The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
A Fully Automated Approach to Spike Sorting
Open accessAbstract
Summary 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 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.
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Cites
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120
The Kolmogorov-Smirnov Test for Goodness of Fit
Massey, F. J. (1951) · Journal of the American Statistical Association · doi:10.1080/01621459.1951.10500769 · Cited by 7,210
The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat
O'Keefe, J., & Dostrovsky, J. (1971) · Brain Research · doi:10.1016/0006-8993(71)90358-1 · Cited by 6,151
Clustering by fast search and find of density peaks
Rodriguez, A., & Laio, A. (2014) · Science · doi:10.1126/science.1242072 · Cited by 4,942
Interneurons of the hippocampus
Freund, T. F., & Buzsáki, G. (1998) · Hippocampus · doi:10.1002/(sici)1098-1063(1996)6:4<347::aid-hipo1>3.0.co;2-i · Cited by 3,441
Dynamics of the Hippocampal Ensemble Code for Space
Wilson, M. A., & McNaughton, B. L. (1993) · Science · doi:10.1126/science.8351520 · Cited by 2,350
Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering
Quiroga, R. Q., Nadasdy, Z., & Ben-Shaul, Y. (2004) · Neural Computation · doi:10.1162/089976604774201631 · Cited by 2,119
Hartigan, J. A., & Hartigan, P. M. (1985) · The Annals of Statistics · doi:10.1214/aos/1176346577 · Cited by 1,973
A review of methods for spike sorting: the detection and classification of neural action potentials
Lewicki, M. S. (1998) · Network Computation in Neural Systems · doi:10.1088/0954-898x/9/4/001 · Cited by 1,370
Harris, K. D., Henze, D. A., Csicsvari, J., Hirase, H., & Buzsáki, G. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.84.1.401 · Cited by 1,151
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Martorell, A. J., Paulson, A. L., Suk, H.-J., Abdurrob, F., Drummond, G. T., Guan, W., Young, J. Z., Kim, D. N.-W., Kritskiy, O., Barker, S. J., et al. (2019) · Cell · doi:10.1016/j.cell.2019.02.014 · Cited by 798
Genetic Dissection of Neural Circuits: A Decade of Progress
Luo, L., Callaway, E. M., & Svoboda, K. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.040 · Cited by 448
Yger, P., Spampinato, G. L., Esposito, E., Lefebvre, B., Deny, S., Gardella, C., Stimberg, M., Jetter, F., Zeck, G., Picaud, S., et al. (2018) · eLife · doi:10.7554/elife.34518 · Cited by 441
Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires
Sainburg, T., Thielk, M., & Gentner, T. Q. (2020) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1008228 · Cited by 319
Accurate Estimation of Neural Population Dynamics without Spike Sorting
Trautmann, E. M., Stavisky, S. D., Lahiri, S., Ames, K. C., Kaufman, M. T., O’Shea, D. J., Vyas, S., Sun, X., Ryu, S. I., Ganguli, S., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.05.003 · Cited by 255
Metadata
- Authors
- Chung, J. E., Magland, J. F., Barnett, A. H., Tolosa, V. M., Tooker, A. C., Lee, K. Y., Shah, K. G., Felix, S. H., Frank, L. M., & Greengard, L. F.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2017-09
- Volume
- 95
- Issue
- 6
- Pages
- 1381-1394.e6
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2756375591
- Cited by
- 490
- References
- 63
- FWCI
- 14.68
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Chung, J. E., Magland, J. F., Barnett, A. H., Tolosa, V. M., Tooker, A. C., Lee, K. Y., Shah, K. G., Felix, S. H., Frank, L. M., & Greengard, L. F. (2017). A Fully Automated Approach to Spike Sorting. Neuron. https://doi.org/10.1016/j.neuron.2017.08.030
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