1,349 research outputs found
Analyzing Self-similar and Fractal Properties of the C. Elegans Neural Network
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
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
Advances in Neural Signal Processing
Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications
Advances in Neural Signal Processing
Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications
The Network Analysis of Urban Streets: A Primal Approach
The network metaphor in the analysis of urban and territorial cases has a
long tradition especially in transportation/land-use planning and economic
geography. More recently, urban design has brought its contribution by means of
the "space syntax" methodology. All these approaches, though under different
terms like accessibility, proximity, integration,connectivity, cost or effort,
focus on the idea that some places (or streets) are more important than others
because they are more central. The study of centrality in complex
systems,however, originated in other scientific areas, namely in structural
sociology, well before its use in urban studies; moreover, as a structural
property of the system, centrality has never been extensively investigated
metrically in geographic networks as it has been topologically in a wide range
of other relational networks like social, biological or technological. After
two previous works on some structural properties of the dual and primal graph
representations of urban street networks (Porta et al. cond-mat/0411241;
Crucitti et al. physics/0504163), in this paper we provide an in-depth
investigation of centrality in the primal approach as compared to the dual one,
with a special focus on potentials for urban design.Comment: 19 page, 4 figures. Paper related to the paper "The Network Analysis
of Urban Streets: A Dual Approach" cond-mat/041124
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