@article{rossant_spike_2016,
  title = {{Spike sorting for large, dense electrode arrays}},
  issn = {1097-6256, 1546-1726},
  url = {http://www.nature.com/doifinder/10.1038/nn.4268},
  doi = {10.1038/nn.4268},
  urldate = {2016-03-15},
  journal = {Nature Neuroscience},
  author = {Rossant, Cyrille and Kadir, Shabnam N and Goodman, Dan F M and Schulman, John and Hunter, Maximilian L D and Saleem, Aman B and Grosmark, Andres and Belluscio, Mariano and Denfield, George H and Ecker, Alexander S and Tolias, Andreas S and Solomon, Samuel and Buzsáki, György and Carandini, Matteo and Harris, Kenneth D},
  month = {mar},
  year = {2016},
  file = {nn.4268.pdf:E\:\\George\\Zotero\\storage\\4N53D68I\\nn.4268.pdf:application/pdf;nn.4268-S1.pdf:E\:\\George\\Zotero\\storage\\HSXWSKJE\\nn.4268-S1.pdf:application/pdf;nn.4268-S2.pdf:E\:\\George\\Zotero\\storage\\ZWWWHTCN\\nn.4268-S2.pdf:application/pdf},
}


@article{rey_past_2015,
  title = {{Past, present and future of spike sorting techniques}},
  volume = {119},
  issn = {03619230},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S0361923015000684},
  doi = {10.1016/j.brainresbull.2015.04.007},
  language = {en},
  urldate = {2016-02-25},
  journal = {Brain Research Bulletin},
  author = {Rey, Hernan Gonzalo and Pedreira, Carlos and Quian Quiroga, Rodrigo},
  month = {oct},
  year = {2015},
  pages = {106--117},
  file = {1-s2.0-S0361923015000684-main.pdf:E\:\\George\\Zotero\\storage\\N7U4K7S9\\1-s2.0-S0361923015000684-main.pdf:application/pdf},
}


@article{van_der_maaten_visualizing_2008,
  title = {{Visualizing data using t-{SNE}}},
  volume = {9},
  url = {http://siplab.tudelft.nl/sites/default/files/vandermaaten08a.pdf},
  number = {2579-2605},
  urldate = {2016-02-25},
  journal = {Journal of Machine Learning Research},
  author = {Van der Maaten, Laurens and Hinton, Geoffrey},
  year = {2008},
  pages = {85},
  file = {JMLR_2008.pdf:E\:\\George\\Zotero\\storage\\K3V4D592\\JMLR_2008.pdf:application/pdf},
}


@techreport{neto_validating_2016,
  title = {{Validating silicon polytrodes with paired juxtacellular recordings: method and dataset}},
  shorttitle = {Validating silicon polytrodes with paired juxtacellular recordings},
  url = {http://biorxiv.org/lookup/doi/10.1101/037937},
  language = {en},
  number = {biorxiv;037937v2},
  urldate = {2016-02-25},
  author = {Neto, Joana P. and Lopes, Gonçalo and Frazão, João and Nogueira, Joana and Lacerda, Pedro and Baião, Pedro and Aarts, Arno and Andrei, Alexandru and Musa, Silke and Fortunato, Elvira and Barquinha, Pedro and Kampff, Adam},
  month = {jan},
  year = {2016},
  file = {037937.full.pdf:E\:\\George\\Zotero\\storage\\9BH96W4H\\037937.full.pdf:application/pdf},
}


@incollection{recce_tetrode:_1989,
  title = {{The {Tetrode}: {An} {Improved} technique for multi-unit extracellular recording,}},
  volume = {2(15)},
  booktitle = {Society for {Neuroscience} {Abs}},
  author = {Recce, M. L. and O'Keefe, John},
  year = {1989},
  pages = {1250},
}


@article{alivisatos_nanotools_2013,
  title = {{Nanotools for {Neuroscience} and {Brain} {Activity} {Mapping}}},
  volume = {7},
  issn = {1936-0851, 1936-086X},
  url = {http://pubs.acs.org/doi/abs/10.1021/nn4012847},
  doi = {10.1021/nn4012847},
  language = {en},
  number = {3},
  urldate = {2016-03-04},
  journal = {ACS Nano},
  author = {Alivisatos, A. Paul and Andrews, Anne M. and Boyden, Edward S. and Chun, Miyoung and Church, George M. and Deisseroth, Karl and Donoghue, John P. and Fraser, Scott E. and Lippincott-Schwartz, Jennifer and Looger, Loren L. and Masmanidis, Sotiris and McEuen, Paul L. and Nurmikko, Arto V. and Park, Hongkun and Peterka, Darcy S. and Reid, Clay and Roukes, Michael L. and Scherer, Axel and Schnitzer, Mark and Sejnowski, Terrence J. and Shepard, Kenneth L. and Tsao, Doris and Turrigiano, Gina and Weiss, Paul S. and Xu, Chris and Yuste, Rafael and Zhuang, Xiaowei},
  month = {mar},
  year = {2013},
  pages = {1850--1866},
  file = {nn4012847.pdf:E\:\\George\\Zotero\\storage\\56UEZRSK\\nn4012847.pdf:application/pdf},
}


@article{rey_single-cell_2015,
  title = {{Single-cell recordings in the human medial temporal lobe}},
  volume = {227},
  issn = {00218782},
  url = {http://doi.wiley.com/10.1111/joa.12228},
  doi = {10.1111/joa.12228},
  language = {en},
  number = {4},
  urldate = {2016-03-04},
  journal = {Journal of Anatomy},
  author = {Rey, Hernan G. and Ison, Matias J. and Pedreira, Carlos and Valentin, Antonio and Alarcon, Gonzalo and Selway, Richard and Richardson, Mark P. and Quian Quiroga, Rodrigo},
  month = {oct},
  year = {2015},
  pages = {394--408},
  file = {a3748acc8f562707a1380a7af189a99c002dacbadc12bba7ad176cbe9accfa05.pdf:E\:\\George\\Zotero\\storage\\D9HVMJWT\\a3748acc8f562707a1380a7af189a99c002dacbadc12bba7ad176cbe9accfa05.pdf:application/pdf},
}


@article{redish_independence_2001,
  title = {{Independence of firing correlates of anatomically proximate hippocampal pyramidal cells}},
  volume = {21},
  issn = {1529-2401},
  abstract = {In neocortex, neighboring neurons frequently exhibit correlated encoding properties. There is conflicting evidence whether a similar phenomenon occurs in hippocampus. To assess this quantitatively, a comparison was made of the spatial and temporal firing correlations within and between local groups of hippocampal cells, spaced 350-1400 microm apart. No evidence of clustering was found in a sample of {\textgreater}3000 neurons. Moreover, cells active in two environments were uniformly interspersed at a scale of {\textless}100 microm, as assessed by the activity-induced gene Arc. Independence of encoding characteristics implies uncorrelated inputs, which could enhance the capacity of the hippocampus to store arbitrary associations.},
  language = {eng},
  number = {5},
  journal = {The Journal of Neuroscience: The Official Journal of the Society for Neuroscience},
  author = {Redish, A. D. and Battaglia, F. P. and Chawla, M. K. and Ekstrom, A. D. and Gerrard, J. L. and Lipa, P. and Rosenzweig, E. S. and Worley, P. F. and Guzowski, J. F. and McNaughton, B. L. and Barnes, C. A.},
  month = {mar},
  year = {2001},
  pmid = {11222672},
  keywords = {Action Potentials, Animals, Cytoskeletal Proteins, Electric Stimulation, Electrodes, Implanted, Environment, Hippocampus, Interneurons, Male, Maze Learning, Medial Forebrain Bundle, Motor Activity, Nerve Tissue Proteins, Pyramidal Cells, Rats, Rats, Inbred F344, RNA, Messenger, Signal Processing, Computer-Assisted, Spatial Behavior},
  pages = {RC134},
}


@article{wood_variability_2004,
  title = {{On the variability of manual spike sorting}},
  volume = {51},
  url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1300782},
  number = {6},
  urldate = {2016-03-03},
  journal = {Biomedical Engineering, IEEE Transactions on},
  author = {Wood, Frank and Black, Michael J. and Vargas-Irwin, Carlos and Fellows, Matthew and Donoghue, John P.},
  year = {2004},
  pages = {912--918},
  file = {Wood-TBME-2004.pdf:E\:\\George\\Zotero\\storage\\IDUNAU4A\\Wood-TBME-2004.pdf:application/pdf},
}


@article{gray_tetrodes_1995,
  title = {{Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex}},
  volume = {63},
  issn = {0165-0270},
  url = {http://www.sciencedirect.com/science/article/pii/0165027095000852},
  doi = {10.1016/0165-0270(95)00085-2},
  abstract = {The majority of techniques for separating multiple single-unit spike trains from a multi-unit recording rely on the assumption that different cells exhibit action potentials having unique amplitudes and waveforms. When this assumption fails, due to the similarity of spike shape among different cells or to the presence of complex spikes with declining intra-burst amplitude, these methods lead to errors in classification. In an effort to avoid these errors, the stereotrode (McNaughton et al., 1983) and later the tetrode (O'Keefe and Reece, 1993; Wilson and McNaughton, 1993) recording techniques were developed. Because the latter technique has been applied primarily to the hippocampus, we sought to evaluate its performance in the neocortex. Multi-unit recordings, using single tetrodes, were made at 28 sites in area 17 of 3 anesthetized cats. Neurons were activated with moving bars and square wave gratings. Single units were separated by identification of clusters in 2-D projections of either peak-to-peak amplitude, spike width, spike area, or the 1st versus 2nd principal components of the waveforms recorded on each channel. Using tetrodes, we recorded a total of 154 single cells (mean = 5.4, max = 9). By cross-checking the performance of the tetrode with the stereotrode and electrode, we found that the best of the 6 possible stereotrode pairs and the best of 4 possible electrodes from each tetrode yielded 102 (mean = 3.6, max = 7) and 95 (mean = 3.4, max = 6) cells, respectively. Moreover, we found that the number of cells isolated at each site by the tetrode was greater than the stereotrode or electrode in 1628 and 2828 cases, respectively. Thus, both stereotrodes, and particularly electrodes, often lumped 2 or more cells in a single cluster that could be easily separated by the tetrode. We conclude that tetrode recording currently provides the best and most reliable method for the isolation of multiple single units in the neocortex using a single probe.},
  number = {1–2},
  urldate = {2016-03-10},
  journal = {Journal of Neuroscience Methods},
  author = {Gray, Charles M. and Maldonado, Pedro E. and Wilson, Mathew and McNaughton, Bruce},
  month = {dec},
  year = {1995},
  keywords = {Multi-unit recording, Principal components analysis, Spike detection, Visual cortex, area 17},
  pages = {43--54},
  file = {ScienceDirect Snapshot:E\:\\George\\Zotero\\storage\\IEH8UX2B\\0165027095000852.html:text/html},
}


@article{fee_automatic_1996,
  title = {{Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-{Gaussian} variability}},
  volume = {69},
  issn = {0165-0270},
  doi = {10.1016/S0165-0270(96)00050-7},
  abstract = {Neuronal noise sources and systematic variability in the shape of a spike limit the ability to sort multiple unit waveforms recorded from nervous tissue into their single neuron constituents. Here we present a procedure to efficiently sort spikes in the presence of noise that is anisotropic, i.e., dominated by particular frequencies, and whose amplitude distribution may be non-Gaussian, such as occurs when spike waveforms are a function of interspike interval. Our algorithm uses a hierarchical clustering scheme. First, multiple unit records are sorted into an overly large number of clusters by recursive bisection. Second, these clusters are progressively aggregated into a minimal set of putative single units based on both similarities of spike shape as well as the statistics of spike arrival times, such as imposed by the refractory period. We apply the algorithm to waveforms recorded with chronically implanted micro-wire stereotrodes from neocortex of behaving rat. Natural extension of the algorithm may be used to cluster spike waveforms from records with many input channels, such as those obtained with tetrodes and multiple site optical techniques.},
  language = {eng},
  number = {2},
  journal = {Journal of Neuroscience Methods},
  author = {Fee, M. S. and Mitra, P. P. and Kleinfeld, D.},
  month = {nov},
  year = {1996},
  pmid = {8946321},
  keywords = {Action Potentials, Algorithms, Animals, Anisotropy, Data Interpretation, Statistical, Electrophysiology, Neurons, Afferent, Normal Distribution, Rats, Rats, Inbred Strains, Vibrissae},
  pages = {175--188},
}


@article{wehr_simultaneous_1999,
  title = {{Simultaneous paired intracellular and tetrode recordings for evaluating the performance of spike sorting algorithms}},
  volume = {26–27},
  issn = {0925-2312},
  url = {http://www.sciencedirect.com/science/article/pii/S0925231299001058},
  doi = {10.1016/S0925-2312(99)00105-8},
  abstract = {Objective evaluation of spike sorting algorithms such as those used to decompose tetrode recordings into distinct spike trains requires a priori knowledge of the correct classification for a given recording. Intracellular recording can unambiguously assign spikes to a single neuron, and thus provide correct classification if signals from that neuron concurrently appear in a tetrode recording. Simultaneous single or paired intracellular and tetrode recordings are used here to evaluate a contemporary spike sorting algorithm for isolated as well as overlapped events. These data are also used to demonstrate that overlapping extracellular spikes combine additively, and to introduce a means for quantifying variability in action potential shape.},
  urldate = {2016-03-03},
  journal = {Neurocomputing},
  author = {Wehr, M. and Pezaris, J. S. and Sahani, M.},
  month = {jun},
  year = {1999},
  keywords = {NSCA, Overlap resolution, Spike linearity, Spike sorting, Tetrode},
  pages = {1061--1068},
  file = {1-s2.0-S0925231299001058-main.pdf:E\:\\George\\Zotero\\storage\\N2VIBBXD\\1-s2.0-S0925231299001058-main.pdf:application/pdf;ScienceDirect Snapshot:E\:\\George\\Zotero\\storage\\WAE8QPFE\\S0925231299001058.html:text/html},
}


@article{buzsaki_large-scale_2004,
  title = {{Large-scale recording of neuronal ensembles}},
  volume = {7},
  issn = {1097-6256},
  url = {http://www.nature.com/doifinder/10.1038/nn1233},
  doi = {10.1038/nn1233},
  number = {5},
  urldate = {2016-04-13},
  journal = {Nature Neuroscience},
  author = {Buzsáki, György},
  month = {may},
  year = {2004},
  pages = {446--451},
  file = {nn1233.pdf:E\:\\George\\Zotero\\storage\\CC9T4H9D\\nn1233.pdf:application/pdf},
}


@article{harris_accuracy_2000,
  title = {{Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements}},
  volume = {84},
  url = {http://jn.physiology.org/content/84/1/401.short},
  number = {1},
  urldate = {2016-03-04},
  journal = {Journal of neurophysiology},
  author = {Harris, Kenneth D. and Henze, Darrell A. and Csicsvari, Jozsef and Hirase, Hajime and Buzsáki, György},
  year = {2000},
  pages = {401--414},
  file = {401.full.pdf:E\:\\George\\Zotero\\storage\\RJEV25VD\\401.full.pdf:application/pdf},
}


@article{quiroga_unsupervised_2004,
  title = {{Unsupervised {Spike} {Detection} and {Sorting} with {Wavelets} and {Superparamagnetic} {Clustering}}},
  volume = {16},
  issn = {0899-7667},
  url = {http://dx.doi.org/10.1162/089976604774201631},
  doi = {10.1162/089976604774201631},
  abstract = {This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wave let transform, which localizes distinctive spike features, with super paramagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods.},
  number = {8},
  urldate = {2016-03-04},
  journal = {Neural Computation},
  author = {Quiroga, R. Quian and Nadasdy, Z. and Ben-Shaul, Y.},
  month = {aug},
  year = {2004},
  pages = {1661--1687},
  file = {Neural Computation Snapshot:E\:\\George\\Zotero\\storage\\79FKND9W\\089976604774201631.html:text/html},
}


@article{takekawa_accurate_2010,
  title = {{Accurate spike sorting for multi-unit recordings}},
  volume = {31},
  copyright = {© The Authors (2010). Journal Compilation © Federation of European Neuroscience Societies and Blackwell Publishing Ltd},
  issn = {1460-9568},
  url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1460-9568.2009.07068.x/abstract},
  doi = {10.1111/j.1460-9568.2009.07068.x},
  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.},
  language = {en},
  number = {2},
  urldate = {2016-03-04},
  journal = {European Journal of Neuroscience},
  author = {Takekawa, Takashi and Isomura, Yoshikazu and Fukai, Tomoki},
  month = {jan},
  year = {2010},
  keywords = {cell assembly, clustering, Efficient Technology of Spike sorting open-source software, robust variational Bayes, wavelet transform},
  pages = {263--272},
  file = {Full Text PDF:E\:\\George\\Zotero\\storage\\WZFERUQT\\Takekawa et al. - 2010 - Accurate spike sorting for multi-unit recordings.pdf:application/pdf;Snapshot:E\:\\George\\Zotero\\storage\\3GJ8M7WG\\abstract.html:text/html},
}


@article{gerstein_simultaneous_1964,
  title = {{Simultaneous {Studies} of {Firing} {Patterns} in {Several} {Neurons}}},
  volume = {143},
  copyright = {© 1964},
  issn = {0036-8075, 1095-9203},
  url = {http://science.sciencemag.org/content/143/3612/1325},
  doi = {10.1126/science.143.3612.1325},
  abstract = {A tungsten microelectrode with several small holes burnt in the vinyl insulation enables the action potentials from several adjacent neurons to be observed simultaneously. A digital computer is used to separate the contributions of each neuron by examining and classifying the waveforms of the action potentials. These methods allow studies to be made of interactions between neurons that lie close together.},
  language = {en},
  number = {3612},
  urldate = {2016-03-12},
  journal = {Science},
  author = {Gerstein, G. L. and Clark, W. A.},
  month = {mar},
  year = {1964},
  pmid = {14108312},
  pages = {1325--1327},
  file = {Snapshot:E\:\\George\\Zotero\\storage\\EXSJDEPA\\1325.html:text/html},
}


@article{ruther_new_2015,
  title = {{New approaches for {CMOS}-based devices for large-scale neural recording}},
  volume = {32},
  issn = {09594388},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S0959438814002074},
  doi = {10.1016/j.conb.2014.10.007},
  language = {en},
  urldate = {2016-03-04},
  journal = {Current Opinion in Neurobiology},
  author = {Ruther, Patrick and Paul, Oliver},
  month = {jun},
  year = {2015},
  pages = {31--37},
  file = {1-s2.0-S0959438814002074-main.pdf:E\:\\George\\Zotero\\storage\\5XWM3JKK\\1-s2.0-S0959438814002074-main.pdf:application/pdf},
}


@article{chaudhuri_computational_2016,
  title = {{Computational principles of memory}},
  volume = {19},
  issn = {1097-6256, 1546-1726},
  url = {http://www.nature.com/doifinder/10.1038/nn.4237},
  doi = {10.1038/nn.4237},
  number = {3},
  urldate = {2016-03-03},
  journal = {Nature Neuroscience},
  author = {Chaudhuri, Rishidev and Fiete, Ila},
  month = {feb},
  year = {2016},
  pages = {394--403},
  file = {Chaudhuri and Fiete - 2016 - Computational principles of memory.pdf:E\:\\George\\Zotero\\storage\\JKD947PB\\Chaudhuri and Fiete - 2016 - Computational principles of memory.pdf:application/pdf},
}


@article{lewicki_review_1998,
  title = {{A review of methods for spike sorting: the detection and classification of neural action potentials}},
  volume = {9},
  shorttitle = {A review of methods for spike sorting},
  url = {http://www.tandfonline.com/doi/abs/10.1088/0954-898X_9_4_001},
  number = {4},
  urldate = {2016-03-12},
  journal = {Network: Computation in Neural Systems},
  author = {Lewicki, Michael S.},
  year = {1998},
  pages = {R53--R78},
  file = {Lewicki-Network-98_1.pdf:E\:\\George\\Zotero\\storage\\C6R7X9VS\\Lewicki-Network-98_1.pdf:application/pdf},
}


@inproceedings{wood_automatic_2004,
  title = {{Automatic spike sorting for neural decoding}},
  volume = {2},
  url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1404120},
  urldate = {2016-04-13},
  booktitle = {Engineering in {Medicine} and {Biology} {Society}, 2004. {IEMBS}'04. 26th {Annual} {International} {Conference} of the {IEEE}},
  publisher = {IEEE},
  author = {Wood, Frank and Fellows, M. and Donoghue, J. R. and Black, M. J.},
  year = {2004},
  pages = {4009--4012},
  file = {01404120.pdf:E\:\\George\\Zotero\\storage\\JVX44S2P\\01404120.pdf:application/pdf},
}


@article{wang_robust_2006,
  title = {{A robust method for spike sorting with automatic overlap decomposition}},
  volume = {53},
  issn = {0018-9294},
  doi = {10.1109/TBME.2006.873397},
  abstract = {Spike sorting is the mandatory first step in analyzing multiunit recording signals for studying information processing mechanisms within the nervous system. Extracellular recordings usually contain overlapped spikes produced by a number of neurons adjacent to the electrode, together with unknown background noise, which in turn induce some difficulties in neural signal identification. In this paper, we propose a robust method to deal with these problems, which employs an automatic overlap decomposition technique based on the relaxation algorithm that requires simple fast Fourier transforms. The performance of the presented system was tested at various signal-to-noise ratio levels based on synthetic data that were generated from real recordings.},
  language = {eng},
  number = {6},
  journal = {IEEE transactions on bio-medical engineering},
  author = {Wang, Guang-Li and Zhou, Yi and Chen, Ai-Hua and Zhang, Pu-Ming and Liang, Pei-Ji},
  month = {jun},
  year = {2006},
  pmid = {16761848},
  keywords = {Action Potentials, Algorithms, Animals, Artificial Intelligence, Cells, Cultured, Chickens, Nerve Net, Pattern Recognition, Automated, Retinal Ganglion Cells},
  pages = {1195--1198},
}


@article{bestel_novel_2012,
  title = {{A novel automated spike sorting algorithm with adaptable feature extraction}},
  volume = {211},
  issn = {01650270},
  url = {http://linkinghub.elsevier.com/retrieve/pii/S0165027012003263},
  doi = {10.1016/j.jneumeth.2012.08.015},
  language = {en},
  number = {1},
  urldate = {2016-03-03},
  journal = {Journal of Neuroscience Methods},
  author = {Bestel, Robert and Daus, Andreas W. and Thielemann, Christiane},
  month = {oct},
  year = {2012},
  pages = {168--178},
  file = {1-s2.0-S0165027012003263-main.pdf:E\:\\George\\Zotero\\storage\\C8PHHQZB\\1-s2.0-S0165027012003263-main.pdf:application/pdf},
}


@article{zhang_spike_2004,
  title = {{Spike sorting based on automatic template reconstruction with a partial solution to the overlapping problem}},
  volume = {135},
  issn = {0165-0270},
  url = {http://www.sciencedirect.com/science/article/pii/S0165027003004096},
  doi = {10.1016/j.jneumeth.2003.12.001},
  abstract = {A new method for spike sorting is proposed which partly solves the overlapping problem. Principal component analysis and subtractive clustering techniques are used to estimate the number of neurons contributing to multi-unit recording. Spike templates (i.e. waveforms) are reconstructed according to the clustering results. A template-matching procedure is then performed. Firstly all temporally displaced templates are compared with the spike event to find the best-fitting template that yields the minimum residue variance. If the residue passes the χ2-test, the matching procedure stops and the spike event is classified as the best-fitting template. Otherwise the spike event may be an overlapping waveform. The procedure is then repeated with all possible combinations of two templates, three templates, etc. Once one combination is found, which yields the minimum residue variance among the combinations of the same number of component templates and makes the residue pass the χ2-test, the matching procedure stops. It is unnecessary to check the remaining combinations of more templates. Consequently, the computational effort is reduced and the over-fitting problem can be partly avoided. A simulated spike train was used to assess the performance of the proposed method, which was also applied to a real recording of chicken retina ganglion cells.},
  number = {1–2},
  urldate = {2016-04-13},
  journal = {Journal of Neuroscience Methods},
  author = {Zhang, Pu-Ming and Wu, Jin-Yong and Zhou, Yi and Liang, Pei-Ji and Yuan, Jing-Qi},
  month = {may},
  year = {2004},
  keywords = {Overlapping, Principal component analysis, Spike sorting, Subtractive clustering, Template-matching, χ2-Test},
  pages = {55--65},
  file = {ScienceDirect Full Text PDF:E\:\\George\\Zotero\\storage\\4M6WB8UW\\Zhang et al. - 2004 - Spike sorting based on automatic template reconstr.pdf:application/pdf;ScienceDirect Snapshot:E\:\\George\\Zotero\\storage\\RDCNZR7Q\\S0165027003004096.html:text/html},
}


@article{hulata_method_2002,
  title = {{A method for spike sorting and detection based on wavelet packets and {Shannon}'s mutual information}},
  volume = {117},
  issn = {0165-0270},
  url = {http://www.sciencedirect.com/science/article/pii/S0165027002000328},
  doi = {10.1016/S0165-0270(02)00032-8},
  abstract = {Studying the dynamics of neural activity via electrical recording, relies on the ability to detect and sort neural spikes recorded from a number of neurons by the same electrode. We suggest the wavelet packets decomposition (WPD) as a tool to analyze neural spikes and extract their main features. The unique quality of the wavelet packets-adaptive coverage of both time and frequency domains using a set of localized packets, facilitate the task. The best basis algorithm utilizing the Shannon's information cost function and local discriminant basis (LDB) using mutual information are employed to select a few packets that are sufficient for both detection and sorting of spikes. The efficiency of the method is demonstrated on data recorded from in vitro 2D neural networks, placed on electrodes that read data from as many as five neurons. Comparison between our method and the widely used principal components method and a sorting technique based on the ordinary wavelet transform (WT) shows that our method is more efficient both in separating spikes from noise and in resolving overlapping spikes.},
  number = {1},
  urldate = {2016-04-13},
  journal = {Journal of Neuroscience Methods},
  author = {Hulata, Eyal and Segev, Ronen and Ben-Jacob, Eshel},
  month = {may},
  year = {2002},
  keywords = {Multi-electrodes, Shannon's information, Spike sorting, Wavelet packets, Wavelets},
  pages = {1--12},
  file = {ScienceDirect Full Text PDF:E\:\\George\\Zotero\\storage\\2DVIJUEB\\Hulata et al. - 2002 - A method for spike sorting and detection based on .pdf:application/pdf;ScienceDirect Snapshot:E\:\\George\\Zotero\\storage\\KBX3CU8H\\S0165027002000328.html:text/html},
}


@inproceedings{chang_compute_2008,
  address = {Orlando, Florida, USA},
  title = {{Compute pairwise euclidean distances of data points with {GPUs}}},
  booktitle = {Proceedings of the {IASTED} {International} {Symposium} {Computational} {Biology} and {Bioinformatics}},
  author = {Chang, Darjen and Jones, Nathaniel A. and Li, Dazhuo and Ouyang, Ming},
  month = {nov},
  year = {2008},
  file = {Chang_etal_CBB2008_634-017.pdf:E\:\\George\\Zotero\\storage\\UZ9SEQWZ\\Chang_etal_CBB2008_634-017.pdf:application/pdf},
}


@article{van_der_maaten_accelerating_2014,
  title = {{Accelerating t-sne using tree-based algorithms}},
  volume = {15},
  url = {http://dl.acm.org/citation.cfm?id=2697068},
  number = {1},
  urldate = {2016-02-25},
  journal = {The Journal of Machine Learning Research},
  author = {Van Der Maaten, Laurens},
  year = {2014},
  pages = {3221--3245},
  file = {JMLR_2014.pdf:E\:\\George\\Zotero\\storage\\7I28F6JF\\JMLR_2014.pdf:application/pdf},
}


@inproceedings{ester_density-based_1996,
  title = {{A density-based algorithm for discovering clusters in large spatial databases with noise}},
  abstract = {Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.},
  publisher = {AAAI Press},
  author = {Ester, Martin and Kriegel, Hans-Peter and Sander, Jörg and Xu, Xiaowei},
  year = {1996},
  pages = {226--231},
  file = {Citeseer - Full Text PDF:E\:\\George\\Zotero\\storage\\AZWDDTAB\\Ester et al. - 1996 - A density-based algorithm for discovering clusters.pdf:application/pdf;Citeseer - Snapshot:E\:\\George\\Zotero\\storage\\A4ZDXU84\\summary.html:text/html},
}


@article{xu_visualization_2014,
  title = {{Visualization of genetic disease-phenotype similarities by multiple maps t-{SNE} with {Laplacian} regularization}},
  volume = {7},
  url = {http://www.biomedcentral.com/1755-8794/7/S2/S1/},
  number = {Suppl 2},
  urldate = {2016-05-05},
  journal = {BMC medical genomics},
  author = {Xu, Weiwei and Jiang, Xingpeng and Hu, Xiaohua and Li, Guangrong},
  year = {2014},
  pages = {S1},
  file = {art%3A10.1186%2F1755-8794-7-S2-S1.pdf:E\:\\George\\Zotero\\storage\\XPXQ5CSX\\art%3A10.1186%2F1755-8794-7-S2-S1.pdf:application/pdf},
}


@article{berman_mapping_2014,
  title = {{Mapping the stereotyped behaviour of freely moving fruit flies}},
  volume = {11},
  copyright = {. © 2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.},
  issn = {1742-5689, 1742-5662},
  url = {http://rsif.royalsocietypublishing.org/content/11/99/20140672},
  doi = {10.1098/rsif.2014.0672},
  abstract = {A frequent assumption in behavioural science is that most of an animal's activities can be described in terms of a small set of stereotyped motifs. Here, we introduce a method for mapping an animal's actions, relying only upon the underlying structure of postural movement data to organize and classify behaviours. Applying this method to the ground-based behaviour of the fruit fly, Drosophila melanogaster, we find that flies perform stereotyped actions roughly 50\% of the time, discovering over 100 distinguishable, stereotyped behavioural states. These include multiple modes of locomotion and grooming. We use the resulting measurements as the basis for identifying subtle sex-specific behavioural differences and revealing the low-dimensional nature of animal motions.},
  language = {en},
  number = {99},
  urldate = {2016-05-05},
  journal = {Journal of The Royal Society Interface},
  author = {Berman, Gordon J. and Choi, Daniel M. and Bialek, William and Shaevitz, Joshua W.},
  month = {oct},
  year = {2014},
  pmid = {25142523},
  pages = {20140672},
  file = {Full Text PDF:E\:\\George\\Zotero\\storage\\Q5NU9U27\\Berman et al. - 2014 - Mapping the stereotyped behaviour of freely moving.pdf:application/pdf;Snapshot:E\:\\George\\Zotero\\storage\\HT48QFUA\\20140672.html:text/html},
}


@article{platzer_visualization_2013,
  title = {{Visualization of {SNPs} with t-{SNE}}},
  volume = {8},
  issn = {1932-6203},
  url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0056883},
  doi = {10.1371/journal.pone.0056883},
  abstract = {Background  Single Nucleotide Polymorphisms (SNPs) are one of the largest sources of new data in biology. In most papers, SNPs between individuals are visualized with Principal Component Analysis (PCA), an older method for this purpose.    Principal Findings  We compare PCA, an aging method for this purpose, with a newer method, t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of large SNP datasets. We also propose a set of key figures for evaluating these visualizations; in all of these t-SNE performs better.    Significance  To transform data PCA remains a reasonably good method, but for visualization it should be replaced by a method from the subfield of dimension reduction. To evaluate the performance of visualization, we propose key figures of cross-validation with machine learning methods, as well as indices of cluster validity.},
  number = {2},
  urldate = {2016-05-05},
  journal = {PLOS ONE},
  author = {Platzer, Alexander},
  month = {feb},
  year = {2013},
  keywords = {Arabidopsis thaliana, Data visualization, Haplotypes, Machine learning, Machine learning algorithms, Neural networks, Principal component analysis, Rice},
  pages = {e56883},
  file = {Full Text PDF:E\:\\George\\Zotero\\storage\\NBJCCINU\\Platzer - 2013 - Visualization of SNPs with t-SNE.pdf:application/pdf;Snapshot:E\:\\George\\Zotero\\storage\\DN5TUA6V\\article.html:text/html},
}


@article{mnih_human-level_2015,
  title = {{Human-level control through deep reinforcement learning}},
  volume = {518},
  copyright = {© 2015 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
  issn = {0028-0836},
  url = {http://www.nature.com/nature/journal/v518/n7540/abs/nature14236.html},
  doi = {10.1038/nature14236},
  abstract = {The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.},
  language = {en},
  number = {7540},
  urldate = {2016-05-05},
  journal = {Nature},
  author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A. and Veness, Joel and Bellemare, Marc G. and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K. and Ostrovski, Georg and Petersen, Stig and Beattie, Charles and Sadik, Amir and Antonoglou, Ioannis and King, Helen and Kumaran, Dharshan and Wierstra, Daan and Legg, Shane and Hassabis, Demis},
  month = {feb},
  year = {2015},
  keywords = {Computer science},
  pages = {529--533},
  file = {Full Text PDF:E\:\\George\\Zotero\\storage\\G6AESBPK\\Mnih et al. - 2015 - Human-level control through deep reinforcement lea.pdf:application/pdf;Snapshot:E\:\\George\\Zotero\\storage\\JFWGFCP4\\nature14236.html:text/html},
}


@techreport{pachitariu_kilosort:_2016,
  title = {{Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels}},
  shorttitle = {Kilosort},
  url = {http://biorxiv.org/lookup/doi/10.1101/061481},
  language = {en},
  number = {biorxiv;061481v1},
  urldate = {2016-10-11},
  author = {Pachitariu, Marius and Steinmetz, Nicholas and Kadir, Shabnam and Carandini, Matteo and Harris, Kenneth D},
  month = {jun},
  year = {2016},
  file = {061481.full.pdf:E\:\\George\\Zotero\\storage\\67CIFNJX\\061481.full.pdf:application/pdf},
}


@techreport{yger_fast_2016,
  title = {{Fast and accurate spike sorting in vitro and in vivo for up to thousands of electrodes}},
  url = {http://biorxiv.org/lookup/doi/10.1101/067843},
  language = {en},
  number = {biorxiv;067843v1},
  urldate = {2016-10-11},
  author = {Yger, Pierre and Spampinato, Giulia L. B. and Esposito, Elric and Lefebvre, Baptiste and Deny, Stephane and Gardella, Christophe and Stimberg, Marcel and Jetter, Florian and Zeck, Guenther and Picaud, Serge and Duebel, Jens and Marre, Olivier},
  month = {aug},
  year = {2016},
  file = {067843.full.pdf:E\:\\George\\Zotero\\storage\\CJT63KWB\\067843.full.pdf:application/pdf},
}
