Archive for the ‘Networks’ Category
The developments in the communication and networking technologies have yielded many existing and envisioned information network architectures such as cognitive radio networks, sensor and actor networks, quantum communication networks, terrestrial next generation Internet, and InterPlaNetary Internet. However, there exist many common significant challenges to be addressed for the practical realization of these current and envisioned networking paradigms such as the increased complexity with large scale networks, their dynamic nature, resource constraints, heterogeneous architectures, absence or impracticality of centralized control and infrastructure, need for survivability, and unattended resolution of potential failures. These challenges have been successfully dealt with by Nature, which, as a result of millions of years of evolution, have yielded many biological systems and processes with intrinsic appealing characteristics such as adaptivity to varying environmental conditions, inherent resiliency to failures and damages, successful and collaborative operation on the basis of a limited set of rules and with global intelligence which is larger than superposition of individuals, self-organization, survivability, and evolvability. Inspired by these characteristics, many researchers are currently engaged in developing innovative design paradigms to address the networking challenges of existing and envisioned information systems. In this paper, the current state-of-the-art in bio-inspired networking is captured. The existing bio-inspired networking and communication protocols and algorithms devised by looking at biology as a source of inspiration, and by mimicking the laws and dynamics governing these systems are presented along with open research issues for the bio-inspired networking. Furthermore, the domain of bio-inspired networking is linked to the emerging research domain of nano networks, which bring a set of unique challenges. The objective of this survey is to provide better understanding of the potentials for bio-inspired networking which is currently far from being fully recognized, and to motivate the research community to further explore this timely and exciting topic.
Virtually all domains of cognitive function require the integration of distributed neural activity. Network analysis of human brain connectivity has consistently identified sets of regions that are critically important for enabling efficient neuronal signaling and communication. The central embedding of these candidate ‘brain hubs’ in anatomical networks supports their diverse functional roles across a broad range of cognitive tasks and widespread dynamic coupling within and across functional networks. The high level of centrality of brain hubs also renders them points of vulnerability that are susceptible to disconnection and dysfunction in brain disorders. Combining data from numerous empirical and computational studies, network approaches strongly suggest that brain hubs play important roles in information integration underpinning numerous aspects of complex cognitive function.
Network approaches to neuroscience are currently accelerating at a rapid pace, propelled by the availability of ‘big data’, an expanding computational infrastructure, and the formation of large-scale research consortia and initiatives focused on mapping brain connectivity. As these developments unfold, it seems certain that the study of brain network hubs will remain an enduring theme in the quest to better understand the complex function of the human brain.
Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes—flexibility and selection—must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.
We do not know how the human brain mediates complex and creative behaviors such as artistic, scientific, and mathematical thought. Scholars theorize that these abilities require conscious experience as realized in a widespread neural network, or “mental workspace,” that represents and manipulates images, symbols, and other mental constructs across a variety of domains. Evidence for such a complex, interconnected network has been difficult to produce with current techniques that mainly study brain activity in isolation and are insensitive to distributed informational processes. The present work takes advantage of emerging techniques in network and information analysis to provide empirical support for such a widespread and interconnected information processing network in the brain that supports the manipulation of visual imagery.
The conscious manipulation of mental representations is central to many creative and uniquely human abilities. How does the human brain mediate such flexible mental operations? Here, multivariate pattern analysis of functional MRI data reveals a widespread neural network that performs specific mental manipulations on the contents of visual imagery. Evolving patterns of neural activity within this mental workspace track the sequence of informational transformations carried out by these manipulations. The network switches between distinct connectivity profiles as representations are maintained or manipulated.
James Fowler, a professor at UC-San Diego, is engaged in highly innovative and important research at the crossroads of political science and biology. His recent paper in the Proceedings of the National Academy of Sciences, “Correlated Genotypes in Friendship Networks“, represents an important new study in an emerging research field that is exploring the genetic and biological foundations for our political and social behavior.
In this paper, James and his colleagues Jaime Settle and Nicholas Christakis demonstrate that there is what they call “genotypic clustering in social networks“, by statistically examining the association between markers for six different genes and the reported friendship networks from respondents in data from the National Longitudinal Study of Adolescent Health and the Framingham Heart Study Social Network. They show that one of these genes (DRD2) is positively associated with in friendship networks, meaning that those who have this gene are more likely to be friends with others who have this gene, controlling for demographic similarities and population stratification; another gene, CYP2A6 has a negative association in friendship networks.
That the brain is a powerful and complex organ is no mystery. But what researchers have begun to discover is that there are select areas of the brain that are so dense in their activity and interconnections that researchers have dubbed them the “rich clubs” of the brain. There are regions of the brain in which connectivity is extraordinarily dense — that’s been known for some time. What the present study set out to do was visualize how these dense regions might be connected to one another, possibly forming an elite network between these distinct and powerful regions of the brain.
They found exactly that: Twelve discrete hubs in the brain were interconnected with one another across hemispheres, forming what the researchers call a “rich club,” distinct from the regular or “lower” network of the brain.