14,285 research outputs found
Generative models of the human connectome
The human connectome represents a network map of the brain's wiring diagram
and the pattern into which its connections are organized is thought to play an
important role in cognitive function. The generative rules that shape the
topology of the human connectome remain incompletely understood. Earlier work
in model organisms has suggested that wiring rules based on geometric
relationships (distance) can account for many but likely not all topological
features. Here we systematically explore a family of generative models of the
human connectome that yield synthetic networks designed according to different
wiring rules combining geometric and a broad range of topological factors. We
find that a combination of geometric constraints with a homophilic attachment
mechanism can create synthetic networks that closely match many topological
characteristics of individual human connectomes, including features that were
not included in the optimization of the generative model itself. We use these
models to investigate a lifespan dataset and show that, with age, the model
parameters undergo progressive changes, suggesting a rebalancing of the
generative factors underlying the connectome across the lifespan.Comment: 38 pages, 5 figures + 19 supplemental figures, 1 tabl
Asymmetries arising from the space-filling nature of vascular networks
Cardiovascular networks span the body by branching across many generations of
vessels. The resulting structure delivers blood over long distances to supply
all cells with oxygen via the relatively short-range process of diffusion at
the capillary level. The structural features of the network that accomplish
this density and ubiquity of capillaries are often called space-filling. There
are multiple strategies to fill a space, but some strategies do not lead to
biologically adaptive structures by requiring too much construction material or
space, delivering resources too slowly, or using too much power to move blood
through the system. We empirically measure the structure of real networks (18
humans and 1 mouse) and compare these observations with predictions of model
networks that are space-filling and constrained by a few guiding biological
principles. We devise a numerical method that enables the investigation of
space-filling strategies and determination of which biological principles
influence network structure. Optimization for only a single principle creates
unrealistic networks that represent an extreme limit of the possible structures
that could be observed in nature. We first study these extreme limits for two
competing principles, minimal total material and minimal path lengths. We
combine these two principles and enforce various thresholds for balance in the
network hierarchy, which provides a novel approach that highlights the
trade-offs faced by biological networks and yields predictions that better
match our empirical data.Comment: 17 pages, 15 figure
Neural development features: Spatio-temporal development of the Caenorhabditis elegans neuronal network
The nematode Caenorhabditis elegans, with information on neural connectivity,
three-dimensional position and cell linage provides a unique system for
understanding the development of neural networks. Although C. elegans has been
widely studied in the past, we present the first statistical study from a
developmental perspective, with findings that raise interesting suggestions on
the establishment of long-distance connections and network hubs. Here, we
analyze the neuro-development for temporal and spatial features, using birth
times of neurons and their three-dimensional positions. Comparisons of growth
in C. elegans with random spatial network growth highlight two findings
relevant to neural network development. First, most neurons which are linked by
long-distance connections are born around the same time and early on,
suggesting the possibility of early contact or interaction between connected
neurons during development. Second, early-born neurons are more highly
connected (tendency to form hubs) than later born neurons. This indicates that
the longer time frame available to them might underlie high connectivity. Both
outcomes are not observed for random connection formation. The study finds that
around one-third of electrically coupled long-range connections are late
forming, raising the question of what mechanisms are involved in ensuring their
accuracy, particularly in light of the extremely invariant connectivity
observed in C. elegans. In conclusion, the sequence of neural network
development highlights the possibility of early contact or interaction in
securing long-distance and high-degree connectivity
High speed all optical networks
An inherent problem of conventional point-to-point wide area network (WAN) architectures is that they cannot translate optical transmission bandwidth into comparable user available throughput due to the limiting electronic processing speed of the switching nodes. The first solution to wavelength division multiplexing (WDM) based WAN networks that overcomes this limitation is presented. The proposed Lightnet architecture takes into account the idiosyncrasies of WDM switching/transmission leading to an efficient and pragmatic solution. The Lightnet architecture trades the ample WDM bandwidth for a reduction in the number of processing stages and a simplification of each switching stage, leading to drastically increased effective network throughputs. The principle of the Lightnet architecture is the construction and use of virtual topology networks, embedded in the original network in the wavelength domain. For this construction Lightnets utilize the new concept of lightpaths which constitute the links of the virtual topology. Lightpaths are all-optical, multihop, paths in the network that allow data to be switched through intermediate nodes using high throughput passive optical switches. The use of the virtual topologies and the associated switching design introduce a number of new ideas, which are discussed in detail
The placement of the head that maximizes predictability. An information theoretic approach
The minimization of the length of syntactic dependencies is a
well-established principle of word order and the basis of a mathematical theory
of word order. Here we complete that theory from the perspective of information
theory, adding a competing word order principle: the maximization of
predictability of a target element. These two principles are in conflict: to
maximize the predictability of the head, the head should appear last, which
maximizes the costs with respect to dependency length minimization. The
implications of such a broad theoretical framework to understand the
optimality, diversity and evolution of the six possible orderings of subject,
object and verb are reviewed.Comment: in press in Glottometric
A Literature Survey of Cooperative Caching in Content Distribution Networks
Content distribution networks (CDNs) which serve to deliver web objects
(e.g., documents, applications, music and video, etc.) have seen tremendous
growth since its emergence. To minimize the retrieving delay experienced by a
user with a request for a web object, caching strategies are often applied -
contents are replicated at edges of the network which is closer to the user
such that the network distance between the user and the object is reduced. In
this literature survey, evolution of caching is studied. A recent research
paper [15] in the field of large-scale caching for CDN was chosen to be the
anchor paper which serves as a guide to the topic. Research studies after and
relevant to the anchor paper are also analyzed to better evaluate the
statements and results of the anchor paper and more importantly, to obtain an
unbiased view of the large scale collaborate caching systems as a whole.Comment: 5 pages, 5 figure
The front of the epidemic spread and first passage percolation
In this paper we establish a connection between epidemic models on random
networks with general infection times considered in Barbour and Reinert 2013
and first passage percolation. Using techniques developed in Bhamidi, van der
Hofstad, Hooghiemstra 2012, when each vertex has infinite contagious periods,
we extend results on the epidemic curve in Barbour Reinert 2013 from bounded
degree graphs to general sparse random graphs with degrees having finite third
moments as the number of vertices tends to infinity. We also study the epidemic
trail between the source and typical vertices in the graph. This connection to
first passage percolation can be also be used to study epidemic models with
general contagious periods as in Barbour Reinert 2013 without bounded degree
assumptions.Comment: 14 page
A new design principle of robust onion-like networks self-organized in growth
Today's economy, production activity, and our life are sustained by social
and technological network infrastructures, while new threats of network attacks
by destructing loops have been found recently in network science. We inversely
take into account the weakness, and propose a new design principle for
incrementally growing robust networks. The networks are self-organized by
enhancing interwoven long loops. In particular, we consider the range-limited
approximation of linking by intermediations in a few hops, and show the strong
robustness in the growth without degrading efficiency of paths. Moreover, we
demonstrate that the tolerance of connectivity is reformable even from
extremely vulnerable real networks according to our proposed growing process
with some investment. These results may indicate a prospective direction to the
future growth of our network infrastructures.Comment: 21 pages, 10 figures, 1 tabl
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