NDLI: Patterning motor neurons in the Drosophila ventral nerve cord using latent state Conditional Random Fields

Content Provider	IEEE Xplore Digital Library
Author	Chang, X. Kim, M.D. Chiba, A. Tsechpenakis, G.
Copyright Year	2012
Description	Author affiliation: Computer and Information Science Department, Indiana University-Purdue University Indianapolis, USA (Chang, X.; Tsechpenakis, G.) \|\| Molecular and Cellular Pharmacology Department, University of Miami Miller School of Medicine, USA (Kim, M.D.) \|\| Biology Department, University of Miami, USA (Chiba, A.)
Abstract	Type-specific dendritic arborization patterns dictate synaptic connectivity and are fundamental determinants of neuronal function. We exploit the morphological stereotypy and relative simplicity of the Drosophila nervous system to model the diverse neuronal morphologies of individual motor neurons (MNs) and understand underlying principles of synaptic connectivity in a motor circuit. In our analysis, we use images depicting single neurons labeled with green fluorescent protein (GFP) and serially imaged with laser scanning confocal microscopy. We model morphology with a novel formulation of Conditional Random Fields, a latent state CRF, to capture the highly varying compartment-based structure of the neurons (soma-axon-dendrites). We integrate a multi-class logistic model as the local potential function for combining compartment features. All parameters are learned in a single procedure, while L1-norm logistic model parameters are added in the maximum pseudo-likelihood model for learning with better scalability. The regularization hyper-parameters are chosen with a minimum cross-validation generalization error model.
Starting Page	864
Ending Page	867
File Size	200884
Page Count	4
File Format	PDF
ISBN	9781457718571
ISSN	19457928
e-ISBN	9781457718588
DOI	10.1109/ISBI.2012.6235685
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-02
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manganese Morphology Nerve fibers Topology Logistics Training latent state Conditional Random Fields neuron morphology Drosophila
Content Type	Text
Resource Type	Article

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