Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
ElMessiery, Medhat A.
- Contribution of Branching Order of Dendrites to Morphology of Neural Cells
Abstract Views :231 |
PDF Views:71
Authors
Affiliations
1 Department of Engineering Mathematics and Physics, Faculty of Engineering, Cairo University, Giza, 12613, EG
1 Department of Engineering Mathematics and Physics, Faculty of Engineering, Cairo University, Giza, 12613, EG
Source
Current Science, Vol 116, No 3 (2019), Pagination: 457-462Abstract
It is currently believed that the morphology of dendrites depends on two main factors, namely the material of the cytoplasm and the path length of the signal before reaching the soma. In the present work the branching order is introduced as a third factor; it represents the variation of electrical resistivity of different dendritic segments. A mathematical modification of the optimum cost function is applied. The software package TREES toolbox is employed to reconstruct several types of cells, specifically LPTC, HSS, HSN and starburst amacrine. The effectiveness of the present analysis is assessed by both the electrotonic and Sholl footprints. The results indicate a higher degree of matching between the synthesized reconstructions of these cells and their original morphology. Moreover, a reduction in the storage memory of their in silico morphologies is achieved.Keywords
Branching Order, Dendrites Morphology, Electrotonic Footprint, Nerve Cells.Full Text
References
- Van Elburg, R. A. J. and Van Ooyen, A., Impact of dendritic size and dendritic topology on burst firing in pyramidal cells. PLoS Comput. Biol., 2010, 6(5), e1000781.
- Mainen, Z. F. and Sejnowski, T. J., Influence of dendritic structure on firing pattern in model neocortical neurons. Nature, 1996, 382(6589), 363-366.
- Silver, R. A., Neuronal arithmetic. Nature Rev. Neurosci., 2010, 11(7), 474-489.
- Torben-Nielsen, B. and Stiefel, K. M., An inverse approach for elucidating dendritic function. Front. Comput. Neurosci., 2010, 4(128), 1-11.
- Jia, H. et al., Dendritic organization of sensory input to cortical neurons in vivo. Neuroforum, 2010, 16(3), 236-237.
- Ascoli, G. A., Donohue, D. E. and Halavi, M., NeuroMorpho.Org: a central resource for neuronal morphologies. J. Neurosci., 2007, 27(35), 9247-9251.
- Ascoli, G. A., Progress and perspectives in computational neuroanatomy. Anat. Rec., 1999, 257(6), 195-207.
- Ascoli, G. A., Krichmar, J. L., Scorcioni, R., Nasuto, S. J., Senft, S. L. and Krichmar, G. L., Computer generation and quantitative morphometric analysis of virtual neurons. Anat. Embryol., 2001, 204(4), 283-301.
- Luczak, A., Spatial embedding of neuronal trees modeled by diffusive growth. J. Neurosci. Methods, 2006, 157(1), 132-141.
- Cuntz, H., Forstner, F., Borst, H. and Häusser, M., One rule to grow them all: a general theory of neuronal branching and its practical application. PLoS Comput. Biol., 2010, 6(8), e1000877.
- Cajal, S. R., The structure and connexions of neurons. Nobel Prize in Physiology or Medicine Lecture, Nobel Foundation, 1906, pp. 220-253.
- Van Pelt, J., A simple vector implementation of the Laplacetransformed cable equations in passive dendritic trees. Biol. Cybern., 1992, 68(1), 15-21.
- Rall, W. and Rinzel, J., Branch input resistance and steady attenuation for input to one branch of a dendritic neuron model. Biophys. J., 1973, 13(7), 648-688.
- Elias, M., A biophysics approach to generate synthetic morphologies for real dendritic reconstructions of neuronal cells, M Sc thesis, Cairo University, Egypt, 2015.
- Sholl, D. A., Dendritic organization in the neurons of the visual and motor cortices of the cat. J. Anat., 1953, 87(Pt 4), 387-406.
- Cuntz, H., Förstner, F., Borst, A. and Häusser, M., Trees toolbox. 2010, 90.
- Cuntz, H., Modelling dendrite shape from wiring principles. In The Computing Dendrite: From Structure to Function, Springer, 2014.