2,870 research outputs found
Perspective: network-guided pattern formation of neural dynamics
The understanding of neural activity patterns is fundamentally linked to an
understanding of how the brain's network architecture shapes dynamical
processes. Established approaches rely mostly on deviations of a given network
from certain classes of random graphs. Hypotheses about the supposed role of
prominent topological features (for instance, the roles of modularity, network
motifs, or hierarchical network organization) are derived from these
deviations. An alternative strategy could be to study deviations of network
architectures from regular graphs (rings, lattices) and consider the
implications of such deviations for self-organized dynamic patterns on the
network. Following this strategy, we draw on the theory of spatiotemporal
pattern formation and propose a novel perspective for analyzing dynamics on
networks, by evaluating how the self-organized dynamics are confined by network
architecture to a small set of permissible collective states. In particular, we
discuss the role of prominent topological features of brain connectivity, such
as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the
notion of network-guided pattern formation with numerical simulations and
outline how it can facilitate the understanding of neural dynamics
Élőlények kollektív viselkedésének statisztikus fizikája = Statistical physics of the collective behaviour of organisms
Experiments: We have carried out quantitative experiments on the collective motion of cells as a function of their density. A sharp transition could be observed from the random motility in sparse cultures to the flocking of dense islands of cells. Using ultra light GPS devices developed by us, we have determined the existing hierarchical relations within a flock of 10 homing pigeons. Modelling: From the simulations of our new model of flocking we concluded that the information exchange between particles was maximal at the critical point, in which the interplay of such factors as the level of noise, the tendency to follow the direction and the acceleration of others results in large fluctuations. Analysis: We have proposed a novel link-density based approach to finding overlapping communities in large networks. The algorithm used for the implementation of this technique is very efficient for most real networks, and provides full statistics quickly. Correspondingly, we have developed a by now popular, user-friendly, freely downloadable software for finding overlapping communities. Extending our method to the time-dependent regime, we found that large groups in evolving networks persist for longer if they are capable of dynamically altering their membership, thus, an ability to change the group composition results in better adaptability. We also showed that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime. Experiments: We have carried out quantitative experiments on the collective motion of cells as a function of their density. A sharp transition could be observed from the random motility in sparse cultures to the flocking of dense islands of cells. Using ultra light GPS devices developed by us, we have determined the existing hierarchical relations within a flock of 10 homing pigeons. Modelling: From the simulations of our new model of flocking we concluded that the information exchange between particles was maximal at the critical point, in which the interplay of such factors as the level of noise, the tendency to follow the direction and the acceleration of others results in large fluctuations. Analysis: We have proposed a novel link-density based approach to finding overlapping communities in large networks. The algorithm used for the implementation of this technique is very efficient for most real networks, and provides full statistics quickly. Correspondingly, we have developed a by now popular, user-friendly, freely downloadable software for finding overlapping communities. Extending our method to the time-dependent regime, we found that large groups in evolving networks persist for longer if they are capable of dynamically altering their membership, thus, an ability to change the group composition results in better adaptability. We also showed that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime
The brain: What is critical about it?
We review the recent proposal that the most fascinating brain properties are
related to the fact that it always stays close to a second order phase
transition. In such conditions, the collective of neuronal groups can reliably
generate robust and flexible behavior, because it is known that at the critical
point there is the largest abundance of metastable states to choose from. Here
we review the motivation, arguments and recent results, as well as further
implications of this view of the functioning brain.Comment: Proceedings of BIOCOMP2007 - Collective Dynamics: Topics on
Competition and Cooperation in the Biosciences. Vietri sul Mare, Italy (2007
Cell Towers as Urban Sensors: Understanding the Strengths and Limitations of Mobile Phone Location Data
Understanding urban dynamics and human mobility patterns not only benefits a wide range of real-world applications (e.g., business site selection, public transit planning), but also helps address many urgent issues caused by the rapid urbanization processes (e.g., population explosion, congestion, pollution). In the past few years, given the pervasive usage of mobile devices, call detail records collected by mobile network operators has been widely used in urban dynamics and human mobility studies. However, the derived knowledge might be strongly biased due to the uneven distribution of people’s phone communication activities in space and time.
This dissertation research applies different analytical methods to better understand human activity and urban environment, as well as their interactions, mainly based on a new type of data source: actively tracked mobile phone location data. In particular, this dissertation research achieves three main research objectives. First, this research develops visualization and analysis approaches to uncover hidden urban dynamics patterns from actively tracked mobile phone location data. Second, this research designs quantitative methods to evaluate the representativeness issue of call detail record data. Third, this research develops an appropriate approach to evaluate the performance of different types of tracking data in urban dynamics research.
The major contributions of this dissertation research include: 1) uncovering the dynamics of stay/move activities and distance decay effects, and the changing human mobility patterns based on several mobility indicators derived from actively tracked mobile phone location data; 2) taking the first step to evaluate the representativeness and effectiveness of call detail record and revealing its bias in human mobility research; and 3) extracting and comparing urban-level population movement patterns derived from three different types of tracking data as well as their pros and cons in urban population movement analysis
Mining and correlating traffic events from human sensor observations with official transport data using self-organizing-maps
Cities are complex systems, where related Human activities are increasingly difficult to explore within. In order to understand urban processes and to gain deeper knowledge about cities, the potential of location-based social networks like Twitter could be used a promising example to explore latent relationships of underlying mobility patterns. In this paper, we therefore present an approach using a geographic self-organizing map (Geo-SOM) to uncover and compare previously unseen patterns from social media and authoritative data. The results, which we validated with Live Traffic Disruption (TIMS) feeds from Transport for London, show that the observed geospatial and temporal patterns between special events (r = 0.73), traffic incidents (r = 0.59) and hazard disruptions (r = 0.41) from TIMS, are strongly correlated with traffic-related, georeferenced tweets. Hence, we conclude that tweets can be used as a proxy indicator to detect collective mobility events and may help to provide stakeholders and decision makers with complementary information on complex mobility processes
Predictability of conversation partners
Recent developments in sensing technologies have enabled us to examine the
nature of human social behavior in greater detail. By applying an information
theoretic method to the spatiotemporal data of cell-phone locations, [C. Song
et al. Science 327, 1018 (2010)] found that human mobility patterns are
remarkably predictable. Inspired by their work, we address a similar
predictability question in a different kind of human social activity:
conversation events. The predictability in the sequence of one's conversation
partners is defined as the degree to which one's next conversation partner can
be predicted given the current partner. We quantify this predictability by
using the mutual information. We examine the predictability of conversation
events for each individual using the longitudinal data of face-to-face
interactions collected from two company offices in Japan. Each subject wears a
name tag equipped with an infrared sensor node, and conversation events are
marked when signals are exchanged between sensor nodes in close proximity. We
find that the conversation events are predictable to some extent; knowing the
current partner decreases the uncertainty about the next partner by 28.4% on
average. Much of the predictability is explained by long-tailed distributions
of interevent intervals. However, a predictability also exists in the data,
apart from the contribution of their long-tailed nature. In addition, an
individual's predictability is correlated with the position in the static
social network derived from the data. Individuals confined in a community - in
the sense of an abundance of surrounding triangles - tend to have low
predictability, and those bridging different communities tend to have high
predictability.Comment: 38 pages, 19 figure
Affordances of Historic Urban Landscapes: an Ecological Understanding of Human Interaction with the Past
Heritage has been defined differently in European contexts. Despite differences, a common challenge for historic urban landscape management is the integration of tangible and intangible heritage. Integration demands an active view of perception and human-landscape interaction where intangible values are linked to specific places and meanings are attached to particular cultural practices and socio-spatial organisation. Tangible and intangible values can be examined as part of a system of affordances (potentialities) a place, artefact or cultural practice has to offer. This paper discusses how an ‘affordance analysis’ may serve as a useful tool for the management of historic urban landscapes
Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data
Existing urban boundaries are usually defined by government agencies for
administrative, economic, and political purposes. Defining urban boundaries
that consider socio-economic relationships and citizen commute patterns is
important for many aspects of urban and regional planning. In this paper, we
describe a method to delineate urban boundaries based upon human interactions
with physical space inferred from social media. Specifically, we depicted the
urban boundaries of Great Britain using a mobility network of Twitter user
spatial interactions, which was inferred from over 69 million geo-located
tweets. We define the non-administrative anthropographic boundaries in a
hierarchical fashion based on different physical movement ranges of users
derived from the collective mobility patterns of Twitter users in Great
Britain. The results of strongly connected urban regions in the form of
communities in the network space yield geographically cohesive, non-overlapping
urban areas, which provide a clear delineation of the non-administrative
anthropographic urban boundaries of Great Britain. The method was applied to
both national (Great Britain) and municipal scales (the London metropolis).
While our results corresponded well with the administrative boundaries, many
unexpected and interesting boundaries were identified. Importantly, as the
depicted urban boundaries exhibited a strong instance of spatial proximity, we
employed a gravity model to understand the distance decay effects in shaping
the delineated urban boundaries. The model explains how geographical distances
found in the mobility patterns affect the interaction intensity among different
non-administrative anthropographic urban areas, which provides new insights
into human spatial interactions with urban space.Comment: 32 pages, 7 figures, International Journal of Geographic Information
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