1,250 research outputs found

    Reply to "Comment on `Performance of different synchronization measures in real data: A case study on electroencephalographic signals'"

    Get PDF
    We agree with the Comment by Nicolaou and Nasuto about the utility of mutual information (MI) when properly estimated and we also concur with their view that the estimation based on k nearest neighbors gives optimal results. However, we claim that embedding parameters can indeed change MI results, as we show for the electroencephalogram data sets of our original study and for coupled chaotic systems. Furthermore, we show that proper embedding can actually improve the estimation of MI with the k nearest neighbors algorithm

    Event synchronization: a simple and fast method to measure synchronicity and time delay patterns

    Get PDF
    We propose a simple method to measure synchronization and time delay patterns between signals. It is based on the relative timings of events in the time series, defined e.g. as local maxima. The degree of synchronization is obtained from the number of quasi-simultaneous appearances of events, and the delay is calculated from the precedence of events in one signal with respect to the other. Moreover, we can easily visualize the time evolution of the delay and synchronization level with an excellent resolution. We apply the algorithm to short rat EEG signals, some of them containing spikes. We also apply it to an intracranial human EEG recording containing an epileptic seizure, and we propose that the method might be useful for the detection of foci and for seizure prediction. It can be easily extended to other types of data and it is very simple and fast, thus being suitable for on-line implementations.Comment: 6 pages, including 6 figures, RevTe

    Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering

    Get PDF
    This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods

    Bringing the Theory of Street-Level Bureaucrats into the 21st Century: A Study of Social Workers in Louisiana

    Get PDF
    This study examines the applicability of Michael Lipsky’s (1980) concept of “street-level bureaucracy” to the profession of social work in 2019. Street-level bureaucrats are public service workers “who interact with citizens in the regular course of their jobs; have significant independence in decision making, and potentially have extensive impact on the lives of their citizens” (Lipsky, 1980:3). They are faced with uncertainties in their work related to inadequate resources, unclear policies, and caseloads/workloads that defy what may be possible to achieve by any one worker. Workers develop routines and “coping mechanisms,” to manage their environments. The routines that they develop then become effective public policy for their clients. The street-level bureaucracy theory has been widely applied, but generally with the assumption that street-level bureaucrats are homogenous across occupations and settings. Recent research suggests the need for more nuanced approaches, especially with regard to the effects of professionalism, individual characteristics of workers, and the variety of circumstances in which they interact with clients. Yet most research utilizes small numbers of cases, making it difficult to measure differences among types of workers. The present study addresses that gap with a large survey of social workers in Louisiana. Findings show that these street-level bureaucrats do exercise discretion, but circumstances in which they do so vary significantly, even within a single profession. Further, ways in which they exercise discretion differ from those described by Lipsky. Instead of using coping mechanisms to buffer themselves from an otherwise overwhelming environment, the respondents in this study report consultation with peers and management to find ways to serve client needs. These findings have implications for both the study of street-level bureaucracy and the practice of social work. Keywords: Discretion, decision-making, street-level bureaucracy, social work, coping mechanism

    Surface modification of porous graphite for liquid chromatography

    Get PDF

    Electrochemical deprotonation of phosphate on stainless steel

    Get PDF
    Voltammetric experiments performed in phosphate buffer at constant pH 8.0 on platinum and stainless steel revealed clear reduction currents, which were correlated to the concentrations of phosphate. On the basis of the reactions proposed previously, a model was elaborated, assuming that both H2PO4 and HPO4 2 underwent cathodic deprotonation, and including the acid–base equilibriums. A kinetic model was derived by analogy with the equations generally used for hydrogen evolution. Numerical fitting of the experimental data confirmed that the phosphate species may act as an efficient catalyst of hydrogen evolution via electrochemical deprotonation. This reaction may introduce an unexpected reversible pathway of hydrogen formation in the mechanisms of anaerobic corrosion. The possible new insights offered by the electrochemical deprotonation of phosphate in microbially influenced corrosion was finally discussed

    Latency and Selectivity of Single Neurons Indicate Hierarchical Processing in the Human Medial Temporal Lobe

    Get PDF
    Neurons in the temporal lobe of both monkeys and humans show selective responses to classes of visual stimuli and even to specific individuals. In this study, we investigate the latency and selectivity of visually responsive neurons recorded from microelectrodes in the parahippocampal cortex, entorhinal cortex, hippocampus, and amygdala of human subjects during a visual object presentation task. During 96 experimental sessions in 35 subjects, we recorded from a total of 3278 neurons. Of these units, 398 responded selectively to one or more of the presented stimuli. Mean response latencies were substantially larger than those reported in monkeys. We observed a highly significant correlation between the latency and the selectivity of these neurons: the longer the latency the greater the selectivity. Particularly, parahippocampal neurons were found to respond significantly earlier and less selectively than those in the other three regions. Regional analysis showed significant correlations between latency and selectivity within the parahippocampal cortex, entorhinal cortex, and hippocampus, but not within the amygdala. The later and more selective responses tended to be generated by cells with sparse baseline firing rates and vice versa. Our results provide direct evidence for hierarchical processing of sensory information at the interface between the visual pathway and the limbic system, by which increasingly refined and specific representations of stimulus identity are generated over time along the anatomic pathways of the medial temporal lobe

    Analyzing Multiple Nonlinear Time Series with Extended Granger Causality

    Full text link
    Identifying causal relations among simultaneously acquired signals is an important problem in multivariate time series analysis. For linear stochastic systems Granger proposed a simple procedure called the Granger causality to detect such relations. In this work we consider nonlinear extensions of Granger's idea and refer to the result as Extended Granger Causality. A simple approach implementing the Extended Granger Causality is presented and applied to multiple chaotic time series and other types of nonlinear signals. In addition, for situations with three or more time series we propose a conditional Extended Granger Causality measure that enables us to determine whether the causal relation between two signals is direct or mediated by another process.Comment: 16 pages, 6 figure
    corecore