Towards the automatic classification of neurons
暂无分享,去创建一个
Giorgio A. Ascoli | Rubén Armañanzas | G. Ascoli | Rubén Armañanzas | R. Armañanzas | Ruben Armañanzas Arnedillo
[1] David S. Siroky. Navigating Random Forests and related advances in algorithmic modeling , 2009 .
[2] D. Heckerman,et al. Learning Transcriptional Regulatory Relationships Using Sparse Graphical Models , 2012, PloS one.
[3] M. Salganicoff,et al. Unsupervised waveform classification for multi-neuron recordings: a real-time, software-based system. I. Algorithms and implementation , 1988, Journal of Neuroscience Methods.
[4] R. Yuste,et al. Classification of neocortical interneurons using affinity propagation , 2013, Front. Neural Circuits.
[5] D. Lewis,et al. Electrophysiological classes of layer 2/3 pyramidal cells in monkey prefrontal cortex. , 2012, Journal of neurophysiology.
[6] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[7] G. Buzsáki,et al. Theta Oscillations Provide Temporal Windows for Local Circuit Computation in the Entorhinal-Hippocampal Loop , 2009, Neuron.
[8] Guan-Yu Chen,et al. Three-Dimensional Reconstruction of Brain-wide Wiring Networks in Drosophila at Single-Cell Resolution , 2011, Current Biology.
[9] Richard P. Heitz,et al. Biophysical support for functionally distinct cell types in the frontal eye field. , 2009, Journal of neurophysiology.
[10] R. Miles,et al. How Many Subtypes of Inhibitory Cells in the Hippocampus? , 1998, Neuron.
[11] David Machin,et al. Sample Size Tables for Clinical Studies , 1997 .
[12] J. Bornstein,et al. Electrophysiological characterization of myenteric neurons: how do classification schemes relate? , 1994, Journal of the autonomic nervous system.
[13] G. Fishell,et al. Interneuron cell types are fit to function , 2014, Nature.
[14] Akira Chiba,et al. Motor neuron morphology estimation for its classification in the Drosophila brain , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[15] S. Nelson,et al. The problem of neuronal cell types: a physiological genomics approach , 2006, Trends in Neurosciences.
[16] M. Frotscher,et al. Characterization and distribution of Reelin-positive interneuron subtypes in the rat barrel cortex. , 2014, Cerebral cortex.
[17] Concha Bielza,et al. Bayesian Network Classifiers for Categorizing Cortical GABAergic Interneurons , 2015, Neuroinformatics.
[18] J Anthony Movshon,et al. Putting big data to good use in neuroscience , 2014, Nature Neuroscience.
[19] R. Masland,et al. Physiological clustering of visual channels in the mouse retina. , 2011, Journal of neurophysiology.
[20] Ronald R. Coifman,et al. Quantitative Arbor Analytics: Unsupervised Harmonic Co-Clustering of Populations of Brain Cell Arbors Based on L-Measure , 2014, Neuroinformatics.
[21] Maryann E. Martone,et al. An ontological approach to describing neurons and their relationships , 2012, Front. Neuroinform..
[22] G. Ascoli,et al. Effects of β-Catenin on Dendritic Morphology and Simulated Firing Patterns in Cultured Hippocampal Neurons , 2006, The Biological Bulletin.
[23] Sholl Da. Dendritic organization in the neurons of the visual and motor cortices of the cat. , 1953 .
[24] Demian Battaglia,et al. Classification of NPY-Expressing Neocortical Interneurons , 2009, The Journal of Neuroscience.
[25] G. Ascoli. Mobilizing the base of neuroscience data: the case of neuronal morphologies , 2006, Nature Reviews Neuroscience.
[26] George Gaylord Simpson,et al. The Principles of Classification and a Classification of Mammals. , 1945 .
[27] Giorgio A Ascoli,et al. Quantitative Investigations of Axonal and Dendritic Arbors , 2015, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
[28] R. Yuste,et al. Neural Circuits Original Research Article Materials and Methods Preparation of Brain Slices , 2022 .
[29] Allan R. Jones,et al. Transcriptional Landscape of the Prenatal Human Brain , 2014, Nature.
[30] W. Gerstner,et al. Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. , 2012, Journal of neurophysiology.
[31] Delbert Dueck,et al. Clustering by Passing Messages Between Data Points , 2007, Science.
[32] Nicolas Y. Masse,et al. A Mutual Information Approach to Automate Identification of Neuronal Clusters in Drosophila Brain Images , 2012, Front. Neuroinform..
[33] Jean-Marc Goaillard,et al. Quantitative expression profiling of identified neurons reveals cell-specific constraints on highly variable levels of gene expression , 2007, Proceedings of the National Academy of Sciences.
[34] Henry Markram,et al. The human brain project. , 2012, Scientific American.
[35] Gregor Eichele,et al. A Digital Atlas of Ion Channel Expression Patterns in the Two-Week-Old Rat Brain , 2014, Neuroinformatics.
[36] Ting Zhao,et al. Automatic Neuron Type Identification by Neurite Localization in the Drosophila Medulla , 2014, ArXiv.
[37] J. Rice. Mathematical Statistics and Data Analysis , 1988 .
[38] Steve Horvath,et al. Molecular Systems Biology 5; Article number 291; doi:10.1038/msb.2009.46 Citation: Molecular Systems Biology 5:291 , 2022 .
[39] Jacob Cohen. Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.
[40] Ruchi Parekh,et al. Neuronal Morphology Goes Digital: A Research Hub for Cellular and System Neuroscience , 2013, Neuron.
[41] Tatyana O. Sharpee,et al. Toward Functional Classification of Neuronal Types , 2014, Neuron.
[42] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[43] Gunnar Rätsch,et al. The SHOGUN Machine Learning Toolbox , 2010, J. Mach. Learn. Res..
[44] F. Collins,et al. NIH plans to enhance reproducibility , 2014 .
[45] A. Zaitsev. Classification and function of GABAergic interneurons of the mammalian cerebral cortex , 2013, Biochemistry (Moscow) Supplement Series A: Membrane and Cell Biology.
[46] Usama M. Fayyad,et al. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.
[47] Jason E. Stewart,et al. Minimum information about a microarray experiment (MIAME)—toward standards for microarray data , 2001, Nature Genetics.
[48] Thorsten Meinl,et al. KNIME: The Konstanz Information Miner , 2007, GfKl.
[49] A. Harada,et al. Uncovering genes required for neuronal morphology by morphology-based gene trap screening with a revertible retrovirus vector , 2012, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.
[50] G. Ascoli,et al. Neuronal Morphology Goes Digital: A Research Hub for Cellular and System Neuroscience , 2013, Neuron.
[51] David H. Wolpert,et al. The Lack of A Priori Distinctions Between Learning Algorithms , 1996, Neural Computation.
[52] Larry W. Swanson,et al. The neuron classification problem , 2007, Brain Research Reviews.
[53] L. Wollmuth,et al. Subgroups of parvalbumin-expressing interneurons in layers 2/3 of the visual cortex. , 2013, Journal of neurophysiology.
[54] O. Paulsen,et al. Novel markers reveal subpopulations of subplate neurons in the murine cerebral cortex. , 2009, Cerebral cortex.
[55] Hiroshi Motoda,et al. Computational Methods of Feature Selection , 2022 .
[56] Luciano da Fontoura Costa,et al. Neural cell classification by Wavelets and multiscale curvature , 1998 .
[57] Kenny Q. Ye,et al. An integrated map of genetic variation from 1,092 human genomes , 2012, Nature.
[58] S. Nelson,et al. Molecular taxonomy of major neuronal classes in the adult mouse forebrain , 2006, Nature Neuroscience.
[59] Michael D. Kim,et al. Patterning and organization of motor neuron dendrites in the Drosophila larva. , 2009, Developmental biology.
[60] S. Nelson,et al. Cell Type-Specific Transcriptomics in the Brain , 2011, The Journal of Neuroscience.
[61] Concha Bielza,et al. A Survey of L1 Regression , 2013 .
[62] Akira Chiba,et al. Part-based motor neuron recognition in the Drosophila ventral nerve cord , 2014, NeuroImage.
[63] Concha Bielza,et al. New insights into the classification and nomenclature of cortical GABAergic interneurons , 2013, Nature Reviews Neuroscience.
[64] Matheus Palhares Viana,et al. Morphological Homogeneity of Neurons: Searching for Outlier Neuronal Cells , 2012, Neuroinformatics.
[65] Hongkui Zeng,et al. Correlated gene expression and target specificity demonstrate excitatory projection neuron diversity. , 2015, Cerebral cortex.
[66] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[67] Masato Inoue,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 , 2010, The European journal of neuroscience.
[68] F. Collins,et al. Policy: NIH plans to enhance reproducibility , 2014, Nature.
[69] G. Ascoli,et al. L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies , 2008, Nature Protocols.
[70] Thomas L. Madden,et al. BLAST: at the core of a powerful and diverse set of sequence analysis tools , 2004, Nucleic Acids Res..
[71] Allan R. Jones,et al. An anatomically comprehensive atlas of the adult human brain transcriptome , 2012, Nature.
[72] Concha Bielza,et al. Bayesian network modeling of the consensus between experts: An application to neuron classification , 2014 .
[73] Brian A. Nosek,et al. Power failure: why small sample size undermines the reliability of neuroscience , 2013, Nature Reviews Neuroscience.
[74] Kenneth D. Harris,et al. Data Sharing for Computational Neuroscience , 2008, Neuroinformatics.
[75] D. Santiago Ramon Y Cajal,et al. Nuevo Concepto de la Histologia de los Centros Nerviosos , 1893, The Indian Medical Gazette.
[76] R. Kerr,et al. Discovery of Brainwide Neural-Behavioral Maps via Multiscale Unsupervised Structure Learning , 2014, Science.
[77] J. A. Dani,et al. Quantitative unit classification of ventral tegmental area neurons in vivo. , 2012, Journal of neurophysiology.
[78] Xiaohui Chen,et al. A Two-Graph Guided Multi-task Lasso Approach for eQTL Mapping , 2012, AISTATS.
[79] Frederik Sündermann,et al. High-resolution imaging and evaluation of spines in organotypic hippocampal slice cultures. , 2012, Methods in molecular biology.
[80] Chris J. McBain,et al. A Blueprint for the Spatiotemporal Origins of Mouse Hippocampal Interneuron Diversity , 2011, The Journal of Neuroscience.
[81] Gregory S.X.E. Jefferis,et al. NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and Construction of Neuron Family Databases , 2016, Neuron.
[82] T. Jessell. Neuronal specification in the spinal cord: inductive signals and transcriptional codes , 2000, Nature Reviews Genetics.
[83] Kamran Diba,et al. Neurosharing: large-scale data sets (spike, LFP) recorded from the hippocampal-entorhinal system in behaving rats , 2014, F1000Research.
[84] T. Insel,et al. The NIH BRAIN Initiative , 2013, Science.
[85] E. P. Gardner,et al. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex , 2008, Nature Reviews Neuroscience.
[86] Sen Song,et al. A genetic and computational approach to structurally classify neuronal types , 2014, Nature Communications.
[87] Staci A. Sorensen,et al. Shifting the paradigm: new approaches for characterizing and classifying neurons , 2009, Current Opinion in Neurobiology.
[88] Henry Markram,et al. A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis. , 2013, Cerebral cortex.
[89] Satoshi Yamada,et al. Neuron classification based on temporal firing patterns by the dynamical analysis with changing time resolution (DCT) method , 2003, Biological Cybernetics.
[90] Giorgio A. Ascoli,et al. Statistical analysis and data mining of digital reconstructions of dendritic morphologies , 2014, Front. Neuroanat..
[91] Andrey A. Shabalin,et al. Matrix eQTL: ultra fast eQTL analysis via large matrix operations , 2011, Bioinform..
[92] Jonathan Stone,et al. Parallel Processing in the Visual System: The Classification of Retinal Ganglion Cells and its Impact on the Neurobiology of Vision , 1983 .
[93] Yuchio Yanagawa,et al. Morpho-physiological Criteria Divide Dentatecc Gyrus Interneurons into Classes , 2013, Hippocampus.
[94] Allan R. Jones,et al. An anatomic gene expression atlas of the adult mouse brain , 2009, Nature Neuroscience.
[95] Sacha B. Nelson,et al. A Quantitative Comparison of Cell-Type-Specific Microarray Gene Expression Profiling Methods in the Mouse Brain , 2011, PloS one.
[96] H. Seung,et al. Neuronal Cell Types and Connectivity: Lessons from the Retina , 2014, Neuron.
[97] Concha Bielza,et al. Machine Learning in Bioinformatics , 2008, Encyclopedia of Database Systems.
[98] Pedro Larrañaga,et al. A review of feature selection techniques in bioinformatics , 2007, Bioinform..
[99] R. Yuste,et al. Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study , 2010, Developmental neurobiology.
[100] Badrinath Roysam,et al. The FARSIGHT Trace Editor: An Open Source Tool for 3-D Inspection and Efficient Pattern Analysis Aided Editing of Automated Neuronal Reconstructions , 2011, Neuroinformatics.
[101] Harold W. Gutch,et al. Beyond the frontiers of neuronal types , 2012, Front. Neural Circuits.
[102] R. Shapley,et al. Cat and monkey retinal ganglion cells and their visual functional roles , 1986, Trends in Neurosciences.
[103] D. V. van Essen,et al. Challenges and Opportunities in Mining Neuroscience Data , 2011, Science.
[104] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[105] Jeremy MG Taylor,et al. Validation of Biomarker-Based Risk Prediction Models , 2008, Clinical Cancer Research.
[106] Giorgio A. Ascoli,et al. The Coming of Age of the Hippocampome , 2010, Neuroinformatics.
[107] Giorgio A. Ascoli,et al. Digital Morphometry of Rat Cerebellar Climbing Fibers Reveals Distinct Branch and Bouton Types , 2012, The Journal of Neuroscience.
[108] Y. Xing,et al. A Transcriptome Database for Astrocytes, Neurons, and Oligodendrocytes: A New Resource for Understanding Brain Development and Function , 2008, The Journal of Neuroscience.
[109] Christophe Bernard,et al. Interneurons targeting similar layers receive synaptic inputs with similar kinetics , 2006, Hippocampus.
[110] D. Sharpe. Why the resistance to statistical innovations? Bridging the communication gap. , 2013, Psychological methods.
[111] A. van Oudenaarden,et al. MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals. , 2007, Molecular cell.
[112] Karsten M. Borgwardt,et al. ccSVM: correcting Support Vector Machines for confounding factors in biological data classification , 2011, Bioinform..
[113] Y. B. Wah,et al. Power comparisons of Shapiro-Wilk , Kolmogorov-Smirnov , Lilliefors and Anderson-Darling tests , 2011 .
[114] Larry W. Swanson,et al. BAMS Neuroanatomical Ontology: Design and Implementation , 2008, Frontiers Neuroinformatics.