How to cite SpyKING CIRCUS


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


  • Kajiwara, M. et al, Inhibitory neurons exhibit high controlling ability in the cortical microconnectome, PLOS Computational Biology, 17(4), e1008846
  • Sans-Dublanc, A. et al, Optogenetic fUSI for brain-wide mapping of neural activity mediating collicular-dependent behaviors, Neuron
  • Passaro, A. P. et Stice, S. L., Electrophysiological analysis of brain organoids: current approaches and advancements, Frontiers in Neuroscience, 14, 1405.
  • Li, W. et al, A facile and comprehensive algorithm for electrical response identification in mouse retinal ganglion cells, Plos one, 16(3), e0246547.
  • Araya, J. et al, Retinal Ganglion Cells Functional Changes in a Mouse Model of Alzheimer’s Disease Are Linked with Neurotransmitter Alterations, Journal of Alzheimer’s Disease, (Preprint), 1-14.
  • Rokai, J. et al, ELVISort: encoding latent variables for instant sorting, an artificial intelligence-based end-to-end solution, Journal of Neural Engineering, 18(4), 046033.
  • Provansal, M. et al Functional ultrasound imaging of the spreading activity following optogenetic stimulation of the rat visual cortex, bioRxiv.
  • Saif-ur-Rehman, M. et al, SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm, Journal of Neural Engineering, 18(1), 016009.
  • Sedaghat-Nejad, E. et al, P-sort: an open-source software for cerebellar neurophysiology, bioRxiv.
  • Perez-Ortega, J. E. et al, Parallel processing of natural images by overlapping retinal neuronal ensembles, bioRxiv.
  • Hall, N. J. et al, Evaluation and resolution of many challenges of neural spike-sorting: a new sorter, bioRxiv.
  • Sorochynskyi, O. et al, Predicting synchronous firing of large neural populations from sequential recordings, PLoS computational biology, 17(1), e1008501.
  • Malfatti, T. et al, Activity of CaMKIIa+ dorsal cochlear nucleus neurons are crucial for tinnitus perception but not for tinnitus induction, bioRxiv.
  • Waschke, L. et al, Behavior needs neural variability, Neuron.
  • Chen, Z. S. et Pesaran, B., Improving scalability in systems neuroscience, Neuron.


  • Hudetz, A. G. et al, Desflurane Anesthesia Alters Cortical Layer–specific Hierarchical Interactions in Rat Cerebral Cortex, Anesthesiology: The Journal of the American Society of Anesthesiologists, 132(5), 1080-1090.
  • Morningstar M. D. et al, Ethanol Alters Variability, But Not Rate, of Firing in Medial Prefrontal Cortex Neurons of Awake‐Behaving Rats, Alcoholism: Clinical and Experimental Research, 44(11), 2225-2238.
  • Zhang Z. et al, Network Dynamics in the Developing Piriform Cortex of Unanesthetized Rats, Cerebral Cortex.
  • Lee H. et al, Differential effect of anesthesia on visual cortex neurons with diverse population coupling. Neuroscience.
  • García-Rosales F., et al, Fronto-Temporal Coupling Dynamics During Spontaneous Activity and Auditory Processing in the Bat Carollia perspicillata, Frontiers in systems neuroscience, 14, 14.
  • Weineck K et al, Neural oscillations in the fronto-striatal network predict vocal output in bats, PLoS biology, 18(3), e3000658.
  • Lee H. et al, State-dependent cortical unit activity reflects dynamic brain state transitions in anesthesia, Journal of Neuroscience, 40(49), 9440-9454.
  • Bolding K. A. et al, Recurrent circuitry is required to stabilize piriform cortex odor representations across brain states, Elife, 9, e53125.
  • Jin M. et al, Mouse higher visual areas provide both distributed and discrete contributions to visually guided behaviors, bioRxiv, 001446
  • Petersen P. C, et al., CellExplorer: a graphical user interface and standardized pipeline for visualizing and characterizing single neuron features, bioRxiv, 083436
  • Kajiwara al., Inhibitory neurons are a Central Controlling regulator in the effective cortical microconnectome, bioRxiv, 954016
  • 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
  • Estabanze L. et al., Sensorimotor neuronal learning requires cortical topography, bioRxiv, 873794
  • 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
  • Yuan et al., Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level, bioRXiv, 071787
  • Buccino A. P. et al., SpikeInterface, a unified framework for spike sorting, bioRxiv, 796599
  • García-Rosales F. et al., Fronto-temporal coupling dynamics during spontaneous activity and auditory processing, bioRxiv, 886770


  • 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:
  • 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:
  • 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.


  • Parikh R., Large-scale neuron cell classification of single-channel and multi-channel extracellularrecordings in the anterior lateral motor cortex, bioRxiv 445700; doi:
  • 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)


  • 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.


  • 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