Data-oriented neuron classification from their parts

The shape of a neuron can reveal many interesting properties about its function. Therefore, organizing neuronal cells into appropriate classes according to their respective shape is a fundamental endeavor in neuroscience. Available online datasets allow new data-oriented approaches to solve such neuroscience problems. Here we analyze the feasibility of classifying neurons according not to their respective wholes, but to its constituent parts. Such a study may reveal interesting insights, including whether parts of the neuronal dendritic arborization preserve proper information about the morphology of the whole neuron. Experimental results using open datasets are reported, thus corroborating our approach.

[1]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[2]  S. Schultz.  Principles of Neural Science, 4th ed. , 2001 .

[3]  P. Somogyi,et al.  Synchronization of neuronal activity in hippocampus by individual GABAergic interneurons , 1995, Nature.

[4]  H. Seung,et al.  Neuronal Cell Types and Connectivity: Lessons from the Retina , 2014, Neuron.

[5]  C. Stevens,et al.  A General Principle of Neural Arbor Branch Density , 2011, Current Biology.

[6]  S. Sherman,et al.  Structure/function relationships of retinal ganglion cells in the cat , 1984, Brain Research.

[7]  Henry Markram,et al.  The human brain project. , 2012, Scientific American.

[8]  Giorgio A. Ascoli,et al.  The importance of metadata to assess information content in digital reconstructions of neuronal morphology , 2014, Cell and Tissue Research.

[9]  R. Yuste,et al.  Neural Circuits Original Research Article Materials and Methods Preparation of Brain Slices , 2022 .

[10]  Giorgio A. Ascoli,et al.  Towards the automatic classification of neurons , 2015, Trends in Neurosciences.

[11]  Ting Zhao,et al.  Automatic Neuron Type Identification by Neurite Localization in the Drosophila Medulla , 2014, ArXiv.

[12]  G. Ascoli,et al.  Neuronal Morphology Goes Digital: A Research Hub for Cellular and System Neuroscience , 2013, Neuron.

[13]  Y. Uji,et al.  Morphological classification of retinal ganglion cells in mice , 1995, The Journal of comparative neurology.

[14]  N. Spruston.  Pyramidal neurons: dendritic structure and synaptic integration , 2008, Nature Reviews Neuroscience.

[15]  S. Schultz,et al.  Localising and classifying neurons from high density MEA recordings , 2014, Journal of Neuroscience Methods.

[16]  Ramón y Cajal,et al.  Histologie du système nerveux de l'homme & des vertébrés , 1909 .

[17]  Y. Jan,et al.  Branching out: mechanisms of dendritic arborization , 2010, Nature Reviews Neuroscience.

[18]  Giorgio A. Ascoli,et al.  Digital Reconstructions of Neuronal Morphology: Three Decades of Research Trends , 2012, Front. Neurosci..

[19]  R. Shapley,et al.  The primate retina contains two types of ganglion cells, with high and low contrast sensitivity. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[21]  Yuchio Yanagawa,et al.  Morpho-physiological Criteria Divide Dentatecc Gyrus Interneurons into Classes , 2013, Hippocampus.

[22]  Cesar H. Comin,et al.  Archetypes and Outliers in the Neuromorphological Space , 2014 .

[23]  Giorgio A. Ascoli,et al.  Successes and Rewards in Sharing Digital Reconstructions of Neuronal Morphology , 2007, Neuroinformatics.

[24]  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.

[25]  Vivien A. Casagrande,et al.  Biophysics of Computation: Information Processing in Single Neurons , 1999 .

[26]  G. Ascoli,et al.  NeuroMorpho.Org: A Central Resource for Neuronal Morphologies , 2007, The Journal of Neuroscience.

[27]  Lydia Ng,et al.  Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system , 2012, Nucleic Acids Res..

[28]  W. Rall.  Electrophysiology of a dendritic neuron model. , 1962, Biophysical journal.

[29]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[30]  Shinichi Morishita,et al.  SCMD: Saccharomyces cerevisiae Morphological Database , 2004, Nucleic Acids Res..

[31]  Bartlett W. Mel,et al.  Impact of Active Dendrites and Structural Plasticity on the Memory Capacity of Neural Tissue , 2001, Neuron.

[32]  W. Rall.  Core Conductor Theory and Cable Properties of Neurons , 2011 .

[33]  Ronald R. Coifman,et al.  Quantitative Arbor Analytics: Unsupervised Harmonic Co-Clustering of Populations of Brain Cell Arbors Based on L-Measure , 2014, Neuroinformatics.

[34]  Giorgio A. Ascoli,et al.  Topological characterization of neuronal arbor morphology via sequence representation: I - motif analysis , 2015, BMC Bioinformatics.

[35]  G. Ascoli.  Mobilizing the base of neuroscience data: the case of neuronal morphologies , 2006, Nature Reviews Neuroscience.

[36]  Tatyana O. Sharpee,et al.  Toward Functional Classification of Neuronal Types , 2014, Neuron.

[37]  Florence Besse,et al.  Axonal tree classification using an Elastic Shape Analysis based distance , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[38]  Larry W. Swanson,et al.  The neuron classification problem , 2007, Brain Research Reviews.

[39]  Y. Kawaguchi,et al.  Groupings of nonpyramidal and pyramidal cells with specific physiological and morphological characteristics in rat frontal cortex. , 1993, Journal of neurophysiology.

[40]  Liqun Luo,et al.  How do dendrites take their shape? , 2001, Nature Neuroscience.

[41]  R. Yuste,et al.  Classification of neocortical interneurons using affinity propagation , 2013, Front. Neural Circuits.

[42]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[43]  Giorgio A. Ascoli,et al.  Doubling up on the Fly: NeuroMorpho.Org Meets Big Data , 2014, Neuroinformatics.

[44]  Concha Bielza,et al.  Bayesian Network Classifiers for Categorizing Cortical GABAergic Interneurons , 2015, Neuroinformatics.

[45]  Robert E Burke,et al.  Simulation of motoneuron morphology in three dimensions. I. Building individual dendritic trees , 2007, The Journal of comparative neurology.

[46]  R. Shapley,et al.  Cat and monkey retinal ganglion cells and their visual functional roles , 1986, Trends in Neurosciences.

[47]  Perry L. Miller,et al.  Database tools for integrating and searching membrane property data correlated with neuronal morphology , 1998, Journal of Neuroscience Methods.

[48]  Sen Song,et al.  A genetic and computational approach to structurally classify neuronal types , 2014, Nature Communications.

[49]  Luciano da Fontoura Costa,et al.  Shape, connectedness and dynamics in neuronal networks , 2013, Journal of Neuroscience Methods.

[50]  Javier DeFelipe,et al.  Cortical interneurons: from Cajal to 2001. , 2002, Progress in brain research.

[51]  Evelyne Sernagor,et al.  Development of Retinal Ganglion Cell Structure and Function , 2001, Progress in Retinal and Eye Research.