Karthikeyan Rajendran

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Quiet standing balance and postural control are often assessed by drawing information from center of pressure (COP) data collected with a force platform. Efforts to better understand the underlying processes involved in postural control have lead to methods that examine the dynamic or stochastic characteristics of the COP. One method that has recently(More)
The formation of oscillating phase clusters in a network of identical Hodgkin-Huxley neurons is studied, along with their dynamic behavior. The neurons are synaptically coupled in an all-to-all manner, yet the synaptic coupling characteristic time is heterogeneous across the connections. In a network of N neurons where this heterogeneity is characterized by(More)
The goal of this investigation was to explore the use of spectral analysis to examine the data obtained during computerized dynamic posturography (CDP). In particular, we examined whether spectral analysis would provide more detailed information about underlying postural control strategies and potential learning across conditions and trials of the sensory(More)
We consider the simplest network of coupled non-identical phase oscillators capable of displaying a "chimera" state (namely, two subnetworks with strong coupling within the subnetworks and weaker coupling between them) and systematically investigate the effects of gradually removing connections within the network, in a random but systematically specified(More)
We present a computer-assisted approach to coarse graining the evolutionary dynamics of a system of nonidentical oscillators coupled through a (fixed) network structure. The existence of a spectral gap for the coupling network graph Laplacian suggests that the graph dynamics may quickly become low dimensional. Our first choice of coarse variables consists(More)
We explore a systematic approach to studying the dynamics of evolving networks at a coarse-grained, system level. We emphasize the importance of finding good observables (network properties) in terms of which coarse grained models can be developed. We illustrate our approach through a particular social network model: the “rise and fall” of a networked(More)
Katherine A. Bold,1, ∗ Karthikeyan Rajendran,2, † Balázs Ráth,3, ‡ and Ioannis G. Kevrekidis2, 4, § Program in Applied and Computational Mathematics (PACM), Princeton University, Princeton, New Jersey 08544, USA Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA Institute of Mathematics, Budapest(More)
We discuss the problem of extending data mining approaches to cases in which data points arise in the form of individual graphs. Being able to find the intrinsic low-dimensionality in ensembles of graphs can be useful in a variety of modeling contexts, especially when coarse-graining the detailed graph information is of interest. One of the main challenges(More)