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Riccardo Barbieri

Bio: Riccardo Barbieri is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Heartbeat & Supersymmetry. The author has an hindex of 52, co-authored 375 publications receiving 10690 citations. Previous affiliations of Riccardo Barbieri include Medical University of South Carolina & University of Pisa.


Papers
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Journal ArticleDOI
TL;DR: The Inert Doublet Model as discussed by the authors is an extension of the Standard Model to include a second Higgs doublet that has neither a vev nor couplings to quarks and leptons.
Abstract: The quadratic divergences of the Higgs mass may be cancelled either accidentally or by the exchange of some new particles. Alternatively its impact on naturalness may be weakened by raising the Higgs mass, which requires changing the Standard Model below its natural cut-off. We show in detail how this can be achieved, while preserving perturbativity and consistency with the electroweak precision tests, by extending the Standard Model to include a second Higgs doublet that has neither a vev nor couplings to quarks and leptons. This Inert Doublet Model yields a perturbative and completely natural description of electroweak physics at all energies up to 1.5 TeV. The discrete symmetry that yields the Inert Doublet is unbroken, so that Dark Matter may be composed of neutral inert Higgs bosons, which may have escaped detection at LEP2. Predictions are given for multilepton events with missing transverse energy at the Large Hadron Collider, and for the direct detection of dark matter.

888 citations

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TL;DR: The time-rescaling theorem may be used to develop goodness-of-fit tests for both parametric and histogram-based point process models of neural spike trains, and a proof using only elementary probability theory arguments is presented.
Abstract: Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model's validity prior to using it to make inferences about a particular neural system. Assessing goodness-of-fit is a challenging problem for point process neural spike train models, especially for histogram-based models such as perstimulus time histograms (PSTH) and rate functions estimated by spike train smoothing. The time-rescaling theorem is a well-known result in probability theory, which states that any point process with an integrable conditional intensity function may be transformed into a Poisson process with unit rate. We describe how the theorem may be used to develop goodness-of-fit tests for both parametric and histogram-based point process models of neural spike trains. We apply these tests in two examples: a comparison of PSTH, inhomogeneous Poisson, and inhomogeneous Markov interval models of neural spike trains from the supplementary eye field of a macque monkey and a comparison of temporal and spatial smoothers, inhomogeneous Poisson, inhomogeneous gamma, and inhomogeneous inverse gaussian models of rat hippocampal place cell spiking activity. To help make the logic behind the time-rescaling theorem more accessible to researchers in neuroscience, we present a proof using only elementary probability theory arguments. We also show how the theorem may be used to simulate a general point process model of a spike train. Our paradigm makes it possible to compare parametric and histogram-based neural spike train models directly. These results suggest that the time-rescaling theorem can be a valuable tool for neural spike train data analysis.

590 citations

Journal ArticleDOI
TL;DR: This work uses the Bayes' rule Chapman-Kolmogorov paradigm with a linear state equation and point process observation models to derive adaptive filters appropriate for estimation from neural spike trains and suggests a practical approach for constructing filtering algorithms to track neural receptive field dynamics on a millisecond timescale.
Abstract: Neural receptive fields are dynamic in that with experience, neurons change their spiking responses to relevant stimuli. To understand how neural systems adapt their representations of biological information, analyses of receptive field plasticity from experimental measurements are crucial. Adaptive signal processing, the well-established engineering discipline for characterizing the temporal evolution of system parameters, suggests a framework for studying the plasticity of receptive fields. We use the Bayes' rule Chapman-Kolmogorov paradigm with a linear state equation and point process observation models to derive adaptive filters appropriate for estimation from neural spike trains. We derive point process filter analogues of the Kalman filter, recursive least squares, and steepest-descent algorithms and describe the properties of these new filters. We illustrate our algorithms in two simulated data examples. The first is a study of slow and rapid evolution of spatial receptive fields in hippocampal neurons. The second is an adaptive decoding study in which a signal is decoded from ensemble neural spiking activity as the receptive fields of the neurons in the ensemble evolve. Our results provide a paradigm for adaptive estimation for point process observations and suggest a practical approach for constructing filtering algorithms to track neural receptive field dynamics on a millisecond timescale.

379 citations

Journal ArticleDOI
TL;DR: In this paper, the authors propose a possible interpretation of these anomalies in the context of weakly broken $$U(2)^5$$ B-decays with leptoquark mediators.
Abstract: The collection of a few anomalies in semileptonic B-decays invites to speculate about the emergence of some strikingly new phenomena. Here we offer a possible interpretation of these anomalies in the context of a weakly broken $$U(2)^5$$ flavor symmetry and leptoquark mediators.

350 citations

Journal ArticleDOI
TL;DR: A new method which relates cardiac-gated fMRI timeseries with continuous-time heart rate variability (HRV) to estimate central autonomic processing and demonstrated HF correlation with fMRI activity in the hypothalamus, cerebellum, parabrachial nucleus/locus ceruleus, periaqueductal gray, amygdala, hippocampus, thalamus, and dorsomedial/dorsolateral prefrontal, posterior insular, and middle temporal cortices.

314 citations


Cited by
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Journal ArticleDOI
Claude Amsler1, Michael Doser2, Mario Antonelli, D. M. Asner3  +173 moreInstitutions (86)
TL;DR: This biennial Review summarizes much of particle physics, using data from previous editions.

12,798 citations

Journal ArticleDOI
TL;DR: The current status of particle dark matter, including experimental evidence and theoretical motivations, including direct and indirect detection techniques, is discussed in this paper. But the authors focus on neutralinos in models of supersymmetry and Kaluza-Klein dark matter in universal extra dimensions.

4,614 citations

Journal ArticleDOI
TL;DR: In this article, theoretical and phenomenological aspects of two-Higgs-doublet extensions of the Standard Model are discussed and a careful study of spontaneous CP violation is presented, including an analysis of the conditions which have to be satisfied in order for a vacuum to violate CP.

2,395 citations

Journal ArticleDOI
TL;DR: The various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV are discussed.
Abstract: Heart rate variability (HRV) is a reliable reflection of the many physiological factors modulating the normal rhythm of the heart. In fact, they provide a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems. It shows that the structure generating the signal is not only simply linear, but also involves nonlinear contributions. Heart rate (HR) is a nonstationary signal; its variation may contain indicators of current disease, or warnings about impending cardiac diseases. The indicators may be present at all times or may occur at random-during certain intervals of the day. It is strenuous and time consuming to study and pinpoint abnormalities in voluminous data collected over several hours. Hence, HR variation analysis (instantaneous HR against time axis) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics. Therefore, the HRV signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. In this paper, we have discussed the various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV.

2,344 citations

Journal ArticleDOI
TL;DR: A meta-analysis of recent neuroimaging studies on the relationship between heart rate variability and regional cerebral blood flow identified a number of regions, including the amygdala and ventromedial prefrontal cortex, in which significant associations across studies were found.

2,174 citations