@article{ quiroga_unsupervised_2004,
  author = {Quiroga, R. Quian and Nadasdy, Z. and Ben-Shaul, Y.},
  title = {Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic
	Clustering},
  journal = {Neural Computation},
  year = {2004},
  volume = {16},
  pages = {1661--1687},
  number = {8},
  month = {August},
  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.},
  doi = {10.1162/089976604774201631},
  file = {Neural Computation Full Text PDF:D:\Zotero\storage\5VQNBCT7\Quiroga et al. - 2004 - Unsupervised Spike Detection and Sorting with Wave.pdf:application/pdf},
  issn = {0899-7667},
  urldate = {2014-02-08}
}

{"_id":{"_str":"5405dda468c1c0b80700048b"},"__v":0,"authorIDs":[],"author_short":["Quiroga","Quian, R.","Nadasdy, Z.","Ben-Shaul, Y."],"bibbaseid":"quiroga-quian-nadasdy-benshaul-unsupervisedspikedetectionandsortingwithwaveletsandsuperparamagneticclustering-2004","bibdata":{"downloads":0,"role":"author","bibbaseid":"quiroga-quian-nadasdy-benshaul-unsupervisedspikedetectionandsortingwithwaveletsandsuperparamagneticclustering-2004","year":"2004","volume":"16","urldate":"2014-02-08","type":"article","title":"Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering","pages":"1661--1687","number":"8","month":"August","key":"quiroga_unsupervised_2004","journal":"Neural Computation","issn":"0899-7667","id":"quiroga_unsupervised_2004","file":"Neural Computation Full Text PDF:D:\\Zotero\\storage\\5VQNBCT7\\Quiroga et al. - 2004 - Unsupervised Spike Detection and Sorting with Wave.pdf:application/pdf","doi":"10.1162/089976604774201631","bibtype":"article","bibtex":"@article{ quiroga_unsupervised_2004,\n author = {Quiroga, R. Quian and Nadasdy, Z. and Ben-Shaul, Y.},\n title = {Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic\r\n\tClustering},\n journal = {Neural Computation},\n year = {2004},\n volume = {16},\n pages = {1661--1687},\n number = {8},\n month = {August},\n abstract = {This study introduces a new method for detecting and sorting spikes\r\n\tfrom multiunit recordings. The method combines the wave let transform,\r\n\twhich localizes distinctive spike features, with super paramagnetic\r\n\tclustering, which allows automatic classification of the data without\r\n\tassumptions such as low variance or gaussian distributions. Moreover,\r\n\tan improved method for setting amplitude thresholds for spike detection\r\n\tis proposed. We describe several criteria for implementation that\r\n\trender the algorithm unsupervised and fast. The algorithm is compared\r\n\tto other conventional methods using several simulated data sets whose\r\n\tcharacteristics closely resemble those of in vivo recordings. For\r\n\tthese data sets, we found that the proposed algorithm outperformed\r\n\tconventional methods.},\n doi = {10.1162/089976604774201631},\n file = {Neural Computation Full Text PDF:D:\\Zotero\\storage\\5VQNBCT7\\Quiroga et al. - 2004 - Unsupervised Spike Detection and Sorting with Wave.pdf:application/pdf},\n issn = {0899-7667},\n urldate = {2014-02-08}\n}","author_short":["Quiroga","Quian, R.","Nadasdy, Z.","Ben-Shaul, Y."],"author":["Quiroga","Quian, R.","Nadasdy, Z.","Ben-Shaul, Y."],"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."},"bibtype":"article","biburl":"http://keshtkaran.com/publication/mypublish.bib","creationDate":"2014-09-02T15:09:24.152Z","downloads":0,"keywords":[],"search_terms":["unsupervised","spike","detection","sorting","wavelets","superparamagnetic","clustering","quiroga","quian","nadasdy","ben-shaul"],"title":"Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering","year":2004,"dataSources":["gtrCodY7PFNKjatPy"]}