Neuronal network model of interictal and recurrent ictal activity

This is the final version of the article. Available from American Physical Society via the DOI in this record.We propose a neuronal network model which undergoes a saddle node on an invariant circle bifurcation as the mechanism of the transition from the interictal to the ictal (seizure) state. In the vicinity of this transition, the model captures important dynamical features of both interictal and ictal states. We study the nature of interictal spikes and early warnings of the transition predicted by this model. We further demonstrate that recurrent seizures emerge due to the interaction between two networks.This work was partially supported by FET IP Project MULTIPLEX 317532. A.V.G. is grateful to LA I3N for Grant No. PEST UID/CTM/50025/2013. M.A.L. acknowledges the financial support of the Medical Research Council (MRC) via Grant No. MR/K013998/01. K.E.L. was supported by the Department of Anesthesiology at the University of Michigan and the National Institutes of Health, Grant No. RO1 GM098578

Raman spectroscopy as a versatile tool for studying the properties of graphene.

Raman spectroscopy is an integral part of graphene research. It is used to determine the number and orientation of layers, the quality and types of edge, and the effects of perturbations, such as electric and magnetic fields, strain, doping, disorder and functional groups. This, in turn, provides insight into all sp(2)-bonded carbon allotropes, because graphene is their fundamental building block. Here we review the state of the art, future directions and open questions in Raman spectroscopy of graphene. We describe essential physical processes whose importance has only recently been recognized, such as the various types of resonance at play, and the role of quantum interference. We update all basic concepts and notations, and propose a terminology that is able to describe any result in literature. We finally highlight the potential of Raman spectroscopy for layered materials other than graphene

Frequently asked questions about chlorophyll fluorescence, the sequel

[EN] Using chlorophyll (Chl) a fluorescence many aspects of the photosynthetic apparatus can be studied, both in vitro and, noninvasively, in vivo. Complementary techniques can help to interpret changes in the Chl a fluorescence kinetics. Kalaji et al. (Photosynth Res 122: 121-158, 2014a) addressed several questions about instruments, methods and applications based on Chl a fluorescence. Here, additionalChl a fluorescence-related topics are discussed again in a question and answer format. Examples are the effect of connectivity on photochemical quenching, the correction of F-V/F-M values for PSI fluorescence, the energy partitioning concept, the interpretation of the complementary area, probing the donor side of PSII, the assignment of bands of 77 K fluorescence emission spectra to fluorescence emitters, the relationship between prompt and delayed fluorescence, potential problems when sampling tree canopies, the use of fluorescence parameters in QTL studies, the use of Chl a fluorescence in biosensor applications and the application of neural network approaches for the analysis of fluorescence measurements. The answers draw on knowledge fromdifferent Chl a fluorescence analysis domains, yielding in several cases new insights.Kalaji, H.; Schansker, G.; Brestic, M.; Bussotti, F.; Calatayud, A.; Ferroni, L.; Goltsev, V.... (2017). Frequently asked questions about chlorophyll fluorescence, the sequel. 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The Two Regimes of Neutral Evolution: Localization on Hubs and Delocalized Diffusion

It has been argued that much of evolution takes place in the absence of fitness gradients. Such periods of evolution can be analysed by examining the mutational network formed by sequences of equal fitness, that is the neutral network. It has been demonstrated that, in large populations under a high mutation rate, the population distribution over the neutral network and average mutational robustness are given by the principle eigenvector and eigenvalue, respectively, of the network’s adjacency matrix. However, little progress has been made towards understanding the manner in which the topology of the neutral network influences the resulting population distribution and robustness. In this work, we build on recent results from spectral graph theory and utilize numerical methods to demonstrate that there exist two regimes of behaviour: convergence on hubs and diffusion over the network. We also derive approximations for the population’s behaviour under these regimes. This challenges the widespread assumption that neutral evolution always leads to exploration of the neutral network and elucidates the conditions which result in the evolution of robust organisms

Using Link Clustering to Detect Influential Spreaders

Spreading processes are increasingly analysed in the context of complex networks, for example in epidemics research, information dissemination or rumors. In these contexts, the effect of structural properties that facilitate or decelerate spreading processes are of high interest, since this allows insights into the extent to which those processes are controllable and predictable. In social networks, actors usually participate in different densely connected social groups that emerge from various social contexts, such as workplace, interests, etc. In this paper, it is examined if the number of groups an actor connects to can be used as a predictor for its capability to spread information effectively. The social contexts (i.e. groups) a node participates in are determined by the Link Communities approach by Ahn et al. (2010). The results are contrasted to previous findings of structural node properties based on the k-shell index of nodes (Kitsak et al. 2010) by applying both methods on artificially generated and real-world networks. They show that the approach is comparable to existing ones using structural node properties as a predictor, yet no clear evidence is found indicating that one or the other approach leads to better predictions in all investigated networks

Rejection-Based Simulation of Non-Markovian Agents on Complex Networks

Stochastic models in which agents interact with their neighborhood according to a network topology are a powerful modeling framework to study the emergence of complex dynamic patterns in real-world systems. Stochastic simulations are often the preferred\u2014sometimes the only feasible\u2014way to investigate such systems. Previous research focused primarily on Markovian models where the random time until an interaction happens follows an exponential distribution. In this work, we study a general framework to model systems where each agent is in one of several states. Agents can change their state at random, influenced by their complete neighborhood, while the time to the next event can follow an arbitrary probability distribution. Classically, these simulations are hindered by high computational costs of updating the rates of interconnected agents and sampling the random residence times from arbitrary distributions. We propose a rejection-based, event-driven simulation algorithm to overcome these limitations. Our method over-approximates the instantaneous rates corresponding to inter-event times while rejection events counter-balance these over-approximations. We demonstrate the effectiveness of our approach on models of epidemic and information spreading