K. Deisseroth, Circuit dynamics of adaptive and maladaptive behaviour, Nature, vol.505, p.24429629, 2014.
DOI : 10.1038/nature12982

URL : http://europepmc.org/articles/pmc4069282?pdf=render

K. D. Harris, Neural signatures of cell assembly organization, Nat Rev Neurosci, vol.6, p.15861182, 2005.
DOI : 10.1038/nrn1669

K. L. Briggman and W. B. Kristan, Multifunctional pattern-generating circuits, Annu Rev Neurosci, vol.31, p.18558856, 2008.
DOI : 10.1146/annurev.neuro.31.060407.125552

D. J. Wallace and J. N. Kerr, Chasing the cell assembly, Curr Opin Neurobiol. Elsevier Ltd, vol.20, p.20570133, 2010.
DOI : 10.1016/j.conb.2010.05.003

K. Ohki, S. Chung, Y. H. Ch'ng, P. Kara, and R. C. Reid, Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex, Nature, vol.433, p.15660108, 2005.

M. B. Ahrens, P. J. Keller, M. B. Orger, D. N. Robson, J. M. Li et al., Whole-brain functional imaging at cellular resolution using light-sheet microscopy, Nat Methods, vol.10, p.23524393, 2013.
DOI : 10.1038/nmeth.2434

T. Panier, S. A. Romano, R. Olive, T. Pietri, G. Sumbre et al., Fast functional imaging of multiple brain regions in intact zebrafish larvae using selective plane illumination microscopy. Front Neural Circuits, Frontiers Media {SA}, vol.7, p.23576959, 2013.
URL : https://hal.archives-ouvertes.fr/inserm-00842318

R. Portugues, C. E. Feierstein, F. Engert, and M. B. Orger, Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior, Neuron, vol.81, p.24656252, 2014.
DOI : 10.1016/j.neuron.2014.01.019

URL : https://doi.org/10.1016/j.neuron.2014.01.019

S. Romano, T. Pietri, V. Pérez-schuster, A. Jouary, M. Haudrechy et al., Spontaneous Neuronal Network Dynamics Reveal Circuit's Functional Adaptations for Behavior, Neuron, vol.85, p.25704948, 2015.
DOI : 10.1016/j.neuron.2015.01.027

URL : https://doi.org/10.1016/j.neuron.2015.01.027

R. Candelier, M. S. Murmu, S. A. Romano, A. Jouary, G. Debrégeas et al., A microfluidic device to study neuronal and motor responses to acute chemical stimuli in zebrafish. Sci Rep, Nature Publishing Group, vol.5, p.12196, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01223347

S. P. Peron, J. Freeman, V. Iyer, C. Guo, and K. Svoboda, A Cellular Resolution Map of Barrel Cortex Activity during Tactile Behavior, Neuron, vol.86, p.25913859, 2015.

S. Peron, T. Chen, and K. Svoboda, Comprehensive imaging of cortical networks, Curr Opin Neurobiol, vol.32, p.25880117, 2015.

K. D. Harris, R. Q. Quiroga, J. Freeman, and S. L. Smith, Improving data quality in neuronal population recordings, Nat Neurosci, vol.19, p.27571195, 2016.
DOI : 10.1038/nn.4365

URL : http://europepmc.org/articles/pmc5244825?pdf=render

E. A. Pnevmatikakis, D. Soudry, Y. Gao, T. A. Machado, J. Merel et al., Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data, Neuron, vol.89, p.26774160, 2016.
DOI : 10.1016/j.neuron.2015.11.037

URL : https://doi.org/10.1016/j.neuron.2015.11.037

J. Tomek, O. Novak, and J. Syka, Two-Photon Processor and SeNeCA: a freely available software package to process data from two-photon calcium imaging at speeds down to several milliseconds per frame, J Neurophysiol, vol.110, p.23576700, 2013.
DOI : 10.1152/jn.00087.2013

P. Kaifosh, J. D. Zaremba, N. B. Danielson, and L. A. Sima, Python software for analysis of dynamic fluorescence imaging data, Front Neuroinform, vol.8, p.25295002, 2014.
DOI : 10.3389/fninf.2014.00080

URL : https://www.frontiersin.org/articles/10.3389/fninf.2014.00080/pdf

J. P. Cunningham and B. M. Yu, Dimensionality reduction for large-scale neural recordings, Nat Neurosci, vol.17, p.25151264, 2014.
DOI : 10.1038/nn.3776

URL : http://europepmc.org/articles/pmc4433019?pdf=render

J. Niessing and R. W. Friedrich, Olfactory pattern classification by discrete neuronal network states, Nature, vol.465, p.20393466, 2010.
DOI : 10.1038/nature08961

O. Mazor and G. Laurent, Transient dynamics versus fixed points in odor representations by locust antennal lobe projection neurons, Neuron, vol.48, p.16301181, 2005.
DOI : 10.1016/j.neuron.2005.09.032

URL : https://doi.org/10.1016/j.neuron.2005.09.032

V. Mante, D. Sussillo, K. Shenoy, and W. T. Newsome, Context-dependent computation by recurrent dynamics in prefrontal cortex, Nature. Nature Publishing Group, vol.503, p.24201281, 2013.
DOI : 10.1038/nature12742

URL : http://europepmc.org/articles/pmc4121670?pdf=render

A. Peyrache, M. Khamassi, K. Benchenane, S. I. Wiener, and F. P. Battaglia, Replay of rule-learning related neural patterns in the prefrontal cortex during sleep, Nat Neurosci. Nature Publishing Group, vol.12, p.19483687, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00551868

K. Benchenane, A. Peyrache, M. Khamassi, P. L. Tierney, Y. Gioanni et al., Coherent Theta Oscillations and Reorganization of Spike Timing in the Hippocampal-Prefrontal Network upon Learning, Neuron. Elsevier Ltd, vol.66, p.20620877, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00554482

V. Lopes-dos-santos, S. Conde-ocazionez, M. Nicolelis, S. T. Ribeiro, and A. Tort, Neuronal assembly detection and cell membership specification by principal component analysis, PLoS One, vol.6, p.21698248, 2011.

A. Pouget, P. Dayan, and R. Zemel, Information processing with population codes, Nat Rev Neurosci, vol.1, p.11252775, 2000.
DOI : 10.1038/35039062

A. W. Thompson, G. C. Vanwalleghem, L. A. Heap, and E. K. Scott, Functional Profiles of Visual-, Auditory-, and Water Flow-Responsive Neurons in the Zebrafish Tectum, Curr Biol. Elsevier Ltd, vol.26, p.26923787, 2016.

V. Pérez-schuster, A. Kulkarni, M. Nouvian, S. A. Romano, K. Lygdas et al., Sustained Rhythmic Brain Activity Underlies Visual Motion Perception in Zebrafish, Cell Rep, vol.17, p.27760314, 2016.

S. A. Romano, V. Pérez-schuster, A. Jouary, A. Candeo, J. Boulanger-weill et al., A computational toolbox and step-by-step tutorial for the analysis of neuronal population dynamics in calcium imaging data, bioRxiv, 2017.

T. Chen, T. J. Wardill, Y. Sun, S. R. Pulver, S. L. Renninger et al., Ultrasensitive fluorescent proteins for imaging neuronal activity, Nature, vol.499, p.23868258, 2013.
DOI : 10.1038/nature12354

URL : http://europepmc.org/articles/pmc3777791?pdf=render

N. Ji, T. R. Sato, and E. Betzig, Characterization and adaptive optical correction of aberrations during in vivo imaging in the mouse cortex, Proc Natl Acad Sci, vol.109, p.22190489, 2012.

E. Yaksi and R. W. Friedrich, Reconstruction of firing rate changes across neuronal populations by temporally deconvolved Ca2+ imaging, Nat Methods, vol.3, p.16628208, 2006.
DOI : 10.1038/nmeth874

J. T. Vogelstein, A. M. Packer, . Machado-t-a, T. Sippy, B. Babadi et al., Fast nonnegative deconvolution for spike train inference from population calcium imaging, J Neurophysiol, vol.104, p.20554834, 2010.
DOI : 10.1152/jn.01073.2009

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3007657/pdf

A. J. Izenman, Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. Springer Texts in Statistics, 2008.

H. Trevor, T. Robert, and J. Friedman, The Elements of Statistical Learning The Elements of Statistical Learning Data Mining, Inference, and Prediction, Second Edition. Springer series in statistics, 2009.

A. Peyrache, K. Benchenane, M. Khamassi, S. I. Wiener, and F. P. Battaglia, Principal component analysis of ensemble recordings reveals cell assemblies at high temporal resolution, J Comput Neurosci, vol.29, p.19529888, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00551873

A. Hendrickson and P. White, Promax: A quick method for rotation to oblique simple structure, Br J Stat, vol.17, pp.65-70, 1964.

C. A. Tracy and H. Widom, Level-Spacing Distributions and the Airy Kernel, Commun Math Phys, vol.159, p.35, 1992.

B. Scholl, J. J. Pattadkal, G. A. Dilly, N. J. Priebe, and B. V. Zemelman, Local Integration Accounts for Weak Selectivity of Mouse Neocortical Parvalbumin Interneurons, Neuron, vol.87, p.26182423, 2015.

Z. Guo, N. Li, D. Huber, E. Ophir, D. Gutnisky et al., Flow of cortical activity underlying a tactile decision in mice, Neuron. Elsevier Inc, vol.81, p.24361077, 2014.

, Svoboda (contact) K. Simultaneous imaging and looseseal cell-attached electrical recordings from neurons expressing a variety of genetically encoded calcium indicators, GENIE project Janelia Farm Campus HHMI, 2015.

L. Theis, P. Berens, E. Froudarakis, J. Reimer, R. Rosón et al., Benchmarking Spike Rate Inference in Population Calcium Imaging, Neuron, vol.90, p.27151639, 2016.

J. D. Burrill and S. S. Easter, Development of the retinofugal projections in the embryonic and larval zebrafish (Brachydanio rerio), J Comp Neurol, vol.346, p.7983245, 1994.

C. M. Niell and S. J. Smith, Functional imaging reveals rapid development of visual response properties in the zebrafish tectum, Neuron, vol.45, p.15797554, 2005.

E. A. Naumann, J. E. Fitzgerald, T. W. Dunn, J. Rihel, H. Sompolinsky et al., From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response, Cell, vol.167, p.27814522, 2016.

O. Randlett, C. L. Wee, E. A. Naumann, O. Nnaemeka, D. Schoppik et al., Whole-brain activity mapping onto a zebrafish brain atlas, Nat Methods. Nature Publishing Group, vol.12, p.26778924, 2015.

Y. Gong, C. Huang, J. Z. Li, B. F. Grewe, Y. Zhang et al., High-speed recording of neural spikes in awake mice and flies with a fluorescent voltage sensor, Science, vol.350, p.26586188, 2015.

C. Kibat, S. Krishnan, M. Ramaswamy, B. J. Baker, and S. Jesuthasan, Imaging voltage in zebrafish as a route to characterizing a vertebrate functional connectome: promises and pitfalls of genetically encoded indicators, J Neurogenet, vol.30, p.27328843, 2016.

M. Stopfer, V. Jayaraman, and G. Laurent, Intensity versus identity coding in an olfactory system, Neuron, vol.39, p.12971898, 2003.

S. T. Roweis and L. K. Saul, Nonlinear dimensionality reduction by locally linear embedding, Science, vol.290, p.11125150, 2000.

M. D. Humphries, Spike-Train Communities: Finding Groups of Similar Spike Trains, J Neurosci, vol.31, p.21307268, 2011.