A Primal Analysis System of Brain Neurons Data

It is a very challenging work to classify the 86 billions of neurons in the human brain. The most important step is to get the features of these neurons. In this paper, we present a primal system to analyze and extract features from brain neurons. First, we make analysis on the original data of neurons in which one neuron contains six parameters: room type, X, Y, Z coordinate range, total number of leaf nodes, and fuzzy volume of neurons. Then, we extract three important geometry features including rooms type, number of leaf nodes, and fuzzy volume. As application, we employ the feature database to fit the basic procedure of neuron growth. The result shows that the proposed system is effective.

[1]  Wang Ting-hua.  The Morphological Evidence on the Various Subpopulations of Neurons in L_6 DRG of Adult Cat by Criterion of Size , 2000 .

[2]  Rene F. Swarttouw,et al.  Orthogonal polynomials , 2020, NIST Handbook of Mathematical Functions.

[3]  Charles C. Kim,et al.  The microRNA bantam Functions in Epithelial Cells to Regulate Scaling Growth of Dendrite Arbors in Drosophila Sensory Neurons , 2009, Neuron.

[4]  L. Billeci,et al.  Automated extraction and classification of dynamic metrical features of morphological development in dissociated Purkinje neurons , 2010, Journal of Neuroscience Methods.

[5]  Conrad D James,et al.  Combined chemical and topographical guidance cues for directing cytoarchitectural polarization in primary neurons. , 2011, Biomaterials.

[6]  Walter Van Assche.  Orthogonal polynomials, special functions, and applications , 2011, J. Approx. Theory.

[7]  J. Burke,et al.  On the Subdifferential Regularity of Max Root Functions for Polynomials , 2012 .

[8]  Y. Jan,et al.  Integrins Regulate Repulsion-Mediated Dendritic Patterning of Drosophila Sensory Neurons by Restricting Dendrites in a 2D Space , 2012, Neuron.

[9]  Jorge Sastre.  Efficient mixed rational and polynomial approximation of matrix functions , 2012, Appl. Math. Comput..

[10]  Michael A. Cohen,et al.  A polynomial approximation for arbitrary functions , 2012, Appl. Math. Lett..

[11]  B. Ye,et al.  Dscam Expression Levels Determine Presynaptic Arbor Sizes in Drosophila Sensory Neurons , 2013, Neuron.

[12]  T. Lillicrap,et al.  Preference Distributions of Primary Motor Cortex Neurons Reflect Control Solutions Optimized for Limb Biomechanics , 2013, Neuron.

[13]  Yukako Hattori,et al.  Sensory-neuron subtype-specific transcriptional programs controlling dendrite morphogenesis: genome-wide analysis of Abrupt and Knot/Collier. , 2013, Developmental cell.

[14]  Yukako Hattori.  Subtype-specific postmitotic transcriptional programs controlling dendrite morphogenesis of Drosophila sensory neuron , 2014 .