLT_ICE_Theta-associated2013.pdf
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
http://www.jneurosci.org/content/33/4/1535.full.pdf
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)
http://www.physiology.northwestern.edu/secondlevel/miller/publications/media/PLoSCompBiolv8e1002775.pdf
Peter Stratton, Allen Cheung, Janet Wiles, Eugene Kiyatkin, Pankaj Sah, Francois Windels
Plos One 7 (6) e38482 (2012)
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038482
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
http://www.frontiersin.org/Journal/Abstract.aspx?s=752&name=neuroinformatics&ART_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
http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.89
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)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-29-1-130
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
http://www.jneurosci.org/content/31/45/16398.short
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
http://stacks.iop.org/JNE/8/066014
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
http://www.sciencedirect.com
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
http://www.ece.nus.edu.sg/stfpage/eleyangz/ABME_11.pdf
11) Spike-Train Communities: Finding Groups of Similar Spike Trains
Mark D. Humphries
Journal of Neuroscience 9 February 2011, 31 (6):2321-2336
DOI:10.1523/JNEUROSCI.2853-10.201
http://www.jneurosci.org/content/31/6/2321.short
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
http://www.springerlink.com/content/73l4j324g63150n1/
9) Gaussian process modulated renewal processes
V. Rao and Y. W. Teh
Advances In Neural Information Processing Systems, 2011
http://books.nips.cc/papers/files/nips24/NIPS2011_1333.pdf
8) On the analysis of multi-channel neural spike data
B. Chen, D. Carlson and L. Carin
Advances in Neural
Information Processing Systems, 2011
http://books.nips.cc/papers/files/nips24/NIPS2011_0590.pdf
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.
http://arxiv.org/pdf/1004.0085 [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
http://jn.physiology.org/cgi/content/full/103/5/2912
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)
http://www.ncbi.nlm.nih.gov/pubmed/19922298
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
http://www3.interscience.wiley.com/journal/123239648/abstract
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
http://linkinghub.elsevier.com/retrieve/pii/S0893608009003256
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
http://www.ncbi.nlm.nih.gov/pubmed/20735338
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
http://www.ncbi.nlm.nih.gov/pubmed/19622433
D. Book chapters
5) MATLAB for Brain and Cognitive Scientists (MIT Press).
Mike X Cohen. The MIT Press, 2017b. ISBN 0262035820.
https://www.amazon.com/MATLAB-Brain-Cognitive-Scientists-Press/dp/0262035820 hc-2.
4) Signal Processing in Neuroscience
Xiaoli Li
Springer, 2016. ISBN 9789811018220.
https://www.amazon.com/Signal-Processing-Neuroscience-Xiaoli-Li-ebook/dp/B01LC9GNIK 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
http://link.springer.com/chapter/10.1007/978-3-642-37431-9_45
http://dx.doi.org/10.1007/978-3-642-37431-9_45
http://tcts.fpms.ac.be/publications/papers/2012/accv2012_nrmmbgtd.pdf
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
http://dx.doi.org/10.1007/978-3-642-24028-7_13
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
https://ccneuro.org/2019/proceedings/0000149.pdf. 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.
https://papers.nips.cc/paper/8755-mutually-regressive-point-processes.pdf. 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.
https://ieeexplore.ieee.org/document/8743726. 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)
https://doi.org/10.1186/s12868-018-0452-x. 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.
https://doi.org/10.1109/eusipco.2016.7760331. alm-1.
45) Face Recognition using TSF Model and DWT based Multilevel Illumination Normalization
Madhusoodanan, M., & Cheriyan, J.
http://ijsr.net/archive/v5i2/NOV161283.pdf
vim-1
44) Neural Decoding Technique to Reconstruct Stimulus from the Evoked fMRI Voxel Responses.
Liby, M. B., & Cheriyan, J.
http://ijsr.net/archive/v5i2/NOV161137.pdf, 2016
vim-1
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.
http://jmlr.org/proceedings/papers/v37/shajarisales15.pdf
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
http://mcl.ensc.sfu.ca/pubs/kbs_ICIP2014.pdf
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
eye-?
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
http://dl.acm.org/citation.cfm?id=2522853
28) Compressive Multiplexing of Correlated Signals
A. Ahmed and J. Romberg
In Proceedings of CoRR. 2013
http://arxiv.org/abs/1308.5146
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.
http://stefan.winkler.net/Publications/qomex2013eye.pdf
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.
http://iipl.tk/paper/ICASSP2013/pdfs/0000954.pdf
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
eye-1
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
http://www.aliahmed.org/pdf/conf2.pdf
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.
http://www.icassp2012.org/Papers/ViewPapers.asp?PaperNum=3285
http://www.staff.uni-oldenburg.de/joerg-hendrik.bach/publications/BachEtAl_ICASSP2012.pdf
20) Adaptive Object Tracking by Learning Background Context
Ali Borji, Simone Frintrop, Dicky N. Sihite, Laurent Itti
CVPR 2012, Egocentric Vision workshop.
http://ilab.usc.edu/~borji/Publications.html
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
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6190203&isnumber=6189941
18) The space of neurons
James Gillespie and Conor Houghton
Mathematical Neuroscience 2011, Edinburgh, Scotland, April 11-13, 2011.
www.maths.tcd.ie/~mnl/papers/tcd-mnl-18a.pdf
http://www.maths.tcd.ie/~mnl/papers/tcd-mnl-18a.pdf
17) Gaussian process modulated renewal processes
Vinayak Rao
Advances in Neural Information Processing Systems 24 (NIPS, 2011 conference).
http://books.nips.cc/papers/files/nips24/NIPS2011_1333.pdf
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).
http://books.nips.cc/papers/files/nips24/NIPS2011_0590.pdf
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
http://inc.ucsd.edu/jsnc_2011.html
www.jsnc.caltech.edu/2011/abstracts/iyer-a.pdf
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
http://tcts.fpms.ac.be/publications/regpapers/2011/mmfpmp_isvc_LNCS.pdf
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
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05766191
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
http://www.biomedcentral.com/1471-2202/12/S1/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
http://data.clement.farabet.net/pubs/ciss11.pdf
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
http://www.clfs.umd.edu/biology/ntlab/NTlab/Cosyne2010_files/Cosyne2010_V1_small.pdf
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
http://dl.acm.org/citation.cfm?id=1699033
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
http://www.springerlink.com/content/86163265j5323p25/
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.
http://www.carmen.org.uk/publications/SFN09-poster-final-ls.pdf
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)
http://www.brl.ntt.co.jp/people/akisato/pdf/icme2009miyazato.pdf
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
www.brl.ntt.co.jp/people/akisato/pdf/miru2009Miyazato.pdf
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)
http://books.nips.cc/papers/files/nips21/NIPS2008_0998.pdf
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
http://nct.brain.riken.jp/~takekawa/SfN2008.pdf
1) Introduction of an automatic spike sorter, Clust016; its features and performance evaluation.
Hidekazu Kaneko, Hiroshi Tamura
2008 abstract
http://www2.bpe.es.osaka-u.ac.jp/multineuron/multineuron2008/abstract/Kaneko.pdf
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
https://etda.libraries.psu.edu/files/final_submissions/20123. 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
https://doi.org/10.18910/73519. 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
https://ora.ox.ac.uk/objects/uuid:63d14863-e937-40fb-81a2-c23783edf951/download_file?file_format=pdf&safe_filename=HIGGINS_Thesis.pdf&type_of_work=Thesis
. 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
https://scholarship.rice.edu/bitstream/handle/1911/106148/ACKERMANN-DOCUMENT-2019.pdf?sequence=1. 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
https://tel.archives-ouvertes.fr/tel-02408326/document. eye-1.
9) Modelling Neuronal Activity with Jittered Generalised Linear Models
Kristian Aga. Master Thesis, Norwegian University of Science and Technology, March 2018.
https://brage.bibsys.no/xmlui/bitstream/handle/11250/2499971/20193_FULLTEXT.pdf?sequence=1
8) Modelling neuronal activity using lasso regularized logistic regression.
Haris Fawad. Master’s thesis Norwegian University of Science and Technology, June 2017.
https://brage.bibsys.no/xmlui/bitstream/handle/11250/2448349/17291_FULLTEXT.pdf. 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
http://www.springer.com/us/book/9783319410388. hc-3.
6) Estudo dos modos de atividade de c ́elulas de lugar no hipocampo de ratos com livre movimentac ̧ ̃ao
JADER PERES DA SILVA.
Master’s thesis, UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL, 2016.
http://www.bibliotecadigital.ufrgs.br/da.php?nrb=001015261&loc=2017&l=d9f127a4c5b998a3. hc-3.
5) Biophysical mechanisms and sources of extracellular potentials in the hippocampus
Fernández Ruiz, A. (2016).
Doctoral Thesis, Universidad Complutense de Madrid, Spain
http://eprints.ucm.es/35177/1/T36756.pdf
hc-3
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
hc-3
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
http://www.gatsby.ucl.ac.uk/~ucabjga/papers/Gasthaus_2008_MScThesis.pdf
G. Lecture slides
3) The variation of spike time
Conor Houghton
Mathematical Neuroscience Laboratory;
School of Mathematics,
Trinity College Dublin;
Galway, January 2012
http://www.maths.tcd.ie/~mnl/papers/tcd-mnl-21a.pdf
aa-2
2) Spike sorting on silicon probes
John Schulman
2012
www.eecs.berkeley.edu/~joschu/docs/caton-janelia.pdf
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
http://www.ceremade.dauphine.fr/~peyre/mspc/mspc-mia-12/program/slides/romberg.pdf
G. Technical reports
3) D4.7.2 - Theoretical Neuroscience – Results for SGA1 Period 2
Human Brain Project SGA1, March 2018
https://sos-ch-dk-2.exo.io/public-website-production/filer_public/d8/82/d882880d-653d-4954-ba1b-5ea47d2e5dbb/d472_d252_d33_sga1_m24_accepted_180907.pdf. pvc-5
Jeffrey L Teeters, Friedrich T. Sommer (2013)
http://crcns.org/files/papers/ephdf.pdf
1) HDFds – Conventions to facilitate data sharing using HDF5
Jeffrey L Teeters, Friedrich T. Sommer (2013)
http://crcns.org/files/papers/hdfds.pdf
I. Online resources
Finding spikes in electrophysiological data from neurons
Selina Dasol Kim. 2017.
Wolfram High School Summer Camp project
http://community.wolfram.com/groups/-/m/t/1142260. ac-2.
Spike sorting software which uses data hosted at CRCNS.org to test spike sorting algorithms.
http://xcorr.wordpress.com/2009/10/22/crcns-data-set-pvc-1-by-ringach-lab-getting-something-to-work/
Description of an analysis that was developed and tested using a data set hosted at CRCNS.org.
H. Courses referencing data
7) Neural Signal Processing (UCSD Cogs 118C)
Univ. of California at San Diego
Instructor: Eran Mukamel
https://courses.ucsd.edu/syllabi/SP17/901345.pdf
Taught 2017
6) CoSMo (Summer School in Computational Sensory-Motor Neuroscience)
Northwestern University
Instructor: Konrad Körding
http://klab.smpp.northwestern.edu/wiki/index.php5/CoSMo_2017
Taught 2017
5) Intro to Data Science for Biology
UMASS Boston
Instructor: Jarrett Byrnes
http://byrneslab.net/teaching/data_science/
http://byrneslab.net/classes/data_science/assignments/5_integrating_ggplot_plyr.html
Taught 2016
4) Fundamentals of Computational Neuroscience
Harvard University, Neurobiology
105
Instructors: Alexander Mathis and Ashesh Dhawale
Course description
at:
https://courses.harvard.edu/detail?q=id:d_colgsas_2015_2_1596
Course syllabus at:
https://cluster28-files.instructure.com/files/1875~935854/download?download_frd=1&verifier=o1nlTOko77G74aaOtMXytFugdUysp4LKfes8g9zL&usg=AOvVaw2AlAXi4lwVfw9TOfV-zP1c
3) Advanced Topics in Machine Learning
CalTech, CS/CNS/EE 253
http://courses.cms.caltech.edu/cs253/projects.html
Instructors: Andreas Krause and Daniel Golovin
Taught 2010
2) Statistical analysis of neural data
Columbia University
Instructor: Liam Paninski
http://www.stat.columbia.edu/~liam/teaching/neurostat-spr09/
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
http/crcns.org/course
2018 summer course videos available online
