title: Non-parametric algorithmic generation of neuronal morphologies.
abstract: Generation algorithms allow for the generation of Virtual Neurons (VNs) from a small set of morphological properties. The set describes the morphological properties of real neurons in terms of statistical descriptors such as the number of branches and segment lengths (among others). The majority of reconstruction algorithms use the observed properties to estimate the parameters of a priori fixed probability distributions in order to construct statistical descriptors that fit well with the observed data. In this article, we present a non-parametric generation algorithm based on kernel density estimators (KDEs). The new algorithm is called KDE-NEURON: and has three advantages over parametric reconstruction algorithms: (1) no a priori specifications about the distributions underlying the real data, (2) peculiarities in the biological data will be reflected in the VNs, and (3) ability to reconstruct different cell types. We experimentally generated motor neurons and granule cells, and statistically validated the obtained results. Moreover, we assessed the quality of the prototype data set and observed that our generated neurons are as good as the prototype data in terms of the used statistical descriptors. The opportunities and limitations of data-driven algorithmic reconstruction of neurons are discussed.
authors.affiliation: TENU, Okinawa Institute of Science and Technology, Okinawa, Japan. nielsen@oist.jp
authors.forename: Benjamin
authors.initials: B
authors.lastname: Torben-Nielsen
authors.forename: Stijn
authors.initials: S
authors.lastname: Vanderlooy
authors.forename: Eric O
authors.initials: EO
authors.lastname: Postma
mesh_terms: D000465:Algorithms; D000818:Animals; D016764:Cell Polarity / Q000502:physiology; D048430:Cell Shape / Q000502:physiology*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003712:Dendrites / Q000502:physiology / Q000648:ultrastructure; D006624:Hippocampus / Q000166:cytology / Q000502:physiology; D007395:Interneurons / Q000166:cytology / Q000502:physiology; D015233:Models, Statistical; D009046:Motor Neurons / Q000166:cytology / Q000502:physiology; D009445:Neuroanatomy / Q000379:methods*; D009474:Neurons / Q000166:cytology* / Q000502:physiology; D051381:Rats; D015203:Reproducibility of Results; D012984:Software; D013116:Spinal Cord / Q000166:cytology / Q000502:physiology
references.citation: Nat Neurosci. 2001 Apr;4(4):359-65
references.pmid: 11276225
references.citation: J Neurosci. 1992 Jun;12(6):2403-16
references.pmid: 1607948
references.citation: Nat Neurosci. 2002 Nov;5(11):1177-84
references.pmid: 12368805
references.citation: J Neurophysiol. 1994 Jan;71(1):375-400
references.pmid: 7512629
references.citation: Network. 2002 Aug;13(3):357-80
references.pmid: 12222819
references.citation: Math Biosci. 2004 Mar-Apr;188:147-55
references.pmid: 14766099
references.citation: J Comp Neurol. 1991 Dec 22;314(4):763-76
references.pmid: 1816274
references.citation: Anat Embryol (Berl). 2001 Oct;204(4):283-301
references.pmid: 11720234
references.citation: Math Biosci. 2007 Feb;205(2):271-96
references.pmid: 17011000
references.citation: Philos Trans R Soc Lond B Biol Sci. 2001 Aug 29;356(1412):1187-208
references.pmid: 11545698
references.citation: Brain Res Dev Brain Res. 1990 Jun 1;54(1):115-24
references.pmid: 2364540
references.citation: Comput Med Imaging Graph. 1990 Sep-Oct;14(5):307-17
references.pmid: 2224829
references.citation: Methods. 2001 Aug;24(4):309-21
references.pmid: 11465996
references.citation: J Comp Neurol. 2004 May 24;473(2):177-93
references.pmid: 15101088
references.citation: Anat Rec. 1999 Dec 15;257(6):195-207
references.pmid: 10620749
references.citation: Hippocampus. 2005;15(2):166-83
references.pmid: 15390156
references.citation: Neuroinformatics. 2007 Fall;5(3):154-60
references.pmid: 17917126
references.citation: Hippocampus. 2005;15(3):302-15
references.pmid: 15490464
references.citation: Brain Res. 1992 Mar 27;576(1):152-6
references.pmid: 1381258
references.citation: Eur J Neurosci. 2004 Mar;19(5):1257-66
references.pmid: 15016083
references.citation: J Comput Neurosci. 2005 Oct;19(2):223-38
references.pmid: 16133820
references.citation: J Neurosci Methods. 2006 Oct 15;157(1):132-41
references.pmid: 16690135
references.citation: Nat Rev Neurosci. 2006 Feb;7(2):153-60
references.pmid: 16429124
references.citation: Nat Rev Neurosci. 2006 Apr;7(4):318-24
references.pmid: 16552417
references.citation: J Neurosci Res. 2003 Jan 15;71(2):173-87
references.pmid: 12503080
references.citation: J Neurosci Methods. 1998 Oct 1;84(1-2):49-54
references.pmid: 9821633