Comparison between supervised and unsupervised classifications of neuronal cell types: A case study

Developmental Neurobiology

Volumn 71, Issue 1, 2011, Pages 71-82

  Guerra, Luis ^a   McGarry, Laura M ^b   Robles, Víctor ^a   Bielza, Concha ^a   Larrañaga, Pedro ^a   Yuste, Rafael ^b  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^b Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

Classification; Clustering; Interneuron; Pyramidal cell; Supervised

Indexed keywords

ANIMAL CELL; ANIMAL EXPERIMENT; ANIMAL TISSUE; ARTICLE; CLASSIFICATION ALGORITHM; CLINICAL CLASSIFICATION; CLINICAL EVALUATION; CLINICAL SUPERVISION; CLUSTER ANALYSIS; CONTROLLED STUDY; DENDRITE; INTERNEURON; MORPHOLOGY; MOUSE; NEOCORTEX; NERVE CELL; NERVE FIBER; NONHUMAN; PRIORITY JOURNAL; PYRAMIDAL NERVE CELL; SOMATIC CELL;

ALGORITHMS; ANIMALS; CELL SHAPE; CEREBRAL CORTEX; IMAGE CYTOMETRY; INTERNEURONS; MICE; MICE, INBRED C57BL; ORGAN CULTURE TECHNIQUES; PYRAMIDAL CELLS;

EID: 78650043633     PISSN: 19328451     EISSN: 10974695     Source Type: Journal    
DOI: 10.1002/dneu.20809     Document Type: Article

References (32)

1. Ascoli GA, Alonso-Nanclares L, Anderson SA, Barrionuevo G, Benavides-Piccione R, Burkhalter A, Buzsáki G, et al. 2008. Petilla terminology: Nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat Rev Neurosci 9: 557-568.
2. Benavides-Piccione R, Sichani FH, Yaez IB, DeFelipe J, Yuste R. 2005. Dendritic size of pyramidal neurons differs among mouse cortical regions. Cerebral Cortex 16: 990-1001.
3. Cauli B, Porter JT, Tsuzuki K, Lambolez B, Rossier J, Quenet B, Audinat E. 2000. Classification of fusiform neocortical interneurons based on unsupervised clustering. PNAS 97: 6144-6149.
4. Conover WJ. 1971. Practical Nonparametric Statistics. Wiley Series in Probability and Statistics, New York, USA.
5. Cover TM, Hart PE. 1967. Nearest neighbor pattern classification. IEEE Trans Inform Theory 13: 21-27.
6. de Nó L. 1922. La corteza cerebral del ratón. Trabajos Laboratorio Investigaciones Biológicas 20: 41-78.
7. Dumitriu D, Cossart R, Huang J, Yuste R. 2007. Correlation between axonal morphologies and synaptic input kineptics of interneurons from mouse visual cortex. Cerebral Cortex 17: 81-91.
8. Goldberg DE. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Boston, MA, USA: Addison-Wesley Longman.
9. Helmstaedter M, Sakmann B, Feldmeyer D. 2008a. L2/3 interneuron groups defined by multiparameter analysis of axonal projection, dendritic geometry and electrical excitability. Cerebral Cortex 19: 951-962.
10. Helmstaedter M, Sakmann B, Feldmeyer D. 2008b. Neuronal correlates of local, lateral, and translaminar inhibition with reference to cortical columns. Cerebral Cortex 19: 926-937.
11. Helmstaedter M, Sakmann B, Feldmeyer D. 2008c. The relation between dendritic geometry, electrical excitability, and axonal projections of L2/3 interneurons in rat barrel cortex. Cerebral Cortex 19: 938-950.
12. Hosmer DW, Lemeshow S. 2000. Applied Logistic Regression, 2nd ed. Wiley Online Library Publication, New York, USA.
13. Ihaka R, Gentleman R. 1996. R: A language for data analysis and graphics. J Computat Graphical Stat 5: 299-314.
14. Jardine N, Sibson R. 1968. The construction of hierarchic and non-hierarchic classifications. Comput J 11: 177-184.
15. Jolliffe I. 1986. Principal Component Analysis. New York, USA: Springer-Verlag.
16. Karagiannis A, Gallopin T, Csaba D, Battaglia D, Geoffroy H, Rossier J, Hillman E, et al. 2009. Classification of NPY-expressing neocortical interneurons. J Neurosci 29: 3642-3659.
17. Kittler J. 1978. Feature set search algorithms. Pattern Recogn Signal Process. In: Chen CH, editor, pp 41-60.
18. Kohavi R, John GH. 1997. Wrappers for feature subset selection. Artif Intelligence 97(1-2): 273-324.
19. Kozloski J, Hamzei-Sichani F, Yuste R. 2001. Stereotyped position of local synaptic targets in neocortex. Science 293: 868-872.
20. Liu H, Motoda H. 1998. Feature Selection for Knowledge Discovery and Data Mining. Norwell, MA, USA: Kluwer Academic Publishers.
21. Marin EC, Jefferys G, Komiyama T, Zhu H, Luo L. 2002. Representation of the glomerular olfactory map in the drosophila brain. Cell 149: 243-255.
22. McGarry LM, Packer A, Fino E, Nikolenko V, Sippy T, Yuste R. 2010. Quantitative classification of somatostatin-positive neocortical interneurons identifies three interneuron subtypes. Front Neural Circuits 4: 12.
23. Minsky M. 1961. Steps toward artificial intelligence. Comput Thought 49: 406-450.
24. Peters A. 1987. Number of neurons and synapses in primary visual cortex. Cerebral Cortex 6: 267-294.
25. Quinlan JR. 1993. C4.5: Programs for Machine Learning. San Mateo, CA, USA: Morgan Kaufmann Publishers.
26. Ramón y Cajal S. 1899. Textura del Sistema Nervioso del Hombre y de los Vertebrados. Moya.
27. Rumerlhart D, Hinton G, Williams R. 1986. Learning internal representations by backpropagation errors. Nature 323: 533-536.
28. Stone M. 1974. Cross-validatory choice and assessment of statistical predictions. J Roy Stat Soc Series B 36: 111-147.
29. Tsiola A, Hamzei-Sichani F, Peterlin Z, Yuste R. 2003. Quantitative morphological classification of layer 5 neurons from mouse primary visual cortex. J Compar Neurol 461: 415-428.
30. Wilcoxon F. 1945. Individual comparisons by ranking methods. Biometrics 1: 80-83.
31. Witten IH, Frank E. 2005. Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. San Francisco, USA: Morgan Kaufmann.
32. Wong AM, Wang JW, Axel R. 2002. Spatial representation of the glomerular map in the drosophila protocerebrum. Cell 109: 229-241.