817 research outputs found
Brain architecture: A design for natural computation
Fifty years ago, John von Neumann compared the architecture of the brain with
that of computers that he invented and which is still in use today. In those
days, the organisation of computers was based on concepts of brain
organisation. Here, we give an update on current results on the global
organisation of neural systems. For neural systems, we outline how the spatial
and topological architecture of neuronal and cortical networks facilitates
robustness against failures, fast processing, and balanced network activation.
Finally, we discuss mechanisms of self-organization for such architectures.
After all, the organization of the brain might again inspire computer
architecture
Evolution and development of Brain Networks: From Caenorhabditis elegans to Homo sapiens
Neural networks show a progressive increase in complexity during the time
course of evolution. From diffuse nerve nets in Cnidaria to modular,
hierarchical systems in macaque and humans, there is a gradual shift from
simple processes involving a limited amount of tasks and modalities to complex
functional and behavioral processing integrating different kinds of information
from highly specialized tissue. However, studies in a range of species suggest
that fundamental similarities, in spatial and topological features as well as
in developmental mechanisms for network formation, are retained across
evolution. 'Small-world' topology and highly connected regions (hubs) are
prevalent across the evolutionary scale, ensuring efficient processing and
resilience to internal (e.g. lesions) and external (e.g. environment) changes.
Furthermore, in most species, even the establishment of hubs, long-range
connections linking distant components, and a modular organization, relies on
similar mechanisms. In conclusion, evolutionary divergence leads to greater
complexity while following essential developmental constraints
From Caenorhabditis elegans to the Human Connectome: A Specific Modular Organisation Increases Metabolic, Functional, and Developmental Efficiency
The connectome, or the entire connectivity of a neural system represented by
network, ranges various scales from synaptic connections between individual
neurons to fibre tract connections between brain regions. Although the
modularity they commonly show has been extensively studied, it is unclear
whether connection specificity of such networks can already be fully explained
by the modularity alone. To answer this question, we study two networks, the
neuronal network of C. elegans and the fibre tract network of human brains
yielded through diffusion spectrum imaging (DSI). We compare them to their
respective benchmark networks with varying modularities, which are generated by
link swapping to have desired modularity values but otherwise maximally random.
We find several network properties that are specific to the neural networks and
cannot be fully explained by the modularity alone. First, the clustering
coefficient and the characteristic path length of C. elegans and human
connectomes are both higher than those of the benchmark networks with similar
modularity. High clustering coefficient indicates efficient local information
distribution and high characteristic path length suggests reduced global
integration. Second, the total wiring length is smaller than for the
alternative configurations with similar modularity. This is due to lower
dispersion of connections, which means each neuron in C. elegans connectome or
each region of interest (ROI) in human connectome reaches fewer ganglia or
cortical areas, respectively. Third, both neural networks show lower
algorithmic entropy compared to the alternative arrangements. This implies that
fewer rules are needed to encode for the organisation of neural systems
Edge vulnerability in neural and metabolic networks
Biological networks, such as cellular metabolic pathways or networks of
corticocortical connections in the brain, are intricately organized, yet
remarkably robust toward structural damage. Whereas many studies have
investigated specific aspects of robustness, such as molecular mechanisms of
repair, this article focuses more generally on how local structural features in
networks may give rise to their global stability. In many networks the failure
of single connections may be more likely than the extinction of entire nodes,
yet no analysis of edge importance (edge vulnerability) has been provided so
far for biological networks. We tested several measures for identifying
vulnerable edges and compared their prediction performance in biological and
artificial networks. Among the tested measures, edge frequency in all shortest
paths of a network yielded a particularly high correlation with vulnerability,
and identified inter-cluster connections in biological but not in random and
scale-free benchmark networks. We discuss different local and global network
patterns and the edge vulnerability resulting from them.Comment: 8 pages, 4 figures, to appear in Biological Cybernetic
Metrics for Measuring Data Quality - Foundations for an Economic Oriented Management of Data Quality
The article develops metrics for an economic oriented management of data quality. Two data quality dimensions are focussed: consistency and timeliness. For deriving adequate metrics several requirements are stated (e. g. normalisation, cardinality, adaptivity, interpretability). Then the authors discuss existing approaches for measuring data quality and illustrate their weaknesses. Based upon these considerations, new metrics are developed for the data quality dimensions consistency and timeliness. These metrics are applied in practice and the results are illustrated in the case of a major German mobile services provider
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