메뉴 건너뛰기




Volumn 38, Issue 5, 2015, Pages 307-318

Towards the automatic classification of neurons

Author keywords

Big data; Machine learning; Metadata; Neural classification; Standardization

Indexed keywords

MOLECULAR MARKER;

EID: 84928434625     PISSN: 01662236     EISSN: 1878108X     Source Type: Journal    
DOI: 10.1016/j.tins.2015.02.004     Document Type: Review
Times cited : (81)

References (114)
  • 1
    • 0001597874 scopus 로고
    • El nuevo concepto de la histología de los centros nerviosos
    • Ramón y Cajal S. El nuevo concepto de la histología de los centros nerviosos. Rev. Cienc. Méd. 1892, 18:457-476.
    • (1892) Rev. Cienc. Méd. , vol.18 , pp. 457-476
    • Ramón y Cajal, S.1
  • 2
    • 84877615795 scopus 로고    scopus 로고
    • The NIH BRAIN Initiative
    • Insel T.R., et al. The NIH BRAIN Initiative. Science 2013, 340:687-688.
    • (2013) Science , vol.340 , pp. 687-688
    • Insel, T.R.1
  • 3
    • 84861414911 scopus 로고    scopus 로고
    • The Human Brain Project
    • Markram H. The Human Brain Project. Sci. Am. 2012, 306:50-55.
    • (2012) Sci. Am. , vol.306 , pp. 50-55
    • Markram, H.1
  • 4
    • 45749115353 scopus 로고    scopus 로고
    • Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex
    • Ascoli G.A., et al. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat. Rev. Neurosci. 2008, 9:557-568.
    • (2008) Nat. Rev. Neurosci. , vol.9 , pp. 557-568
    • Ascoli, G.A.1
  • 5
    • 29444452573 scopus 로고    scopus 로고
    • Molecular taxonomy of major neuronal classes in the adult mouse forebrain
    • Sugino K., et al. Molecular taxonomy of major neuronal classes in the adult mouse forebrain. Nat. Neurosci. 2006, 9:99-107.
    • (2006) Nat. Neurosci. , vol.9 , pp. 99-107
    • Sugino, K.1
  • 6
    • 34548760721 scopus 로고    scopus 로고
    • Quantitative expression profiling of identified neurons reveals cell-specific constraints on highly variable levels of gene expression
    • Schulz D.J., et al. Quantitative expression profiling of identified neurons reveals cell-specific constraints on highly variable levels of gene expression. Proc. Natl. Acad. Sci. U.S.A. 2007, 104:13187-13191.
    • (2007) Proc. Natl. Acad. Sci. U.S.A. , vol.104 , pp. 13187-13191
    • Schulz, D.J.1
  • 7
    • 38149129457 scopus 로고    scopus 로고
    • A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function
    • Cahoy J.D., et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 2008, 28:264-278.
    • (2008) J. Neurosci. , vol.28 , pp. 264-278
    • Cahoy, J.D.1
  • 8
    • 61349201471 scopus 로고    scopus 로고
    • Biophysical support for fnctionally distinct cell types in the frontal eye field
    • Cohen J.Y., et al. Biophysical support for fnctionally distinct cell types in the frontal eye field. J. Neurophysiol. 2009, 101:912-916.
    • (2009) J. Neurophysiol. , vol.101 , pp. 912-916
    • Cohen, J.Y.1
  • 9
    • 63849258894 scopus 로고    scopus 로고
    • Classification of NPY-expressing neocortical interneurons
    • Karagiannis A., et al. Classification of NPY-expressing neocortical interneurons. J. Neurosci. 2009, 29:3642-3659.
    • (2009) J. Neurosci. , vol.29 , pp. 3642-3659
    • Karagiannis, A.1
  • 10
    • 67651091753 scopus 로고    scopus 로고
    • Novel markers reveal subpopulations of subplate neurons in the murine cerebral cortex
    • Hoerder-Suabedissen A., et al. Novel markers reveal subpopulations of subplate neurons in the murine cerebral cortex. Cereb. Cortex 2009, 19:1738-1750.
    • (2009) Cereb. Cortex , vol.19 , pp. 1738-1750
    • Hoerder-Suabedissen, A.1
  • 11
    • 77953423433 scopus 로고    scopus 로고
    • Quantitative classification of somatostatin-positive neocortical interneurons identifies three interneuron subtypes
    • McGarry L.M., et al. Quantitative classification of somatostatin-positive neocortical interneurons identifies three interneuron subtypes. Front. Neural Circuits 2010, 4:12.
    • (2010) Front. Neural Circuits , vol.4 , pp. 12
    • McGarry, L.M.1
  • 12
    • 78650043633 scopus 로고    scopus 로고
    • Comparison between supervised and unsupervised classifications of neuronal cell types: a case study
    • Guerra L., et al. Comparison between supervised and unsupervised classifications of neuronal cell types: a case study. Dev. Neurobiol. 2011, 71:71-82.
    • (2011) Dev. Neurobiol. , vol.71 , pp. 71-82
    • Guerra, L.1
  • 13
    • 79955164276 scopus 로고    scopus 로고
    • Physiological clustering of visual channels in the mouse retina
    • Farrow K., Masland R.H. Physiological clustering of visual channels in the mouse retina. J. Neurophysiol. 2011, 105:1516-1530.
    • (2011) J. Neurophysiol. , vol.105 , pp. 1516-1530
    • Farrow, K.1    Masland, R.H.2
  • 14
    • 79551520605 scopus 로고    scopus 로고
    • A quantitative comparison of cell-type-specific microarray gene expression profiling methods in the mouse brain
    • Okaty B.W., et al. A quantitative comparison of cell-type-specific microarray gene expression profiling methods in the mouse brain. PLoS ONE 2011, 6:e16393.
    • (2011) PLoS ONE , vol.6
    • Okaty, B.W.1
  • 15
    • 84861122283 scopus 로고    scopus 로고
    • Quantitative unit classification of ventral tegmental area neurons in vivo
    • Li W., et al. Quantitative unit classification of ventral tegmental area neurons in vivo. J. Neurophysiol. 2012, 107:2808-2820.
    • (2012) J. Neurophysiol. , vol.107 , pp. 2808-2820
    • Li, W.1
  • 16
    • 84863941523 scopus 로고    scopus 로고
    • A mutual information approach to automate identification of neuronal clusters in Drosophila brain images
    • Masse N.Y., et al. A mutual information approach to automate identification of neuronal clusters in Drosophila brain images. Front. Neuroinform. 2012, 6:21.
    • (2012) Front. Neuroinform. , vol.6 , pp. 21
    • Masse, N.Y.1
  • 17
    • 84875424532 scopus 로고    scopus 로고
    • New insights into the classification and nomenclature of cortical GABAergic interneurons
    • DeFelipe J., et al. New insights into the classification and nomenclature of cortical GABAergic interneurons. Nat. Rev. Neurosci. 2013, 14:202-216.
    • (2013) Nat. Rev. Neurosci. , vol.14 , pp. 202-216
    • DeFelipe, J.1
  • 18
    • 84873107608 scopus 로고    scopus 로고
    • Beyond the frontiers of neuronal types
    • Battaglia D., et al. Beyond the frontiers of neuronal types. Front. Neural Circuits 2013, 7:13.
    • (2013) Front. Neural Circuits , vol.7 , pp. 13
    • Battaglia, D.1
  • 19
    • 84902899552 scopus 로고    scopus 로고
    • A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis
    • Druckmann S., et al. A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis. Cereb. Cortex 2013, 23:2994-3006.
    • (2013) Cereb. Cortex , vol.23 , pp. 2994-3006
    • Druckmann, S.1
  • 20
    • 84910596185 scopus 로고    scopus 로고
    • Characterization and distribution of reelin-positive interneuron subtypes in the rat barrel cortex
    • Pohlkamp T., et al. Characterization and distribution of reelin-positive interneuron subtypes in the rat barrel cortex. Cereb. Cortex 2013, 24:3046-3058.
    • (2013) Cereb. Cortex , vol.24 , pp. 3046-3058
    • Pohlkamp, T.1
  • 21
    • 84889596969 scopus 로고    scopus 로고
    • Classification of neocortical interneurons using affinity propagation
    • Santana R., et al. Classification of neocortical interneurons using affinity propagation. Front. Neural Circuits 2013, 7:185.
    • (2013) Front. Neural Circuits , vol.7 , pp. 185
    • Santana, R.1
  • 22
    • 84889569751 scopus 로고    scopus 로고
    • Bayesian network modeling of the consensus between experts: an application to neuron classification
    • López-Cruz P.L., et al. Bayesian network modeling of the consensus between experts: an application to neuron classification. Int. J. Approx. Reason. 2014, 55:3-22.
    • (2014) Int. J. Approx. Reason. , vol.55 , pp. 3-22
    • López-Cruz, P.L.1
  • 23
    • 84928422404 scopus 로고    scopus 로고
    • Quantitative arbor analytics: Unsupervised harmonic co-clustering of populations of brain cell arbors based on L-Measure
    • Lu Y., et al. Quantitative arbor analytics: Unsupervised harmonic co-clustering of populations of brain cell arbors based on L-Measure. Neuroinformatics 2015, 13:47-63.
    • (2015) Neuroinformatics , vol.13 , pp. 47-63
    • Lu, Y.1
  • 24
    • 84903177431 scopus 로고    scopus 로고
    • A genetic and computational approach to structurally classify neuronal types
    • Sümbül U., et al. A genetic and computational approach to structurally classify neuronal types. Nat. Commun. 2014, 5:3512.
    • (2014) Nat. Commun. , vol.5 , pp. 3512
    • Sümbül, U.1
  • 25
    • 84908235789 scopus 로고    scopus 로고
    • Neurosharing: large-scale data sets (spike, LFP) recorded from the hippocampal-entorhinal system in behaving rats
    • Mizuseki K., et al. Neurosharing: large-scale data sets (spike, LFP) recorded from the hippocampal-entorhinal system in behaving rats. F1000Res 2014, 3:98.
    • (2014) F1000Res , vol.3 , pp. 98
    • Mizuseki, K.1
  • 26
    • 84928418968 scopus 로고    scopus 로고
    • NBLAST: rapid, sensitive comparison of neuronal structure and construction of neuron family databases
    • Published online August 9, 2014
    • Costa M., et al. NBLAST: rapid, sensitive comparison of neuronal structure and construction of neuron family databases. BioRxiv 2014, Published online August 9, 2014. http://dx.doi.org/10.1101/006346.
    • (2014) BioRxiv
    • Costa, M.1
  • 27
    • 33748849648 scopus 로고    scopus 로고
    • Machine learning in bioinformatics
    • Larrañaga P., et al. Machine learning in bioinformatics. Brief. Bioinform. 2006, 7:86-112.
    • (2006) Brief. Bioinform. , vol.7 , pp. 86-112
    • Larrañaga, P.1
  • 28
    • 79951548200 scopus 로고    scopus 로고
    • Challenges and opportunities in mining neuroscience data
    • Akil H., et al. Challenges and opportunities in mining neuroscience data. Science 2011, 331:708-712.
    • (2011) Science , vol.331 , pp. 708-712
    • Akil, H.1
  • 29
    • 84875547168 scopus 로고    scopus 로고
    • Neuronal morphology goes digital: a research hub for cellular and system neuroscience
    • Parekh R., Ascoli G.A. Neuronal morphology goes digital: a research hub for cellular and system neuroscience. Neuron 2013, 77:1017-1038.
    • (2013) Neuron , vol.77 , pp. 1017-1038
    • Parekh, R.1    Ascoli, G.A.2
  • 31
    • 36049001150 scopus 로고    scopus 로고
    • The neuron classification problem
    • Bota M., Swanson L.W. The neuron classification problem. Brain Res. Rev. 2007, 56:79-88.
    • (2007) Brain Res. Rev. , vol.56 , pp. 79-88
    • Bota, M.1    Swanson, L.W.2
  • 32
    • 84920129218 scopus 로고    scopus 로고
    • Toward functional classification of neuronal types
    • Sharpee T.O. Toward functional classification of neuronal types. Neuron 2014, 83:1329-1334.
    • (2014) Neuron , vol.83 , pp. 1329-1334
    • Sharpee, T.O.1
  • 33
    • 84891468640 scopus 로고    scopus 로고
    • Classification and function of GABAergic interneurons of the mammalian cerebral cortex
    • Zaitsev A.V. Classification and function of GABAergic interneurons of the mammalian cerebral cortex. Biochem. (Moscow) Suppl. Ser. A: Membr. Cell Biol. 2013, 7:245-259.
    • (2013) Biochem. (Moscow) Suppl. Ser. A: Membr. Cell Biol. , vol.7 , pp. 245-259
    • Zaitsev, A.V.1
  • 34
    • 84858320460 scopus 로고    scopus 로고
    • Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms
    • Mensi S., et al. Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. J. Neurophysiol. 2012, 107:1756-1775.
    • (2012) J. Neurophysiol. , vol.107 , pp. 1756-1775
    • Mensi, S.1
  • 35
    • 0000459353 scopus 로고    scopus 로고
    • The lack of a priori distinctions between learning algorithms
    • Wolpert D. The lack of a priori distinctions between learning algorithms. Neural Comput. 1996, 8:1341-1390.
    • (1996) Neural Comput. , vol.8 , pp. 1341-1390
    • Wolpert, D.1
  • 36
    • 84930328396 scopus 로고    scopus 로고
    • Quantitative investigations of axonal and dendritic arbors: development, structure, function, and pathology
    • Published online June 27, 2014
    • Parekh R., Ascoli G.A. Quantitative investigations of axonal and dendritic arbors: development, structure, function, and pathology. Neuroscientist 2014, Published online June 27, 2014. http://dx.doi.org/10.1177/1073858414540216.
    • (2014) Neuroscientist
    • Parekh, R.1    Ascoli, G.A.2
  • 37
    • 0032193939 scopus 로고    scopus 로고
    • Neural cell classification by wavelets and multiscale curvature
    • Cesar Junior R.M., Costa L.F. Neural cell classification by wavelets and multiscale curvature. Biol. Cybern. 1998, 79:347-360.
    • (1998) Biol. Cybern. , vol.79 , pp. 347-360
    • Cesar Junior, R.M.1    Costa, L.F.2
  • 38
    • 79551516806 scopus 로고    scopus 로고
    • BAMS neuroanatomical ontology: design and implementation
    • Bota M., Swanson L.W. BAMS neuroanatomical ontology: design and implementation. Front. Neuroinform. 2008, 2:2.
    • (2008) Front. Neuroinform. , vol.2 , pp. 2
    • Bota, M.1    Swanson, L.W.2
  • 39
    • 84929964433 scopus 로고    scopus 로고
    • Bayesian network classifiers for categorizing cortical GABAergic interneurons
    • Published online November 25, 2015
    • Mihaljevic B., et al. Bayesian network classifiers for categorizing cortical GABAergic interneurons. Neuroinformatics 2014, Published online November 25, 2015. http://dx.doi.org/10.1007/s12021-014-9254-1.
    • (2014) Neuroinformatics
    • Mihaljevic, B.1
  • 40
    • 3543144446 scopus 로고
    • Dendritic organization in the neurons of the visual and motor cortices of the cat
    • Sholl D.A. Dendritic organization in the neurons of the visual and motor cortices of the cat. J. Anat. 1953, 87:387-406.
    • (1953) J. Anat. , vol.87 , pp. 387-406
    • Sholl, D.A.1
  • 41
    • 43149098519 scopus 로고    scopus 로고
    • L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies
    • Scorcioni R., et al. L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nat. Protoc. 2008, 3:866-876.
    • (2008) Nat. Protoc. , vol.3 , pp. 866-876
    • Scorcioni, R.1
  • 42
    • 79958158724 scopus 로고    scopus 로고
    • The FARSIGHT trace editor: an open source tool for 3-D Inspection and efficient pattern analysis aided editing of automated neuronal reconstructions
    • Luisi J., et al. The FARSIGHT trace editor: an open source tool for 3-D Inspection and efficient pattern analysis aided editing of automated neuronal reconstructions. Neuroinformatics 2011, 9:305-315.
    • (2011) Neuroinformatics , vol.9 , pp. 305-315
    • Luisi, J.1
  • 43
    • 84928416618 scopus 로고    scopus 로고
    • Statistical analysis and data mining of digital reconstructions of dendritic morphologies
    • Polavaram S., et al. Statistical analysis and data mining of digital reconstructions of dendritic morphologies. Front. Neuroanat. 2014, 8:138.
    • (2014) Front. Neuroanat. , vol.8 , pp. 138
    • Polavaram, S.1
  • 44
    • 84868303503 scopus 로고    scopus 로고
    • Morphological homogeneity of neurons: searching for outlier neuronal cells
    • Zawadzki K., et al. Morphological homogeneity of neurons: searching for outlier neuronal cells. Neuroinformatics 2012, 10:379-389.
    • (2012) Neuroinformatics , vol.10 , pp. 379-389
    • Zawadzki, K.1
  • 45
    • 84867598660 scopus 로고    scopus 로고
    • Digital morphometry of rat cerebellar climbing fibers reveals distinct branch and bouton types
    • Brown K., et al. Digital morphometry of rat cerebellar climbing fibers reveals distinct branch and bouton types. J. Neurosci. 2012, 32:14670-14684.
    • (2012) J. Neurosci. , vol.32 , pp. 14670-14684
    • Brown, K.1
  • 46
    • 84859833067 scopus 로고    scopus 로고
    • High-resolution imaging and evaluation of spines in organotypic hippocampal slice cultures
    • Sündermann F., et al. High-resolution imaging and evaluation of spines in organotypic hippocampal slice cultures. Methods Mol. Biol. 2012, 846:277-293.
    • (2012) Methods Mol. Biol. , vol.846 , pp. 277-293
    • Sündermann, F.1
  • 47
    • 84928421900 scopus 로고    scopus 로고
    • Investigation of a random graph model for neuronal connectivity
    • Marchette D.J., et al. Investigation of a random graph model for neuronal connectivity. Joint Mtg. Am. Math. Soc. 2012, 91.
    • (2012) Joint Mtg. Am. Math. Soc. , pp. 91
    • Marchette, D.J.1
  • 48
    • 85046767900 scopus 로고    scopus 로고
    • Automatic neuron type identification by neurite localization in the Drosophila medulla
    • Published online September 5, 2014.
    • Zhao T., Plaza S.M. Automatic neuron type identification by neurite localization in the Drosophila medulla. ArXiv 2014, Published online September 5, 2014. http://arxiv.org/abs/1409.1892.
    • (2014) ArXiv
    • Zhao, T.1    Plaza, S.M.2
  • 49
    • 3242876311 scopus 로고    scopus 로고
    • BLAST: at the core of a powerful and diverse set of sequence analysis tools
    • McGinnis S., Madden T.L. BLAST: at the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res. 2004, 32:W20-W25.
    • (2004) Nucleic Acids Res. , vol.32 , pp. W20-W25
    • McGinnis, S.1    Madden, T.L.2
  • 50
    • 79151480913 scopus 로고    scopus 로고
    • Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution
    • Chiang A.S., et al. Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution. Curr. Biol. 2011, 21:1-11.
    • (2011) Curr. Biol. , vol.21 , pp. 1-11
    • Chiang, A.S.1
  • 51
    • 33847172327 scopus 로고    scopus 로고
    • Clustering by passing messages between data points
    • Frey B.J., Dueck D. Clustering by passing messages between data points. Science 2007, 315:972-976.
    • (2007) Science , vol.315 , pp. 972-976
    • Frey, B.J.1    Dueck, D.2
  • 52
    • 70449523119 scopus 로고    scopus 로고
    • Patterning and organization of motor neuron dendrites in the Drosophila larva
    • Kim M.D., et al. Patterning and organization of motor neuron dendrites in the Drosophila larva. Dev. Biol. 2009, 336:213-221.
    • (2009) Dev. Biol. , vol.336 , pp. 213-221
    • Kim, M.D.1
  • 54
    • 84893487877 scopus 로고    scopus 로고
    • Part-based motor neuron recognition in the Drosophila ventral nerve cord
    • Chang X., et al. Part-based motor neuron recognition in the Drosophila ventral nerve cord. Neuroimage 2013, 90:33-42.
    • (2013) Neuroimage , vol.90 , pp. 33-42
    • Chang, X.1
  • 55
    • 0028202537 scopus 로고
    • Electrophysiological characterization of myenteric neurons: how do classification schemes relate?
    • Bornstein J.C., et al. Electrophysiological characterization of myenteric neurons: how do classification schemes relate?. J. Auton. Nerv. Syst. 1994, 48:1-15.
    • (1994) J. Auton. Nerv. Syst. , vol.48 , pp. 1-15
    • Bornstein, J.C.1
  • 56
    • 0142039676 scopus 로고    scopus 로고
    • Neuron classification based on temporal firing patterns by the dynamical analysis with changing time resolution (DCT) method
    • Oshio K., et al. Neuron classification based on temporal firing patterns by the dynamical analysis with changing time resolution (DCT) method. Biol. Cybern. 2003, 88:438-449.
    • (2003) Biol. Cybern. , vol.88 , pp. 438-449
    • Oshio, K.1
  • 57
    • 0023680404 scopus 로고
    • Unsupervised waveform classification for multi-neuron recordings: a real-time, software-based system. I. Algorithms and implementation
    • Salganicoff M., et al. Unsupervised waveform classification for multi-neuron recordings: a real-time, software-based system. I. Algorithms and implementation. J. Neurosci. Methods 1988, 25:181-187.
    • (1988) J. Neurosci. Methods , vol.25 , pp. 181-187
    • Salganicoff, M.1
  • 58
    • 77950375054 scopus 로고    scopus 로고
    • Classification of extracellularly recorded neurons by their discharge patterns and their correlates with intracellularly identified neuronal types in the frontal cortex of behaving monkeys
    • Katai S., et al. Classification of extracellularly recorded neurons by their discharge patterns and their correlates with intracellularly identified neuronal types in the frontal cortex of behaving monkeys. Eur. J. Neurosci. 2010, 31:1322-1338.
    • (2010) Eur. J. Neurosci. , vol.31 , pp. 1322-1338
    • Katai, S.1
  • 59
    • 84864007907 scopus 로고    scopus 로고
    • Electrophysiological classes of layer 2/3 pyramidal cells in monkey prefrontal cortex
    • Zaitsev A.V., et al. Electrophysiological classes of layer 2/3 pyramidal cells in monkey prefrontal cortex. J. Neurophysiol. 2012, 108:595-609.
    • (2012) J. Neurophysiol. , vol.108 , pp. 595-609
    • Zaitsev, A.V.1
  • 60
    • 84878497710 scopus 로고    scopus 로고
    • Subgroups of parvalbumin-expressing interneurons in layers 2/3 of the visual cortex
    • Helm J., et al. Subgroups of parvalbumin-expressing interneurons in layers 2/3 of the visual cortex. J. Neurophysiol. 2013, 109:1600-1613.
    • (2013) J. Neurophysiol. , vol.109 , pp. 1600-1613
    • Helm, J.1
  • 61
    • 70350202553 scopus 로고    scopus 로고
    • Theta oscillations provide temporal windows for local circuit computation in the entorhinal hippocampal loop
    • Mizuseki K., et al. Theta oscillations provide temporal windows for local circuit computation in the entorhinal hippocampal loop. Neuron 2009, 64:267-280.
    • (2009) Neuron , vol.64 , pp. 267-280
    • Mizuseki, K.1
  • 62
    • 33745144588 scopus 로고    scopus 로고
    • The problem of neuronal cell types: a physiological genomics approach
    • Nelson S.B., et al. The problem of neuronal cell types: a physiological genomics approach. Trends Neurosci. 2006, 29:339-345.
    • (2006) Trends Neurosci. , vol.29 , pp. 339-345
    • Nelson, S.B.1
  • 63
    • 70450205344 scopus 로고    scopus 로고
    • Shifting the paradigm: new approaches for characterizing and classifying neurons
    • Bernard A., et al. Shifting the paradigm: new approaches for characterizing and classifying neurons. Curr. Opin. Neurobiol. 2009, 19:530-536.
    • (2009) Curr. Opin. Neurobiol. , vol.19 , pp. 530-536
    • Bernard, A.1
  • 64
    • 60749125717 scopus 로고    scopus 로고
    • An anatomic gene expression atlas of the adult mouse brain
    • Ng L., et al. An anatomic gene expression atlas of the adult mouse brain. Nat. Neurosci. 2009, 12:356-362.
    • (2009) Nat. Neurosci. , vol.12 , pp. 356-362
    • Ng, L.1
  • 65
    • 34249819336 scopus 로고    scopus 로고
    • MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals
    • Tsang J., et al. MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals. Mol. Cell 2007, 26:753-767.
    • (2007) Mol. Cell , vol.26 , pp. 753-767
    • Tsang, J.1
  • 66
    • 68749092138 scopus 로고    scopus 로고
    • The organization of the transcriptional network in specific neuronal classes
    • Winden K.D., et al. The organization of the transcriptional network in specific neuronal classes. Mol. Syst. Biol. 2009, 5:291.
    • (2009) Mol. Syst. Biol. , vol.5 , pp. 291
    • Winden, K.D.1
  • 67
    • 79960963398 scopus 로고    scopus 로고
    • A blueprint for the spatiotemporal origins of mouse hippocampal interneuron diversity
    • Tricoire L., et al. A blueprint for the spatiotemporal origins of mouse hippocampal interneuron diversity. J. Neurosci. 2011, 31:10948-10970.
    • (2011) J. Neurosci. , vol.31 , pp. 10948-10970
    • Tricoire, L.1
  • 68
    • 79956315360 scopus 로고    scopus 로고
    • Cell type-specific transcriptomics in the brain
    • Okaty B.W., et al. Cell type-specific transcriptomics in the brain. J. Neurosci. 2011, 31:6939-6943.
    • (2011) J. Neurosci. , vol.31 , pp. 6939-6943
    • Okaty, B.W.1
  • 69
    • 84866518216 scopus 로고    scopus 로고
    • An anatomically comprehensive atlas of the adult human brain transcriptome
    • Hawrylycz M.J., et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 2012, 489:391-399.
    • (2012) Nature , vol.489 , pp. 391-399
    • Hawrylycz, M.J.1
  • 70
    • 84925016216 scopus 로고    scopus 로고
    • Correlated gene expression and target specificity demonstrate excitatory projection neuron diversity
    • Sorensen S.A., et al. Correlated gene expression and target specificity demonstrate excitatory projection neuron diversity. Cereb. Cortex 2013, 25:433-439.
    • (2013) Cereb. Cortex , vol.25 , pp. 433-439
    • Sorensen, S.A.1
  • 71
    • 84900303453 scopus 로고    scopus 로고
    • Transcriptional landscape of the prenatal human brain
    • Miller J.A., et al. Transcriptional landscape of the prenatal human brain. Nature 2014, 508:199-206.
    • (2014) Nature , vol.508 , pp. 199-206
    • Miller, J.A.1
  • 72
    • 33645278642 scopus 로고    scopus 로고
    • Mobilizing the base of neuroscience data: the case of neuronal morphologies
    • Ascoli G.A. Mobilizing the base of neuroscience data: the case of neuronal morphologies. Nat. Rev. Neurosci. 2006, 7:318-324.
    • (2006) Nat. Rev. Neurosci. , vol.7 , pp. 318-324
    • Ascoli, G.A.1
  • 73
    • 56149096245 scopus 로고    scopus 로고
    • Data sharing for computational neuroscience
    • Teeters J.L., et al. Data sharing for computational neuroscience. Neuroinformatics 2008, 6:47-55.
    • (2008) Neuroinformatics , vol.6 , pp. 47-55
    • Teeters, J.L.1
  • 74
    • 84892451597 scopus 로고    scopus 로고
    • Why the resistance to statistical innovations? Bridging the communication gap
    • Sharpe D. Why the resistance to statistical innovations? Bridging the communication gap. Psychol. Methods 2013, 18:572-582.
    • (2013) Psychol. Methods , vol.18 , pp. 572-582
    • Sharpe, D.1
  • 75
    • 76749092270 scopus 로고    scopus 로고
    • The WEKA data mining software: an update
    • Hall M., et al. The WEKA data mining software: an update. SIGKDD Explorations 2009, 11:10-18.
    • (2009) SIGKDD Explorations , vol.11 , pp. 10-18
    • Hall, M.1
  • 76
    • 79953115330 scopus 로고    scopus 로고
    • KNIME: The Konstanz information miner
    • Springer Berlin Heidelberg, C. Preisach (Ed.)
    • Berthold M.R., et al. KNIME: The Konstanz information miner. Data Analysis, Machine Learning and Applications 2007, 319-326. Springer Berlin Heidelberg. C. Preisach (Ed.).
    • (2007) Data Analysis, Machine Learning and Applications , pp. 319-326
    • Berthold, M.R.1
  • 77
    • 77954666305 scopus 로고    scopus 로고
    • The SHOGUN machine learning toolbox
    • Sonnenburg S., et al. The SHOGUN machine learning toolbox. J. Mach. Learn. Res. 2010, 11:1799-1802.
    • (2010) J. Mach. Learn. Res. , vol.11 , pp. 1799-1802
    • Sonnenburg, S.1
  • 80
    • 84876665206 scopus 로고    scopus 로고
    • Power failure: why small sample size undermines the reliability of neuroscience
    • Button K.S., et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 2013, 14:365-376.
    • (2013) Nat. Rev. Neurosci. , vol.14 , pp. 365-376
    • Button, K.S.1
  • 82
    • 84860504202 scopus 로고    scopus 로고
    • Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests
    • Razali N., Wah Y.B. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. J. Stat. Model. Analytics 2011, 2:21-33.
    • (2011) J. Stat. Model. Analytics , vol.2 , pp. 21-33
    • Razali, N.1    Wah, Y.B.2
  • 83
    • 33744481028 scopus 로고    scopus 로고
    • Interneurons targeting similar layers receive synaptic inputs with similar kinetics
    • Cossart R., et al. Interneurons targeting similar layers receive synaptic inputs with similar kinetics. Hippocampus 2006, 16:408-420.
    • (2006) Hippocampus , vol.16 , pp. 408-420
    • Cossart, R.1
  • 84
    • 84892671870 scopus 로고    scopus 로고
    • Morpho-physiological criteria divide dentate gyrus interneurons into classes
    • Hosp J.A., et al. Morpho-physiological criteria divide dentate gyrus interneurons into classes. Hippocampus 2014, 24:189-203.
    • (2014) Hippocampus , vol.24 , pp. 189-203
    • Hosp, J.A.1
  • 85
    • 0032077287 scopus 로고    scopus 로고
    • How many subtypes of inhibitory cells in the hippocampus?
    • Parra P., et al. How many subtypes of inhibitory cells in the hippocampus?. Neuron 1998, 20:983-993.
    • (1998) Neuron , vol.20 , pp. 983-993
    • Parra, P.1
  • 86
    • 84928432973 scopus 로고    scopus 로고
    • Springer, I. Guyon (Ed.)
    • Feature Extraction: Foundations and Applications 2006, Springer. I. Guyon (Ed.).
    • (2006)
  • 87
    • 84928437206 scopus 로고    scopus 로고
    • Chapman and Hall/CRC, H. Liu, H. Motoda (Eds.)
    • Computational Methods of Feature Selection 2007, Chapman and Hall/CRC. H. Liu, H. Motoda (Eds.).
    • (2007)
  • 88
    • 35748932917 scopus 로고    scopus 로고
    • A review of feature selection techniques in bioinformatics
    • Saeys Y., et al. A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23:2507-2517.
    • (2007) Bioinformatics , vol.23 , pp. 2507-2517
    • Saeys, Y.1
  • 89
    • 75149166068 scopus 로고    scopus 로고
    • The coming of age of the hippocampome
    • Ascoli G.A. The coming of age of the hippocampome. Neuroinformatics 2010, 8:1-3.
    • (2010) Neuroinformatics , vol.8 , pp. 1-3
    • Ascoli, G.A.1
  • 91
    • 33748319377 scopus 로고    scopus 로고
    • Effects of β-catenin on dendritic morphology and simulated firing patterns in cultured hippocampal neurons
    • Krichmar J.L., et al. Effects of β-catenin on dendritic morphology and simulated firing patterns in cultured hippocampal neurons. Biol. Bull. 2006, 211:31-43.
    • (2006) Biol. Bull. , vol.211 , pp. 31-43
    • Krichmar, J.L.1
  • 92
    • 84868266043 scopus 로고    scopus 로고
    • Uncovering genes required for neuronal morphology by morphology-based gene trap screening with a revertible retrovirus vector
    • Hashimoto Y., et al. Uncovering genes required for neuronal morphology by morphology-based gene trap screening with a revertible retrovirus vector. FASEB J. 2012, 26:4662-4674.
    • (2012) FASEB J. , vol.26 , pp. 4662-4674
    • Hashimoto, Y.1
  • 93
    • 84925494182 scopus 로고    scopus 로고
    • A digital atlas of ion channel expression patterns in the two-week-old rat brain
    • Shcherbatyy V., et al. A digital atlas of ion channel expression patterns in the two-week-old rat brain. Neuroinformatics 2015, 13:111-125.
    • (2015) Neuroinformatics , vol.13 , pp. 111-125
    • Shcherbatyy, V.1
  • 94
    • 0022638213 scopus 로고
    • Cat and monkey retinal ganglion cells and their visual functional roles
    • Shapley R., Hugh Perry V. Cat and monkey retinal ganglion cells and their visual functional roles. Trends Neurosci. 1986, 9:229-235.
    • (1986) Trends Neurosci. , vol.9 , pp. 229-235
    • Shapley, R.1    Hugh Perry, V.2
  • 95
    • 84893351797 scopus 로고    scopus 로고
    • NIH plans to enhance reproducibility
    • Collins F.S., Tabak L.A. NIH plans to enhance reproducibility. Nature 2014, 505:612-613.
    • (2014) Nature , vol.505 , pp. 612-613
    • Collins, F.S.1    Tabak, L.A.2
  • 96
    • 84876040572 scopus 로고    scopus 로고
    • An ontological approach to describing neurons and their relationships
    • Hamilton D.J., et al. An ontological approach to describing neurons and their relationships. Front. Neuroinform. 2012, 6:15.
    • (2012) Front. Neuroinform. , vol.6 , pp. 15
    • Hamilton, D.J.1
  • 97
    • 18344396568 scopus 로고    scopus 로고
    • Minimum information about a microarray experiment (MIAME)-toward standards for microarray data
    • Brazma A., et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat. Genet. 2001, 29:365-371.
    • (2001) Nat. Genet. , vol.29 , pp. 365-371
    • Brazma, A.1
  • 98
    • 84975795680 scopus 로고    scopus 로고
    • An integrated map of genetic variation from 1,092 human genomes
    • The 1000 Genomes Project Consortium
    • An integrated map of genetic variation from 1,092 human genomes. Nature 2012, 491:56-65. The 1000 Genomes Project Consortium.
    • (2012) Nature , vol.491 , pp. 56-65
  • 99
    • 58149145635 scopus 로고    scopus 로고
    • Validation of biomarker-based risk prediction models
    • Taylor J.M.G., et al. Validation of biomarker-based risk prediction models. Clin. Cancer Res. 2008, 14:5977-5983.
    • (2008) Clin. Cancer Res. , vol.14 , pp. 5977-5983
    • Taylor, J.M.G.1
  • 100
    • 84888206480 scopus 로고    scopus 로고
    • A survey of L1 regression
    • Vidaurre D., et al. A survey of L1 regression. Int. Stat. Rev. 2013, 81:361-387.
    • (2013) Int. Stat. Rev. , vol.81 , pp. 361-387
    • Vidaurre, D.1
  • 101
    • 84861153094 scopus 로고    scopus 로고
    • Matrix eQTL: ultra fast eQTL analysis via large matrix operations
    • Shabalin A.A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 2012, 28:1353-1358.
    • (2012) Bioinformatics , vol.28 , pp. 1353-1358
    • Shabalin, A.A.1
  • 102
    • 84899766043 scopus 로고    scopus 로고
    • A two-graph guided multi-task Lasso approach for eQTL mapping
    • JMLR
    • Chen X., et al. A two-graph guided multi-task Lasso approach for eQTL mapping. Artificial Intelligence and Statistics 2012 2012, Vol. 22:208-217. JMLR.
    • (2012) Artificial Intelligence and Statistics 2012 , vol.22 , pp. 208-217
    • Chen, X.1
  • 103
    • 84860640101 scopus 로고    scopus 로고
    • Learning transcriptional regulatory relationships using sparse graphical models
    • Zhang X., et al. Learning transcriptional regulatory relationships using sparse graphical models. PLoS ONE 2012, 7:e35762.
    • (2012) PLoS ONE , vol.7
    • Zhang, X.1
  • 104
    • 79959443658 scopus 로고    scopus 로고
    • CcSVM: correcting support vector machines for confounding factors in biological data classification
    • Li L., et al. ccSVM: correcting support vector machines for confounding factors in biological data classification. Bioinformatics 2011, 27:i342-i348.
    • (2011) Bioinformatics , vol.27 , pp. i342-i348
    • Li, L.1
  • 105
    • 70649114623 scopus 로고    scopus 로고
    • Navigating random forests and related advances in algorithmic modeling
    • Siroky D.S. Navigating random forests and related advances in algorithmic modeling. Stat. Surv. 2009, 3:147-163.
    • (2009) Stat. Surv. , vol.3 , pp. 147-163
    • Siroky, D.S.1
  • 106
    • 84908508461 scopus 로고    scopus 로고
    • Putting big data to good use in neuroscience
    • Sejnowski T.J., et al. Putting big data to good use in neuroscience. Nat. Neurosci. 2014, 17:1440-1441.
    • (2014) Nat. Neurosci. , vol.17 , pp. 1440-1441
    • Sejnowski, T.J.1
  • 107
    • 0002015259 scopus 로고
    • The principles of classification and a classification of mammals
    • Simpson G.G. The principles of classification and a classification of mammals. Bull. Am. Mus. Nat. Hist. 1945, 85:22-24.
    • (1945) Bull. Am. Mus. Nat. Hist. , vol.85 , pp. 22-24
    • Simpson, G.G.1
  • 108
    • 84920113483 scopus 로고    scopus 로고
    • Neuronal cell types and connectivity: lessons from the retina
    • Seung H.S., Sümbül U. Neuronal cell types and connectivity: lessons from the retina. Neuron 2014, 83:1262-1272.
    • (2014) Neuron , vol.83 , pp. 1262-1272
    • Seung, H.S.1    Sümbül, U.2
  • 109
    • 0018015137 scopus 로고
    • Modeling by shortest data description
    • Rissanen J. Modeling by shortest data description. Automatica 1978, 14:465-658.
    • (1978) Automatica , vol.14 , pp. 465-658
    • Rissanen, J.1
  • 110
    • 0002593344 scopus 로고
    • Multi-interval discretization of continuous-valued attributes for classification learning
    • Morgan Kaufmann, D. Heckerman, E.H. Mamdani (Eds.)
    • Fayyad U., Irani K. Multi-interval discretization of continuous-valued attributes for classification learning. Proceedings of the International Joint Conference on Uncertainty in Artificial Intelligence 1993, 1022-1027. Morgan Kaufmann. D. Heckerman, E.H. Mamdani (Eds.).
    • (1993) Proceedings of the International Joint Conference on Uncertainty in Artificial Intelligence , pp. 1022-1027
    • Fayyad, U.1    Irani, K.2
  • 111
    • 0000120766 scopus 로고
    • Estimating the dimension of a model
    • Schwarz G.E. Estimating the dimension of a model. Ann. Stat. 1978, 6:461-464.
    • (1978) Ann. Stat. , vol.6 , pp. 461-464
    • Schwarz, G.E.1
  • 112
    • 0034303585 scopus 로고    scopus 로고
    • Neuronal specification in the spinal cord: inductive signals and transcriptional codes
    • Jessel T.M. Neuronal specification in the spinal cord: inductive signals and transcriptional codes. Nat. Rev. Genet. 2000, 1:20-29.
    • (2000) Nat. Rev. Genet. , vol.1 , pp. 20-29
    • Jessel, T.M.1
  • 113
    • 84899657155 scopus 로고    scopus 로고
    • Discovery of brainwide neural-behavioral maps via multiscale unsupervised structure learning
    • Vogelstein J.T., et al. Discovery of brainwide neural-behavioral maps via multiscale unsupervised structure learning. Science 2014, 344:386-392.
    • (2014) Science , vol.344 , pp. 386-392
    • Vogelstein, J.T.1
  • 114
    • 84892608163 scopus 로고    scopus 로고
    • Interneuron cell types are fit to function
    • Kepecs A., Fishell G. Interneuron cell types are fit to function. Nature 2014, 505:318-326.
    • (2014) Nature , vol.505 , pp. 318-326
    • Kepecs, A.1    Fishell, G.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.