메뉴 건너뛰기




Volumn 238, Issue , 2014, Pages 43-53

Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering

Author keywords

Feature extraction; Gap statistics; Landmark based spectral clustering; Locality preserving projection; Spike sorting; Superparamagnetic clustering; Wavelet transformation

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; BIOMEDICAL ENGINEERING; LOCALITY PRESERVING PROJECTION; METHODOLOGY; PERFORMANCE; SPIKE; SPIKE SORTING; STATISTICS; ACTION POTENTIAL; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; COMPARATIVE STUDY; NERVE CELL; PHYSIOLOGY; PROCEDURES; SIGNAL PROCESSING; WAVELET ANALYSIS;

EID: 84908213749     PISSN: 01650270     EISSN: 1872678X     Source Type: Journal    
DOI: 10.1016/j.jneumeth.2014.09.011     Document Type: Article
Times cited : (32)

References (29)
  • 2
    • 0000902522 scopus 로고    scopus 로고
    • Data clustering using a model granular magnet
    • Blatt M., Wiseman S., Domany E. Data clustering using a model granular magnet. Neural Comput 1997, 9(8):1805-1842.
    • (1997) Neural Comput , vol.9 , Issue.8 , pp. 1805-1842
    • Blatt, M.1    Wiseman, S.2    Domany, E.3
  • 3
    • 2142765521 scopus 로고    scopus 로고
    • Multiple neural spike train data analysis: state-of-the-art and future challenges
    • Brown E.N., Kass R.E., Mitra P.P. Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat Neurosci 2004, 7(5):456-461.
    • (2004) Nat Neurosci , vol.7 , Issue.5 , pp. 456-461
    • Brown, E.N.1    Kass, R.E.2    Mitra, P.P.3
  • 4
    • 11144355852 scopus 로고    scopus 로고
    • Large-scale recording of neuronal ensembles
    • Buzsáki G. Large-scale recording of neuronal ensembles. Nat Neurosci 2004, 7(5):446-451.
    • (2004) Nat Neurosci , vol.7 , Issue.5 , pp. 446-451
    • Buzsáki, G.1
  • 5
    • 77949270248 scopus 로고    scopus 로고
    • Unsupervised wavelet-based spike sorting with dynamic codebook searching and replenishment
    • Chan H.L., Wu T., Lee S.T., Lin M.A., He S.M., Chao P.K., et al. Unsupervised wavelet-based spike sorting with dynamic codebook searching and replenishment. Neurocomputing 2010, 73(7):1513-1527.
    • (2010) Neurocomputing , vol.73 , Issue.7 , pp. 1513-1527
    • Chan, H.L.1    Wu, T.2    Lee, S.T.3    Lin, M.A.4    He, S.M.5    Chao, P.K.6
  • 8
    • 84888079100 scopus 로고    scopus 로고
    • A unified framework and method for automatic neural spike identification
    • Ekanadham C., Tranchina D., Simoncelli E.P. A unified framework and method for automatic neural spike identification. J Neurosci Methods 2014, 222:47-55.
    • (2014) J Neurosci Methods , vol.222 , pp. 47-55
    • Ekanadham, C.1    Tranchina, D.2    Simoncelli, E.P.3
  • 9
    • 85032752335 scopus 로고    scopus 로고
    • Spike sorting: the first step in decoding the brain
    • Gibson S., Judy J.W., Markovic D. Spike sorting: the first step in decoding the brain. IEEE Signal Process Mag 2012, 29(1):124-143.
    • (2012) IEEE Signal Process Mag , vol.29 , Issue.1 , pp. 124-143
    • Gibson, S.1    Judy, J.W.2    Markovic, D.3
  • 11
    • 79959316238 scopus 로고    scopus 로고
    • Quality metrics to accompany spike sorting of extracellular signals
    • Hill D.N., Mehta S.B., Kleinfeld D. Quality metrics to accompany spike sorting of extracellular signals. J Neurosci 2011, 31(24):8699-8705.
    • (2011) J Neurosci , vol.31 , Issue.24 , pp. 8699-8705
    • Hill, D.N.1    Mehta, S.B.2    Kleinfeld, D.3
  • 12
    • 32544441567 scopus 로고    scopus 로고
    • Solving alignment problems in neural spike sorting using frequency domain PCA
    • Jung H.K., Choi J.H., Kim T. Solving alignment problems in neural spike sorting using frequency domain PCA. Neurocomputing 2006, 69(7):975-978.
    • (2006) Neurocomputing , vol.69 , Issue.7 , pp. 975-978
    • Jung, H.K.1    Choi, J.H.2    Kim, T.3
  • 13
    • 79957961575 scopus 로고    scopus 로고
    • Automatic spike sorting for extracellular electrophysiological recording using unsupervised single linkage clustering based on grey relational analysis
    • Lai H.Y., Chen Y.Y., Lin S.H., Lo Y.C., Tsang S., Chen S.Y., et al. Automatic spike sorting for extracellular electrophysiological recording using unsupervised single linkage clustering based on grey relational analysis. J Neural Eng 2011, 8(3):036003.
    • (2011) J Neural Eng , vol.8 , Issue.3 , pp. 036003
    • Lai, H.Y.1    Chen, Y.Y.2    Lin, S.H.3    Lo, Y.C.4    Tsang, S.5    Chen, S.Y.6
  • 14
    • 0013130627 scopus 로고    scopus 로고
    • A review of methods for spike sorting: the detection and classification of neural action potentials
    • Lewicki M.S. A review of methods for spike sorting: the detection and classification of neural action potentials. Netw: Comput Neural Syst 1998, 9(4):R53-R78.
    • (1998) Netw: Comput Neural Syst , vol.9 , Issue.4 , pp. R53-R78
    • Lewicki, M.S.1
  • 15
    • 0020102027 scopus 로고
    • Least squares quantization in PCM
    • Lloyd S. Least squares quantization in PCM. IEEE Trans Inf Theory 1982, 28(2):129-137.
    • (1982) IEEE Trans Inf Theory , vol.28 , Issue.2 , pp. 129-137
    • Lloyd, S.1
  • 16
    • 84864569777 scopus 로고    scopus 로고
    • Automatic online spike sorting with singular value decomposition and fuzzy c-mean clustering
    • Oliynyk A., Bonifazzi C., Montani F., Fadiga L. Automatic online spike sorting with singular value decomposition and fuzzy c-mean clustering. BMC Neurosci 2012, 13(1):96.
    • (2012) BMC Neurosci , vol.13 , Issue.1 , pp. 96
    • Oliynyk, A.1    Bonifazzi, C.2    Montani, F.3    Fadiga, L.4
  • 17
    • 84877057688 scopus 로고    scopus 로고
    • A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings
    • Pillow J.W., Shlens J., Chichilnisky E.J., Simoncelli E.P. A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings. PLOS ONE 2013, 8(5):e62123.
    • (2013) PLOS ONE , vol.8 , Issue.5 , pp. e62123
    • Pillow, J.W.1    Shlens, J.2    Chichilnisky, E.J.3    Simoncelli, E.P.4
  • 18
    • 3042526258 scopus 로고    scopus 로고
    • Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering
    • Quiroga R.Q., Nadasdy Z., Ben-Shaul Y. Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput 2004, 16(8):1661-1687.
    • (2004) Neural Comput , vol.16 , Issue.8 , pp. 1661-1687
    • Quiroga, R.Q.1    Nadasdy, Z.2    Ben-Shaul, Y.3
  • 19
    • 84865486349 scopus 로고    scopus 로고
    • Spike detection and clustering with unsupervised wavelet optimization in extracellular neural recordings
    • Shalchyan V., Jensen W., Farina D. Spike detection and clustering with unsupervised wavelet optimization in extracellular neural recordings. IEEE Trans Biomed Eng 2012, 59(9):2576-2585.
    • (2012) IEEE Trans Biomed Eng , vol.59 , Issue.9 , pp. 2576-2585
    • Shalchyan, V.1    Jensen, W.2    Farina, D.3
  • 20
    • 84893640689 scopus 로고    scopus 로고
    • Spike sorting for polytrodes: a divide and conquer approach
    • Swindale N.V., Spacek M.A. Spike sorting for polytrodes: a divide and conquer approach. Front Syst Neurosci 2014, 8:6. 10.3389/fnsys.2014.00006.
    • (2014) Front Syst Neurosci , vol.8 , pp. 6
    • Swindale, N.V.1    Spacek, M.A.2
  • 21
    • 0035532141 scopus 로고    scopus 로고
    • Estimating the number of clusters in a data set via the gap statistic
    • Tibshirani R., Walther G., Hastie T. Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc: Ser B (Stat Methodol) 2001, 63(2):411-423.
    • (2001) J R Stat Soc: Ser B (Stat Methodol) , vol.63 , Issue.2 , pp. 411-423
    • Tibshirani, R.1    Walther, G.2    Hastie, T.3
  • 22
    • 81855203192 scopus 로고    scopus 로고
    • A non-parametric method for automatic neural spike clustering based on the non-uniform distribution of the data
    • Tiganj Z., Mboup M. A non-parametric method for automatic neural spike clustering based on the non-uniform distribution of the data. J Neural Eng 2011, 8(6):066014.
    • (2011) J Neural Eng , vol.8 , Issue.6 , pp. 066014
    • Tiganj, Z.1    Mboup, M.2
  • 23
    • 84870450893 scopus 로고    scopus 로고
    • Neural spike sorting using iterative ICA and a deflation-based approach
    • Tiganj Z., Mboup M. Neural spike sorting using iterative ICA and a deflation-based approach. J Neural Eng 2012, 9(6):066002.
    • (2012) J Neural Eng , vol.9 , Issue.6 , pp. 066002
    • Tiganj, Z.1    Mboup, M.2
  • 24
    • 33745615441 scopus 로고    scopus 로고
    • Optimal filtering for spike sorting of multi-site electrode recordings
    • Vollgraf R., Munk M., Obermayer K. Optimal filtering for spike sorting of multi-site electrode recordings. Netw: Comput Neural Syst 2005, 16(1):85-113.
    • (2005) Netw: Comput Neural Syst , vol.16 , Issue.1 , pp. 85-113
    • Vollgraf, R.1    Munk, M.2    Obermayer, K.3
  • 25
    • 3543134272 scopus 로고    scopus 로고
    • Gap statistics for whole genome shotgun DNA sequencing projects
    • Wendl M.C., Yang S.P. Gap statistics for whole genome shotgun DNA sequencing projects. Bioinformatics 2004, 20(10):1527-1534.
    • (2004) Bioinformatics , vol.20 , Issue.10 , pp. 1527-1534
    • Wendl, M.C.1    Yang, S.P.2
  • 26
    • 82055185458 scopus 로고    scopus 로고
    • Performance comparison of extracellular spike sorting algorithms for single-channel recordings
    • Wild J., Prekopcsak Z., Sieger T., Novak D., Jech R. Performance comparison of extracellular spike sorting algorithms for single-channel recordings. J Neurosci Methods 2012, 203(2):369-376.
    • (2012) J Neurosci Methods , vol.203 , Issue.2 , pp. 369-376
    • Wild, J.1    Prekopcsak, Z.2    Sieger, T.3    Novak, D.4    Jech, R.5
  • 27
    • 78650607840 scopus 로고    scopus 로고
    • A multiscale correlation of wavelet coefficients approach to spike detection
    • Yang C., Olson B., Si J. A multiscale correlation of wavelet coefficients approach to spike detection. Neural Comput 2011, 23(1):215-250.
    • (2011) Neural Comput , vol.23 , Issue.1 , pp. 215-250
    • Yang, C.1    Olson, B.2    Si, J.3
  • 28
    • 47149116088 scopus 로고    scopus 로고
    • Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens
    • Yin Z., Zhou X., Bakal C., Li F., Sun Y., Perrimon N., et al. Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens. BMC Bioinformatics 2008, 9(1):264.
    • (2008) BMC Bioinformatics , vol.9 , Issue.1 , pp. 264
    • Yin, Z.1    Zhou, X.2    Bakal, C.3    Li, F.4    Sun, Y.5    Perrimon, N.6
  • 29
    • 84865427229 scopus 로고    scopus 로고
    • The M-Sorter: an automatic and robust spike detection and classification system
    • Yuan Y., Yang C., Si J. The M-Sorter: an automatic and robust spike detection and classification system. J Neurosci Methods 2012, 210(2):281-290.
    • (2012) J Neurosci Methods , vol.210 , Issue.2 , pp. 281-290
    • Yuan, Y.1    Yang, C.2    Si, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.