Analyzing Self-Similar and Fractal Properties of the C. elegans Neural Network
| Autor: | Daniel J. Kelleher, Tyler M. Reese, Dylan T. Yott, Antoni Brzoska |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Connectomics
Neural Networks Self-similarity lcsh:Medicine Geometry Bioinformatics Giant component 03 medical and health sciences Model Organisms Electrical Synapses 0302 clinical medicine Molecular Cell Biology Neural Pathways Connectome Animals lcsh:Science Biology 030304 developmental biology Clustering coefficient Neurons Physics Stochastic Processes 0303 health sciences Multidisciplinary Quantitative Biology::Neurons and Cognition Artificial neural network lcsh:R Animal Models Probability Theory Average path length Fractals Caenorhabditis Elegans Synapses lcsh:Q Neural Networks Computer Cellular Types Nerve Net Laplacian matrix Biological system Mathematics Algorithms 030217 neurology & neurosurgery Research Article Neuroscience |
| Zdroj: | PLoS ONE PLoS ONE, Vol 7, Iss 10, p e40483 (2012) |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0040483 |
| Popis: | The brain is one of the most studied and highly complex systems in the biological world. While much research has concentrated on studying the brain directly, our focus is the structure of the brain itself: at its core an interconnected network of nodes (neurons). A better understanding of the structural connectivity of the brain should elucidate some of its functional properties. In this paper we analyze the connectome of the nematode Caenorhabditis elegans. Consisting of only 302 neurons, it is one of the better-understood neural networks. Using a Laplacian Matrix of the 279-neuron "giant component" of the network, we use an eigenvalue counting function to look for fractal-like self similarity. This matrix representation is also used to plot visualizations of the neural network in eigenfunction coordinates. Small-world properties of the system are examined, including average path length and clustering coefficient. We test for localization of eigenfunctions, using graph energy and spacial variance on these functions. To better understand results, all calculations are also performed on random networks, branching trees, and known fractals, as well as fractals which have been "rewired" to have small-world properties. We propose algorithms for generating Laplacian matrices of each of these graphs. |
| Databáze: | OpenAIRE |
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