Morphological characterization of in-vitro neuronal network

School of Physics and Astronomy, Raymond & Beverly Sackler Faculty of Exact Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel.
Physical Review E (Impact Factor: 2.29). 09/2002; 66(2 Pt 1):021905. DOI: 10.1103/PhysRevE.66.021905
Source: PubMed


We use in vitro neuronal networks as a model system for studying self-organization processes in the nervous system. We follow the neuronal growth process, from isolated neurons to fully connected two-dimensional networks. The mature networks are mapped into connected graphs and their morphological characteristics are measured. The distributions of segment lengths, node connectivity, and path length between nodes, and the clustering coefficient of the networks are used to characterize network morphology and to demonstrate that our networks fall into the category of small-world networks.

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Available from: Orit Shefi, Oct 04, 2014
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    • "In the case of the brain, different kinds of distribution have been reported depending on the spatial scale at which the system is analyzed, since the scale determines the number of nodes N and links L of the network which, in turn, constrains the width of the degree distribution. In cultured neural networks, the fact that neurons primarily connect through a random process leads to exponential distributions (Shefi et al. 2002). This kind of distribution is also reported in the in-degree and the out-degree of the anatomical connections of C. elegans nematode, the only living system with a whole reconstruction of its neural network (Amaral et al. 2000). "
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    ABSTRACT: SynonymsComplex network theory in neuroscience; Graph theory in neuroscienceDefinitionNetwork theory is a branch of mathematics concerned with the analysis of the structure of graphs, the mathematical abstraction of networks. Since the beginning of the twenty-first century, it has become an applied discipline due to the availability of large datasets for social, technological, and biological systems. Although network theory was initially restricted to topological analysis, it has soon become a tool for understanding the emergence, functioning, and evolution of networks and the dynamical processes occurring on them. The application of network theory to neuroscience and, more specifically, to the analysis of brain structure and function represents a qualitatively different view of brain activity and brain-behavior mapping, shifting from a computerlike to a complex system vision of the brain, where networks are endowed with properties which stem in a nontrivial way from those of their con ...
    Encyclopedia of Computational Neuroscience, 01/2014: pages 1-21; , ISBN: 978-1-4614-7320-6
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    • "Furthermore the clustering coefficient C is very high as in regular networks (C = 1) and contrary to random networks. For the Apollonian network C has been found to be equal to 0.828 in the limit of large N. On this basis, the Apollonian network appears to have all the new features that we would like to investigate: small-world property found experimentally (Shefi et al., 2002) and possibility of a very high connectivity degree (scale-free). Moreover it also presents sites connecting bonds of all lengths. "
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    ABSTRACT: Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behavior: these avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems. We present a recent model inspired in self-organized criticality, which consists of an electrical network with threshold firing, refractory period, and activity-dependent synaptic plasticity. The model reproduces the critical behavior of the distribution of avalanche sizes and durations measured experimentally. Moreover, the power spectra of the electrical signal reproduce very robustly the power law behavior found in human electroencephalogram (EEG) spectra. We implement this model on a variety of complex networks, i.e., regular, small-world, and scale-free and verify the robustness of the critical behavior.
    Frontiers in Physiology 03/2012; 3:62. DOI:10.3389/fphys.2012.00062 · 3.53 Impact Factor
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    • "In the literature of the statistical significance testing on graphs, the hypothesis null can be: average clustering coefficient [Koyuturk et al. 2007]: the fraction of number of neighbors of the node with number of possible links, characteristic path length[Shefi et al. 2002; Lerner et al. 2009]: the mean of all pairs shortest paths between the nodes. However, these hypotheses do not take into account the relationship between two specific nodes. "
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    ABSTRACT: Graph is one of important interactive visualization tools. In machine learning, it can be built from observational data, to represent pictorially the characteristics of complex systems. Normally, the di erence between graphs can be used for predicting the variance of systems. However, with a small data system, it is hard to describe the real di erence. Therefore, ensemble methods proposed to use multiple models to obtain better predictive performance. In fact, they combine multiple hypotheses to form a better hypothesis that will make good predictions with a particular problem. We propose in this work a new ensemble approach for graph data: multiple hypothesis testing on edges of graph. This paper describes how to use this approach to deal with the problem of comparison of two sets of graph-based models. In order to perform the interests of proposed approach, we experimented on two sets of simulated Bayesian networks.
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