Spreading dynamics on spatially constrained complex brain networks

The study of dynamical systems defined on complex networks provides a natural framework with which to investigate myriad features of neural dynamics and has been widely undertaken. Typically, however, networks employed in theoretical studies bear little relation to the spatial embedding or connectivity of the neural networks that they attempt to replicate. Here, we employ detailed neuroimaging data to define a network whose spatial embedding represents accurately the folded structure of the cortical surface of a rat brain and investigate the propagation of activity over this network under simple spreading and connectivity rules. By comparison with standard network models with the same coarse statistics, we show that the cortical geometry influences profoundly the speed of propagation of activation through the network. Our conclusions are of high relevance to the theoretical modelling of epileptic seizure events and indicate that such studies which omit physiological network structure risk simplifying the dynamics in a potentially significant way

On inversions and Doob hh-transforms of linear diffusions

Let $X$ be a regular linear diffusion whose state space is an open interval $E\subseteq\mathbb{R}$. We consider a diffusion $X^*$ which probability law is obtained as a Doob $h$-transform of the law of $X$, where $h$ is a positive harmonic function for the infinitesimal generator of $X$ on $E$. This is the dual of $X$ with respect to $h(x)m(dx)$ where $m(dx)$ is the speed measure of $X$. Examples include the case where $X^*$ is $X$ conditioned to stay above some fixed level. We provide a construction of $X^*$ as a deterministic inversion of $X$, time changed with some random clock. The study involves the construction of some inversions which generalize the Euclidean inversions. Brownian motion with drift and Bessel processes are considered in details.Comment: 19 page

Conceptualizing cultures of violence and cultural change

The historiography of violence has undergone a distinct cultural turn as attention has shifted from examining violence as a clearly defined (and countable) social problem to analysing its historically defined 'social meaning'. Nevertheless, the precise nature of the relationship between 'violence' and 'culture' is still being established. How are 'cultures of violence' formed? What impact do they have on violent behaviour? How do they change? This essay examines some of the conceptual aspects of the relationship between culture and violence. It brings together empirical research into nineteenth-century England with recent research results from other European contexts to examine three aspects of the relationship between culture and violence. These are organised under the labels 'seeing violence', 'identifying the violent' and 'changing violence'. Within a particular society, narratives regarding particular kinds of behaviour shape cultural attitudes. The notion 'violence' is thus defined in relation to physically aggressive acts as well as by being connected to other kinds of attitudes and contexts. As a result, the boundaries between physical aggression which is legitimate and that which is illegitimate (and thus 'violence') are set. Once 'violence' is defined, particular cultures form ideas about who is responsible for it: reactions to violence become associated with social arrangements such as class and gender as well as to attitudes toward the self. Finally, cultures of violence make efforts to tame or eradicate illegitimate forms of physical aggression. This process is not only connected to the development of new forms of power (e.g., new policing or punishment strategies) but also to less tangible cultural influences which aim at changing the behaviour defined as violence (in particular among the social groups identified as violent). Even if successful, this three-tiered process of seeing violence, identifying the violent and changing violence continues anew, emphasising the ways that cultures of violence develop through a continuous process of reevaluation and reinvention

Localized transverse bursts in inclined layer convection

We investigate a novel bursting state in inclined layer thermal convection in which convection rolls exhibit intermittent, localized, transverse bursts. With increasing temperature difference, the bursts increase in duration and number while exhibiting a characteristic wavenumber, magnitude, and size. We propose a mechanism which describes the duration of the observed bursting intervals and compare our results to bursting processes in other systems.Comment: 4 pages, 8 figure

Granger causality and transfer entropy are equivalent for Gaussian variables

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained traction in a similarly wide field. While it has been recognized that the two concepts must be related, the exact relationship has until now not been formally described. Here we show that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.Comment: In review, Phys. Rev. Lett., Nov. 200

Shot-noise limited monitoring and phase locking of the motion of a single trapped ion

We perform high-resolution real-time read-out of the motion of a single trapped and laser-cooled Ba ion. By using an interferometric setup we demonstrate shot-noise limited measurement of thermal oscillations with resolution of 4 times the standard quantum limit. We apply the real-time monitoring for phase control of the ion motion through a feedback loop, suppressing the photon recoil-induced phase diffusion. Due to the spectral narrowing in phase-locked mode, the coherent ion oscillation is measured with resolution of about 0.3 times the standard quantum limit

Multivariate Granger Causality and Generalized Variance

Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables, but may occur among groups, or "ensembles", of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke's seminal 1982 work, we offer new justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define "partial" Granger causality in the multivariate context and we also motivate reformulations of "causal density" and "Granger autonomy". Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise.Comment: added 1 reference, minor change to discussion, typos corrected; 28 pages, 3 figures, 1 table, LaTe

Web-Based Training for Native American Tribes

The authors describe a project undertaken at the School of Hotel and Restaurant Management at Northern Arizona University in which the internet is used to present Native American tribes in Arizona with customer service training. It discusses why the project was instigated looks at its development and funding, and highlights the educational and technological challenges that had to be overcome. This is the second in a series of articles on the uses of the internet in educating non-university student constituencies interested in hospitality management.\u2

Causal connectivity of evolved neural networks during behavior

To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics

Multiple G-It\^{o} integral in the G-expectation space

In this paper, motivated by mathematic finance we introduce the multiple G-It\^{o} integral in the G-expectation space, then investigate how to calculate. We get the the relationship between Hermite polynomials and multiple G-It\^{o} integrals which is a natural extension of the classical result obtained by It\^{o} in 1951.Comment: 9 page