Citations¶
How to cite SpyKING CIRCUS¶
Note
If you are using SpyKING CIRCUS for your project, please cite us
Yger P., Spampinato, G.L.B, Esposito E., Lefebvre B., Deny S., Gardella C., Stimberg M., Jetter F., Zeck G. Picaud S., Duebel J., Marre O., A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo, eLife 2018;7:e34518
Publications refering to SpyKING CIRCUS¶
Here is a non exhaustive list of papers using SpyKING CIRCUS. Do not hesitate to send us a mail in order update this list, the more the merrier
2020¶
Magland J. et al, SpikeForest: reproducible web-facing ground-truth validation of automated neural spike sorters, bioRxiv, 900688
Park I. Y., et al. Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings, Applied Sciences 10.1 (2020): 301
Cantu D. A., et al. EZcalcium: Open Source Toolbox for Analysis of Calcium Imaging Data, bioRxiv, 893198
2019¶
Frazzini V. et al., In vivo interictal signatures of human periventricular heterotopia, bioRxiv, 816173
Abbasi A et al., Sensorimotor neuronal learning requires cortical topography, bioRxiv 873794
González-Palomares E. et al., Enhanced representation of natural sound sequences in the ventral auditory midbrain, bioRxiv 846485
Chong E. et al., Manipulating synthetic optogenetic odors reveals the coding logic of olfactory perception, bioRxiv 841916
Bolding K. et al., Robust odor coding across states in piriform cortex requires recurrent circuitry: evidence for pattern completion in an associative network, bioRxiv 694331
Szőnyi1 A. et al., Median raphe controls acquisition of negative experience in the mouse, Science Vol 366, Issue 6469
Buccino A. P. and Einevoll G. T., MEArec: a fast and customizable testbench simulator for ground-truth extracellular spiking activity, bioRxiv, 691642
Wouters J., Kloosterman F., et Bertrand, A., SHYBRID: A graphical tool for generating hybrid ground-truth spiking data for evaluating spike sorting performance, bioRxiv, 734061
Boi F., Perentos N., Lecomte A., Schwesig G., Zordan S., Sirota A. et Angotzi, G. N., Multi-shanks SiNAPS Active Pixel Sensor CMOSprobe: 1024 simultaneously recording channels for high-density intracortical brain mapping, bioRxiv, 749911
Weineck K., García-Rosales F. & Hechavarría, J. C., Fronto-striatal oscillations predict vocal output in bats, bioRxiv, 724112
Bolding K. A., Nagappan S., Han B.-X., Wang F., Franks K. M., Pattern recovery by recurrent circuits in piriform cortex, biooRxiv 694331; doi: https://doi.org/10.1101/694331
Reinhard K., Li C., Do Q., Burke E., Heynderickx S., Farrow K.,*A projection specific logic to sampling visual inputs in mouse superior colliculus*, bioRxiv 272914; doi: https://doi.org/10.1101/272914
Fiáth R., et al., Fine-scale mapping of cortical laminar activity during sleep slow oscillations using high-density linear silicon probes, Journal of neuroscience methods 316: 58-70
Heiney K., et al. µSpikeHunter: An advanced computational tool for the analysis of neuronal communication and action potential propagation in microfluidic platforms, Scientific reports 9.1: 5777
Angotzi, Gian Nicola, et al. SiNAPS: An implantable active pixel sensor CMOS-probe for simultaneous large-scale neural recordings, Biosensors and Bioelectronics 126: 355-364.
Williams, Alex H., et al. Discovering precise temporal patterns in large-scale neural recordings through robust and interpretable time warping, bioRxiv: 661165
Hennig, M. H., Hurwitz C., Sorbaro M., Scaling Spike Detection and Sorting for Next-Generation Electrophysiology, In Vitro Neuronal Networks. Springer, Cham 171-184.
Carlson D., and Lawrence C., Continuing progress of spike sorting in the era of big data, Current opinion in neurobiology 55: 90-96
Souza B. C., Lopes-dos-Santos V., Bacelo J., Tort A. B., Spike sorting with Gaussian mixture models, Scientific reports, 9(1), 3627
Gardella C., Marre O., Mora T., Modeling the correlated activity of neural populations: A review, Neural computation, 31(2), 233-269.
Dai J., Zhang P., Sun H., Qiao X., Zhao Y., Ma J., Wang, C., Reliability of motor and sensory neural decoding by threshold crossings for intracortical brain–machine interface, Journal of neural engineering.
Despouy E., Curot J., Denuelle M., Deudon M., Sol J. C., Lotterie J. A., Valton L., Neuronal spiking activity highlights a gradient of epileptogenicity in human tuberous sclerosis lesions, Clinical Neurophysiology, 130(4), 537-547.
Wouters J., Kloosterman F., Bertrand A., A data-driven regularization approach for template matching in spike sorting with high-density neural probes, In Proceedings of IEEE EMBC. IEEE.
Weingärtner S., Chen X., Akçakaya M., Moore T., Robust Online Spike Recovery for High-Density Electrode Recordings using Convolutional Compressed Sensing. In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 1015-1020). IEEE.
Sorochynskyi O., Deny S., Marre O., Ferrari U., From serial to parallel: predicting synchronous firing of large neural populations from sequential recordings, bioRxiv, 560656.
Mahmud, M., Vassanelli, S., Open-Source Tools for Processing and Analysis of In Vitro Extracellular Neuronal Signals. In In Vitro Neuronal Networks (pp. 233-250). Springer, Cham.
Wouters J., Kloosterman F., Bertrand A., Signal-to-peak-interference ratio maximization with automatic interference weighting for threshold-based spike sorting of high-density neural probe data, In International IEEE/EMBS Conference on Neural Engineering:[proceedings]. International IEEE EMBS Conference on Neural Engineering. IEEE.
2018¶
Parikh R., Large-scale neuron cell classification of single-channel and multi-channel extracellularrecordings in the anterior lateral motor cortex, bioRxiv 445700; doi: https://doi.org/10.1101/445700
Macé E., Montaldo G., Trenholm S., Cowan C., rignall A., Urban A., Roska B., Whole-Brain Functional Ultrasound Imaging Reveals Brain Modules for Visuomotor Integration, Neuron, 5:1241-1251
Aydın C., Couto J., Giugliano M., Farrow K., Bonin V., Locomotion modulates specific functional cell types in the mouse visual thalamus, Nature Communications, 4882 (2018)
Belkhiri M., Kvitsiani D., D.sort: template based automatic spike sorting tool, BioRxiv, 10.1101/423913
Nadian M. H., Karimimehr S., Doostmohammadi J., Ghazizadeh A., Lashgari R., A fully automated spike sorting algorithm using t-distributed neighbor embedding and density based clustering, BioRxiv, 10.1101/418913
Ferrari U., Deny S., Chalk M., Tkacik G., Marre O., Mora T, Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons, BioRxiv, 10.1101/243816
Jin M., Beck J. M, Glickfeld L., Neuronal adaptation reveals a suboptimal decoding of orientation tuned populations in the mouse visual cortex, BioRxiv, 10.1101/433722
Jin M., Glickfeld L., Contribution of sensory encoding to measured bias, BioRxiv, 10.1101/444430
Lazarevich I., Prokin I., Gutkin B., Neural activity classification with machine learning models trained on interspike interval series data, arXiv, 1810.03855
Radosevic M., Willumsen A., Petersen P. C., Linden H., Vestergaard M., Berg R. W. Decoupling of timescales reveals sparse convergent CPG network in the adult spinal cord, BiorXiv, 402917
Chaure F, Rey HG, Quian Quiroga R, A novel and fully automatic spike sorting implementation with variable number of features, J Neurophysiol. 10.1152/jn.00339.2018
Ravello C., Perrinet L. U, Escobar M.-J., Palacios A. G, Speed-Selectivity in Retinal Ganglion Cells is Modulated by the Complexity of the Visual Stimulus, BioRxiv, 350330
Wouters J, Kloosterman F., Bertrand A, Towards online spike sorting for high-density neural probes using discriminative template matching with suppression of interfering spikes, Journal of Neural Engineering, 1741-2552
Vilarchao M. E., Estebanez L., Shulz D. E., Férezou I., Supra-barrel Distribution of Directional Tuning for Global Motion in the Mouse Somatosensory Cortex, Cell Reports 22, 3534–3547
Barth A. M., Domonkos A., Fernandez-Ruiz A., Freund T.F., Varga V., Hippocampal Network Dynamics during Rearing Episodes, Cell Reports, 23(6):1706-1715
Steinmetz N. A., Koch C., Harris K.D., Carandini M., Challenges and opportunities for large-scale electrophysiology with Neuropixels probes, Current Opinion in Neurobiology, Volume 50, 92-100
Stern M., Bolding K. A. , Abbott L. F., Franks K. M, A transformation from temporal to ensemble coding in a model of piriform cortex, eLife, 10.7554/eLife.34831
Bolding K. A., Franks K. M. , Recurrent cortical circuits implement concentration-invariant odor coding, Science, 361(6407)
Escobar M.-J., Otero M., Reyes C., Herzog R., Araya J., Ibaceta C., Palacios A. G., Functional Asymmetries between Central and Peripheral Retinal Ganglion Cells in a Diurnal Rodent, BioRxiv, 277814
Wouters J., Kloosterman F., Bertrand A., Data-driven multi-channel filter design with peak-interference suppression for threshold-based spike sorting in high-density neural probes, IEEE International Conference on Acoustics, Speech and Signal processing (ICASSP)
2017¶
Paninski L., Cunningham J., Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience, BioRxiv, 196949
Lee J., Carlson D., Shokri H., Yao W., Goetz G., Hagen E., Batty E., Chichilnisky E.J., Einevoll G., Paninski L., YASS: Yet Another Spike Sorter, BioRxiv, 151928
Shan K. Q., Lubenov E. V., Siapas A. G., Model-based spike sorting with a mixture of drifting t-distributions, Journal of Neuroscience Methods, 288, 82-98
Deny S., Ferrari U., Mace E., Yger P., Caplette R., Picaud S., Tkacik G., Marre O., Multiplexed computations in retinal ganglion cells of a single type, Nature Communications 10.1038/s41467-017-02159-y
Chung, J. E., Magland, J. F., Barnett, A. H., Tolosa, V. M., Tooker, A. C., Lee, K. Y., … & Greengard, L. F. A Fully Automated Approach to Spike Sorting, Neuron, 95(6), 1381-1394
Mena, G. E., Grosberg, L. E., Madugula, S., Hottowy, P., Litke, A., Cunningham, J., … & Paninski, L. Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays, PLOS Computational Biology, 13(11), e1005842.
Mokri Y., Salazar R.F, Goodell2 B., Baker J., Gray C.M. and Yen S., Sorting Overlapping Spike Waveforms from Electrode and Tetrode Recordings, Front. Neuroinform.
Wilson C.D., Serrano G. O., Koulakov A. A., Rinberg D., A primacy code for odor identity, Nature Communication, 1477
Ferrari U., Gardella C., Marre O., Mora T., Closed-loop estimation of retinal network sensitivity reveals signature of efficient coding, eNeuro, ENEURO.0166-17.2017
Denman, D. J., Siegle, J. H., Koch, C., Reid, R. C., & Blanche, T. J. Spatial organization of chromatic pathways in the mouse dorsal lateral geniculate nucleus, Journal of Neuroscience, 37(5), 1102-1116.
2016¶
Dimitriadis, G., Neto, J., & Kampff, A. T-SNE visualization of large-scale neural recordings, bioRxiv, 087395.
Yger P., Spampinato, G.L.B, Esposito E., Lefebvre B., Deny S., Gardella C., Stimberg M., Jetter F., Zeck G. Picaud S., Duebel J., Marre O., Fast and accurate spike sorting in vitro and in vivo for up to thousands of electrodes, bioRxiv, 67843