references.bib

---
---

% burst detection
@article{cotterill2019burst,
  title={Burst detection methods},
  author={Cotterill, Ellese and Eglen, Stephen J},
  journal={In Vitro Neuronal Networks: From Culturing Methods to Neuro-Technological Applications},
  pages={185--206},
  year={2019},
  publisher={Springer}
}

@article{bakkum2014parameters,
  title={Parameters for burst detection},
  author={Bakkum, Douglas J and Radivojevic, Milos and Frey, Urs and Franke, Felix and Hierlemann, Andreas and Takahashi, Hirokazu},
  journal={Frontiers in computational neuroscience},
  volume={7},
  pages={193},
  year={2014},
  publisher={Frontiers Media SA}
}

@article{Chiappalone2005,
abstract = {Cortical neurons extracted from the developing rat central nervous system and put in culture, show, after a few days, spontaneous activity with a typical electrophysiological pattern ranging from stochastic spiking to synchronized bursting. Using microelectrode arrays (MEA), on which dissociated cultures can be grown for long-term measurements, we recorded the electrophysiological activity of cortical networks during development, in order to monitor their responses at different stages of the maturation process. Employing algorithms for detection and analysis of bursts in single-channel spike trains and of synchronized network bursts in multi-channel spike trains, significant changes have been revealed in the firing dynamics at different stages of the developmental process. {\textcopyright} 2004 Elsevier B.V. All rights reserved.},
author = {Chiappalone, Michela and Novellino, A. and Vajda, I. and Vato, A. and Martinoia, S. and van Pelt, J.},
doi = {10.1016/j.neucom.2004.10.094},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/Chiappalone et al. - 2005 - Burst detection algorithms for the analysis of spatio-temporal patterns in cortical networks of neurons.pdf:pdf},
issn = {09252312},
journal = {Neurocomputing},
keywords = {Burst detection,Development,Microelectrode array,Network burst,Neuronal networks},
mendeley-groups = {Computational Models/Bursts},
number = {SPEC. ISS.},
pages = {653--662},
title = {{Burst detection algorithms for the analysis of spatio-temporal patterns in cortical networks of neurons}},
volume = {65-66},
year = {2005}
}

@article{Wagenaar2006,
abstract = {Background: We have collected a comprehensive set of multi-unit data on dissociated cortical cultures. Previous studies of the development of the electrical activity of dissociated cultures of cortical neurons each focused on limited aspects of its dynamics, and were often based on small numbers of observed cultures. We followed 58 cultures of different densities - 3000 to 50,000 neurons on areas of 30 to 75 mm2 - growing on multi-electrode arrays (MEAs) during the first five weeks of their development. Results: Plating density had a profound effect on development. While the aggregate spike detection rate scaled linearly with density, as expected from the number of cells in proximity to electrodes, dense cultures started to exhibit bursting behavior earlier in development than sparser cultures. Analysis of responses to electrical stimulation suggests that axonal outgrowth likewise occurred faster in dense cultures. After two weeks, the network activity was dominated by population bursts in most cultures. In contrast to previous reports, development continued with changing burst patterns throughout the observation period. Burst patterns were extremely varied, with inter-burst intervals between 1 and 300 s, different amounts of temporal clustering of bursts, and different firing rate profiles during bursts. During certain stages of development bursts were organized into tight clusters with highly conserved internal structure. Conclusion: Dissociated cultures of cortical cells exhibited a much richer repertoire of activity patterns than previously reported. Except for the very sparsest cultures, all cultures exhibited globally synchronized bursts, but bursting patterns changed over the course of development, and varied considerably between preparations. This emphasizes the importance of using multiple preparations - not just multiple cultures from one preparation - in any study involving neuronal cultures. {\textcopyright} 2006 Wagenaar et al; licensee BioMed Central Ltd.},
author = {Wagenaar, Daniel A. and Pine, Jerome and Potter, Steve M.},
doi = {10.1186/1471-2202-7-11},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/Wagenaar, Pine, Potter - 2006 - An extremely rich repertoire of bursting patterns during the development of cortical cultures(2).pdf:pdf},
issn = {14712202},
journal = {BMC Neuroscience},
mendeley-groups = {Computational Models/Bursts},
pages = {1--18},
pmid = {16464257},
title = {{An extremely rich repertoire of bursting patterns during the development of cortical cultures}},
volume = {7},
year = {2006}
}

% cross-correlation
@article{sporns2004organization,
  title={Organization, development and function of complex brain networks},
  author={Sporns, Olaf and Chialvo, Dante R and Kaiser, Marcus and Hilgetag, Claus C},
  journal={Trends in cognitive sciences},
  volume={8},
  number={9},
  pages={418--425},
  year={2004},
  publisher={Elsevier}
}

@article{Spivak2022,
abstract = {Accurate detection and quantification of spike transmission between neurons is essential for determining neural network mechanisms that govern cognitive functions. Using point process and conductance-based simulations, we found that existing methods for determining neuronal connectivity from spike times are highly affected by burst spiking activity, resulting in over- or underestimation of spike transmission. To improve performance, we developed a mathematical framework for decomposing the cross-correlation between two spike trains. We then devised a deconvolution-based algorithm for removing effects of second-order spike train statistics. Deconvolution removed the effect of burst spiking, improving the estimation of neuronal connectivity yielded by state-of-the-art methods. Application of deconvolution to neuronal data recorded from hippocampal region CA1 of freely-moving mice produced higher estimates of spike transmission, in particular when spike trains exhibited bursts. Deconvolution facilitates the precise construction of complex connectivity maps, opening the door to enhanced understanding of the neural mechanisms underlying brain function.},
author = {Spivak, Lidor and Levi, Amir and Sloin, Hadas E. and Someck, Shirly and Stark, Eran},
doi = {10.1038/s42003-022-03450-5},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/Spivak et al. - 2022 - Deconvolution improves the detection and quantification of spike transmission gain from spike trains.pdf:pdf},
issn = {23993642},
journal = {Communications Biology},
mendeley-groups = {Spike Train Analysis/Spike Sorting},
number = {1},
pages = {1--17},
pmid = {35641587},
publisher = {Springer US},
title = {{Deconvolution improves the detection and quantification of spike transmission gain from spike trains}},
volume = {5},
year = {2022}
}

@article{Kobayashi2019,
author = {Kobayashi, Ryota and Kurita, Shuhei and Kurth, Anno and Kitano, Katsunori and Mizuseki, Kenji and Diesmann, Markus and Richmond, Barry J. and Shinomoto, Shigeru},
doi = {10.1038/s41467-019-12225-2},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/Kobayashi et al. - 2019 - Reconstructing neuronal circuitry from parallel spike trains.pdf:pdf},
issn = {2041-1723},
journal = {Nature Communications},
mendeley-groups = {Computational Models/Connectivity,ML/Stats/AI/Point Process},
month = {dec},
number = {1},
pages = {4468},
title = {{Reconstructing neuronal circuitry from parallel spike trains}},
volume = {10},
year = {2019}
}

@article{English2017,
abstract = {Excitatory control of inhibitory neurons is poorly understood due to the difficulty of studying synaptic connectivity in vivo. We inferred such connectivity through analysis of spike timing and validated this inference using juxtacellular and optogenetic control of presynaptic spikes in behaving mice. We observed that neighboring CA1 neurons had stronger connections and that superficial pyramidal cells projected more to deep interneurons. Connection probability and strength were skewed, with a minority of highly connected hubs. Divergent presynaptic connections led to synchrony between interneurons. Synchrony of convergent presynaptic inputs boosted postsynaptic drive. Presynaptic firing frequency was read out by postsynaptic neurons through short-term depression and facilitation, with individual pyramidal cells and interneurons displaying a diversity of spike transmission filters. Additionally, spike transmission was strongly modulated by prior spike timing of the postsynaptic cell. These results bridge anatomical structure with physiological function. English, McKenzie, et al. identify, validate, and quantify monosynaptic connections between pyramidal cells and interneurons, using the spike timing of pre- and postsynaptic neurons in vivo. Their large-scale method uncovers a backbone of connectivity rules in the hippocampus CA1 circuit.},
author = {English, Daniel Fine and McKenzie, Sam and Evans, Talfan and Kim, Kanghwan and Yoon, Euisik and Buzs{\'{a}}ki, Gy{\"{o}}rgy},
doi = {10.1016/j.neuron.2017.09.033},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/English et al. - 2017 - Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks.pdf:pdf},
issn = {10974199},
journal = {Neuron},
keywords = {cell assemblies,circuits,cooperativity,hippocampus,interneuron,lognormal,pyramidal cell,short-term plasticity,spike transmission,synchrony},
mendeley-groups = {Computational Models/Connectivity},
number = {2},
pages = {505--520.e7},
pmid = {29024669},
title = {{Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks}},
volume = {96},
year = {2017}
}

% spike detection
@article{Lee2020,
abstract = {Spike sorting is a critical first step in extracting neural signals from large-scale multi-electrode array (MEA) data. This manuscript presents several new techniques that make MEA spike sorting more robust and accurate. Our pipeline is based on an efficient multi-stage “triage-then-cluster-then-pursuit” approach that initially extracts only clean, high-quality waveforms from the electrophysiological time series by temporarily skipping noisy or “collided” events (representing two neurons firing synchronously). This is accomplished by developing a neural network detection and denoising method followed by efficient outlier triaging. The denoised spike waveforms are then used to infer the set of spike templates through nonparametric Bayesian clustering. We use a divide-and-conquer strategy to parallelize this clustering step. Finally, we recover collided waveforms with matching-pursuit deconvolution techniques, and perform further split-and-merge steps to estimate additional templates from the pool of recovered waveforms. We apply the new pipeline to data recorded in the primate retina, where high firing rates and highly-overlapping axonal units provide a challenging testbed for the deconvolution approach; in addition, the well-defined mosaic structure of receptive fields in this preparation provides a useful quality check on any spike sorting pipeline. We show that our pipeline improves on the state-of-the-art in spike sorting (and outperforms manual sorting) on both real and semi-simulated MEA data with > 500 electrodes; open source code can be found at <https://github.com/paninski-lab/yass>.},
author = {Lee, Jin Hyung and Mitelut, Catalin and Shokri, Hooshmand and Kinsella, Ian and Dethe, Nishchal and Wu, Shenghao and Li, Kevin and Reyes, Eduardo Blancas and Turcu, Denis and Batty, Eleanor and Kim, Young Joon and Brackbill, Nora and Kling, Alexandra and Goetz, Georges and Chichilnisky, E. J. and Carlson, David and Paninski, Liam},
doi = {10.1101/2020.03.18.997924},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/Lee et al. - 2020 - YASS Yet another spike sorter applied to large-scale multi-electrode array recordings in primate retina.pdf:pdf},
issn = {2692-8205},
journal = {bioRxiv},
mendeley-groups = {Spike Train Analysis/Spike Sorting},
pages = {1--46},
title = {{YASS: Yet another spike sorter applied to large-scale multi-electrode array recordings in primate retina}},
year = {2020}
}

@article{Laboy-Juarez2019,
abstract = {Spike sorting is the process of detecting and clustering action potential waveforms of putative single neurons from extracellular voltage recordings. Typically, spike detection uses a fixed voltage threshold and shadow period, but this approach often misses spikes during high firing rate epochs or noisy conditions. We developed a simple, data-driven spike detection method using a scaled form of template matching, based on the sliding cosine similarity between the extracellular voltage signal and mean spike waveforms of candidate single units. Performance was tested in whisker somatosensory cortex (S1) of anesthetized mice in vivo. The method consistently detected whisker-evoked spikes that were missed by the standard fixed threshold. Detection was improved most for spikes evoked by strong stimuli (40–70% increase), improved less for weaker stimuli, and unchanged for spontaneous spiking. This represents improved detection during spatiotemporally dense spiking, and yielded sharper sensory tuning estimates. We also benchmarked performance using computationally generated voltage data. Template matching detected $\sim$85–90% of spikes compared to $\sim$70% for the standard fixed threshold method, and was more tolerant to high firing rates and simulated recording noise. Thus, a simple template matching approach substantially improves detection of single-unit spiking for cortical physiology.},
author = {Laboy-Ju{\'{a}}rez, Keven J. and Ahn, Seoiyoung and Feldman, Daniel E.},
doi = {10.1038/s41598-019-48456-y},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/Laboy-Ju{\'{a}}rez, Ahn, Feldman - 2019 - A normalized template matching method for improving spike detection in extracellular voltage record.pdf:pdf},
issn = {20452322},
journal = {Scientific Reports},
mendeley-groups = {Spike Train Analysis/Spike Sorting},
number = {1},
pages = {1--12},
pmid = {31427615},
title = {{A normalized template matching method for improving spike detection in extracellular voltage recordings}},
volume = {9},
year = {2019}
}

% spike sorting
@article{muller2015high,
  title={High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels},
  author={M{\"u}ller, Jan and Ballini, Marco and Livi, Paolo and Chen, Yihui and Radivojevic, Milos and Shadmani, Amir and Viswam, Vijay and Jones, Ian L and Fiscella, Michele and Diggelmann, Roland and others},
  journal={Lab on a Chip},
  volume={15},
  number={13},
  pages={2767--2780},
  year={2015},
  publisher={Royal Society of Chemistry}
}

@misc{Rey2015,
abstract = {Spike sorting is a crucial step to extract information from extracellular recordings. With new recording opportunities provided by the development of new electrodes that allow monitoring hundreds of neurons simultaneously, the scenario for the new generation of algorithms is both exciting and challenging. However, this will require a new approach to the problem and the development of a common reference framework to quickly assess the performance of new algorithms. In this work, we review the basic concepts of spike sorting, including the requirements for different applications, together with the problems faced by presently available algorithms. We conclude by proposing a roadmap stressing the crucial points to be addressed to support the neuroscientific research of the near future.},
author = {Rey, Hernan Gonzalo and Pedreira, Carlos and {Quian Quiroga}, Rodrigo},
booktitle = {Brain Research Bulletin},
doi = {10.1016/j.brainresbull.2015.04.007},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/Rey, Pedreira, Quian Quiroga - 2015 - Past, present and future of spike sorting techniques.pdf:pdf},
issn = {18732747},
keywords = {Extracellular recordings,Modeling,Multielectrode recordings,On-chip applications,Spike sorting},
mendeley-groups = {Spike Train Analysis/Spike Sorting},
month = {oct},
pages = {106--117},
publisher = {Elsevier Inc.},
title = {{Past, present and future of spike sorting techniques}},
volume = {119},
year = {2015}
}

@misc{Carlson2019,
abstract = {Engineering efforts are currently attempting to build devices capable of collecting neural activity from one million neurons in the brain. Part of this effort focuses on developing dense multiple-electrode arrays, which require post-processing via ‘spike sorting' to extract neural spike trains from the raw signal. Gathering information at this scale will facilitate fascinating science, but these dreams are only realizable if the spike sorting procedure and data pipeline are computationally scalable, at or superior to hand processing, and scientifically reproducible. These challenges are all being amplified as the data scale continues to increase. In this review, recent efforts to attack these challenges are discussed, which have primarily focused on increasing accuracy and reliability while being computationally scalable. These goals are addressed by adding additional stages to the data processing pipeline and using divide-and-conquer algorithmic approaches. These recent developments should prove useful to most research groups regardless of data scale, not just for cutting-edge devices.},
author = {Carlson, David and Carin, Lawrence},
booktitle = {Current Opinion in Neurobiology},
doi = {10.1016/j.conb.2019.02.007},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/Carlson, Carin - 2019 - Continuing progress of spike sorting in the era of big data.pdf:pdf},
issn = {18736882},
mendeley-groups = {Spike Train Analysis/Spike Sorting},
month = {apr},
pages = {90--96},
publisher = {Elsevier Ltd},
title = {{Continuing progress of spike sorting in the era of big data}},
volume = {55},
year = {2019}
}

@article{Buccino2022,
author = {Buccino, Alessio P and Garcia, Samuel and Yger, Pierre},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/Buccino, Garcia, Yger - 2022 - Spike sorting new trends and challenges of the era of high-density probes Progress in Biomedical Enginee.pdf:pdf},
journal = {Progress in Biomedical Engineering},
mendeley-groups = {Spike Train Analysis/Spike Sorting},
pages = {0--15},
title = {{Spike sorting : new trends and challenges of the era of high-density probes}},
year = {2022}
}

@article{Takekawa2010,
abstract = {Simultaneous recordings with multi-channel electrodes are widely used for studying how multiple neurons are recruited for information processing. The recorded signals contain the spike events of a number of adjacent or distant neurons and must be sorted correctly into spike trains of individual neurons. Several mathematical methods have been proposed for spike sorting but the process is difficult in practice, as extracellularly recorded signals are corrupted by biological noise. Moreover, spike sorting is often time-consuming, as it usually requires corrections by human operators. Methods are needed to obtain reliable spike clusters without heavy manual operation. Here, we introduce several methods of spike sorting and compare the accuracy and robustness of their performance by using publicized data of simultaneous extracellular and intracellular recordings of neuronal activity. The best and excellent performance was obtained when a newly proposed filter for spike detection was combined with the wavelet transform and variational Bayes for a finite mixture of Student's t-distributions, namely, robust variational Bayes. Wavelet transform extracts features that are characteristic of the detected spike waveforms and the robust variational Bayes categorizes the extracted features into clusters corresponding to spikes of the individual neurons. The use of Student's t-distributions makes this categorization robust against noisy data points. Some other new methods also exhibited reasonably good performance. We implemented all of the proposed methods in a C++ code named 'EToS' (Efficient Technology of Spike sorting), which is freely available on the Internet. {\textcopyright} 2010 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.},
annote = {おそらくetosの元論文。etosを使う場合には理解しておいた方が良い。というかフィルタの原理を知っておくには重要である。},
author = {Takekawa, Takashi and Isomura, Yoshikazu and Fukai, Tomoki},
doi = {10.1111/j.1460-9568.2009.07068.x},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/Takekawa, Isomura, Fukai - 2010 - Accurate spike sorting for multi-unit recordings(2).pdf:pdf},
issn = {0953816X},
journal = {European Journal of Neuroscience},
keywords = {Cell assembly,Clustering,Efficient Technology of Spike sorting open-source,Robust variational Bayes,Wavelet transform},
mendeley-groups = {Spike Train Analysis/Spike Sorting},
number = {2},
pages = {263--272},
pmid = {20074217},
title = {{Accurate spike sorting for multi-unit recordings}},
volume = {31},
year = {2010}
}

@article{Chung2017,
abstract = {Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible. Chung, Magland, et al. present MountainSort, a new fully automatic spike sorting package with a powerful GUI. MountainSort has accuracy comparable to current methods and runtimes faster than real time, enabling automatic and reproducible spike sorting for high-density extracellular recordings.},
author = {Chung, Jason E. and Magland, Jeremy F. and Barnett, Alex H. and Tolosa, Vanessa M. and Tooker, Angela C. and Lee, Kye Y. and Shah, Kedar G. and Felix, Sarah H. and Frank, Loren M. and Greengard, Leslie F.},
doi = {10.1016/j.neuron.2017.08.030},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/Chung et al. - 2017 - A Fully Automated Approach to Spike Sorting.pdf:pdf},
issn = {10974199},
journal = {Neuron},
keywords = {automated,cluster metrics,clustering,cortex,hippocampus,reproducibility,spike sorting},
mendeley-groups = {Spike Train Analysis/Spike Sorting},
month = {sep},
number = {6},
pages = {1381--1394.e6},
publisher = {Cell Press},
title = {{A Fully Automated Approach to Spike Sorting}},
volume = {95},
year = {2017}
}

@article{Kleinfeld2018,
abstract = {In recent years, multielectrode arrays and large silicon probes have been developed to record simultaneously between hundreds and thousands of electrodes packed with a high density. However, they require novel methods to extract the spiking activity of large ensembles of neurons. Here, we developed a new toolbox to sort spikes from these large-scale extracellular data. To validate our method, we performed simultaneous extracellular and loose patch recordings in rodents to obtain 'ground truth' data, where the solution to this sorting problem is known for one cell. The performance of our algorithm was always close to the best expected performance, over a broad range of signal-to-noise ratios, in vitro and in vivo. The algorithm is entirely parallelized and has been successfully tested on recordings with up to 4225 electrodes. Our toolbox thus offers a generic solution to sort accurately spikes for up to thousands of electrodes.},
annote = {spyking-circusの論文},
author = {Kleinfeld, David and Yger, Pierre and Spampinato, Giulia LB and Esposito, Elric and Lefebvre, Baptiste and phane Deny, St{\'{e}} and Gardella, Christophe and Stimberg, Marcel and Jetter, Florian and Zeck, Guenther and Picaud, Serge and Duebel, Jens and Marre, Olivier},
doi = {10.7554/eLife.34518.001},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/Kleinfeld et al. - 2018 - A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and.pdf:pdf},
journal = {eLife},
mendeley-groups = {Spike Train Analysis/Spike Sorting},
title = {{A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo}},
url = {https://doi.org/10.7554/eLife.34518.001},
year = {2018}
}

@article{Buccino2020,
abstract = {Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.},
author = {Buccino, Alessio P. and Hurwitz, Cole L. and Garcia, Samuel and Magland, Jeremy and Siegle, Joshua H. and Hurwitz, Roger and Hennig, Matthias H.},
doi = {10.7554/eLife.61834},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/Buccino et al. - 2020 - Spikeinterface, a unified framework for spike sorting.pdf:pdf},
issn = {2050084X},
journal = {eLife},
mendeley-groups = {Spike Train Analysis/Spike Sorting,Physiology/Toolbox},
pages = {1--24},
pmid = {33170122},
title = {{Spikeinterface, a unified framework for spike sorting}},
volume = {9},
year = {2020}
}

@article{Hilgen2017,
abstract = {We present a method for automated spike sorting for recordings with high-density, large-scale multielectrode arrays. Exploiting the dense sampling of single neurons by multiple electrodes, an efficient, low-dimensional representation of detected spikes consisting of estimated spatial spike locations and dominant spike shape features is exploited for fast and reliable clustering into single units. Millions of events can be sorted in minutes, and the method is parallelized and scales better than quadratically with the number of detected spikes. Performance is demonstrated using recordings with a 4,096-channel array and validated using anatomical imaging, optogenetic stimulation, and model-based quality control. A comparison with semi-automated, shape-based spike sorting exposes significant limitations of conventional methods. Our approach demonstrates that it is feasible to reliably isolate the activity of up to thousands of neurons and that dense, multi-channel probes substantially aid reliable spike sorting.},
author = {Hilgen, Gerrit and Sorbaro, Martino and Pirmoradian, Sahar and Muthmann, Jens Oliver and Kepiro, Ibolya Edit and Ullo, Simona and Ramirez, Cesar Juarez and {Puente Encinas}, Albert and Maccione, Alessandro and Berdondini, Luca and Murino, Vittorio and Sona, Diego and {Cella Zanacchi}, Francesca and Sernagor, Evelyne and Hennig, Matthias Helge},
doi = {10.1016/j.celrep.2017.02.038},
file = {:Users/TAKUMA/Documents/Mendeley Desktop/Hilgen et al. - 2017 - Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays.pdf:pdf},
issn = {22111247},
journal = {Cell Reports},
keywords = {electrophysiology,high-density multielectrode array,neural cultures,retina,spike sorting},
mendeley-groups = {Spike Train Analysis/Spike Sorting},
pmid = {28273464},
title = {{Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays}},
year = {2017}
}

@inproceedings{kilosort,
 author = {Pachitariu, Marius and Steinmetz, Nicholas A and Kadir, Shabnam N and Carandini, Matteo and Harris, Kenneth D},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Fast and accurate spike sorting of high-channel count probes with KiloSort},
 url = {https://proceedings.neurips.cc/paper/2016/file/1145a30ff80745b56fb0cecf65305017-Paper.pdf},
 volume = {29},
 year = {2016}
}