The Experts below are selected from a list of 620001 Experts worldwide ranked by ideXlab platform
Michael Breakspear - One of the best experts on this subject based on the ideXlab platform.
-
Dynamic Models of large scale brain activity
Nature Neuroscience, 2017Co-Authors: Michael BreakspearAbstract:Cognitive activity requires the collective behavior of cortical, thalamic and spinal neurons across large-scale systems of the CNS. This paper provides an illustrated introduction to Dynamic Models of large-scale brain activity, from the tenets of the underlying theory to challenges, controversies and recent breakthroughs. Movement, cognition and perception arise from the collective activity of neurons within cortical circuits and across large-scale systems of the brain. While the causes of single neuron spikes have been understood for decades, the processes that support collective neural behavior in large-scale cortical systems are less clear and have been at times the subject of contention. Modeling large-scale brain activity with nonlinear Dynamical systems theory allows the integration of experimental data from multiple modalities into a common framework that facilitates prediction, testing and possible refutation. This work reviews the core assumptions that underlie this computational approach, the methodological framework that fosters the translation of theory into the laboratory, and the emerging body of supporting evidence. While substantial challenges remain, evidence supports the view that collective, nonlinear Dynamics are central to adaptive cortical activity. Likewise, aberrant Dynamic processes appear to underlie a number of brain disorders.
-
Dynamic Models of large scale brain activity
Nature Neuroscience, 2017Co-Authors: Michael BreakspearAbstract:Movement, cognition and perception arise from the collective activity of neurons within cortical circuits and across large-scale systems of the brain. While the causes of single neuron spikes have been understood for decades, the processes that support collective neural behavior in large-scale cortical systems are less clear and have been at times the subject of contention. Modeling large-scale brain activity with nonlinear Dynamical systems theory allows the integration of experimental data from multiple modalities into a common framework that facilitates prediction, testing and possible refutation. This work reviews the core assumptions that underlie this computational approach, the methodological framework that fosters the translation of theory into the laboratory, and the emerging body of supporting evidence. While substantial challenges remain, evidence supports the view that collective, nonlinear Dynamics are central to adaptive cortical activity. Likewise, aberrant Dynamic processes appear to underlie a number of brain disorders.
Alison P Galvani - One of the best experts on this subject based on the ideXlab platform.
-
Dynamic Models of infectious disease transmission in prisons and the general population
Epidemiologic Reviews, 2018Co-Authors: Martial L Ndeffombah, Vivian S Vigliotti, Laura Skrip, Kate Dolan, Alison P GalvaniAbstract:Incarcerated populations experience elevated burdens of infectious diseases, which are exacerbated by limited access to prevention measures. Dynamic Models are used to assess the spread and control of diseases within correctional facilities and repercussions on the general population. Our systematic review of Dynamic Models of infectious diseases within correctional settings identified 34 studies published between 1996 and 2017. Of these, 23 focused on disease Dynamics and intervention in prison without accounting for subsequent spread to the community. The main diseases modeled in these studies were human immunodeficiency virus (HIV; n = 14, 41%), tuberculosis (TB; n = 10, 29%), and hepatitis C virus (HCV; n = 7, 21%). Models were fitted to epidemiologic data in 14 studies; uncertainty and sensitivity analyses were conducted in 8, and validation of model projection against empirical data was done in 1 study. According to the Models, prison-based screening and treatment may be highly effective strategies for reducing the burden of HIV, TB, HCV, and other sexually transmissible infections among prisoners and the general community. Decreasing incarceration rates were projected to reduce HIV and HCV infections among people who inject drugs and TB infections among all prisoners. Limitations of the modeling studies and opportunities for using Dynamic Models to develop quantitative evidence for informing prison infection control measures are discussed.
Gerardo Chowell - One of the best experts on this subject based on the ideXlab platform.
-
Assessing parameter identifiability in compartmental Dynamic Models using a computational approach: application to infectious disease transmission Models
Theoretical Biology and Medical Modelling, 2019Co-Authors: Kimberlyn Roosa, Gerardo ChowellAbstract:Background Mathematical modeling is now frequently used in outbreak investigations to understand underlying mechanisms of infectious disease Dynamics, assess patterns in epidemiological data, and forecast the trajectory of epidemics. However, the successful application of mathematical Models to guide public health interventions lies in the ability to reliably estimate model parameters and their corresponding uncertainty. Here, we present and illustrate a simple computational method for assessing parameter identifiability in compartmental epidemic Models. Methods We describe a parametric bootstrap approach to generate simulated data from Dynamical systems to quantify parameter uncertainty and identifiability. We calculate confidence intervals and mean squared error of estimated parameter distributions to assess parameter identifiability. To demonstrate this approach, we begin with a low-complexity SEIR model and work through examples of increasingly more complex compartmental Models that correspond with applications to pandemic influenza, Ebola, and Zika. Results Overall, parameter identifiability issues are more likely to arise with more complex Models (based on number of equations/states and parameters). As the number of parameters being jointly estimated increases, the uncertainty surrounding estimated parameters tends to increase, on average, as well. We found that, in most cases, R_0 is often robust to parameter identifiability issues affecting individual parameters in the model. Despite large confidence intervals and higher mean squared error of other individual model parameters, R_0 can still be estimated with precision and accuracy. Conclusions Because public health policies can be influenced by results of mathematical modeling studies, it is important to conduct parameter identifiability analyses prior to fitting the Models to available data and to report parameter estimates with quantified uncertainty. The method described is helpful in these regards and enhances the essential toolkit for conducting model-based inferences using compartmental Dynamic Models.
-
assessing parameter identifiability in compartmental Dynamic Models using a computational approach application to infectious disease transmission Models
Theoretical Biology and Medical Modelling, 2019Co-Authors: Kimberlyn Roosa, Gerardo ChowellAbstract:Mathematical modeling is now frequently used in outbreak investigations to understand underlying mechanisms of infectious disease Dynamics, assess patterns in epidemiological data, and forecast the trajectory of epidemics. However, the successful application of mathematical Models to guide public health interventions lies in the ability to reliably estimate model parameters and their corresponding uncertainty. Here, we present and illustrate a simple computational method for assessing parameter identifiability in compartmental epidemic Models. We describe a parametric bootstrap approach to generate simulated data from Dynamical systems to quantify parameter uncertainty and identifiability. We calculate confidence intervals and mean squared error of estimated parameter distributions to assess parameter identifiability. To demonstrate this approach, we begin with a low-complexity SEIR model and work through examples of increasingly more complex compartmental Models that correspond with applications to pandemic influenza, Ebola, and Zika. Overall, parameter identifiability issues are more likely to arise with more complex Models (based on number of equations/states and parameters). As the number of parameters being jointly estimated increases, the uncertainty surrounding estimated parameters tends to increase, on average, as well. We found that, in most cases, R0 is often robust to parameter identifiability issues affecting individual parameters in the model. Despite large confidence intervals and higher mean squared error of other individual model parameters, R0 can still be estimated with precision and accuracy. Because public health policies can be influenced by results of mathematical modeling studies, it is important to conduct parameter identifiability analyses prior to fitting the Models to available data and to report parameter estimates with quantified uncertainty. The method described is helpful in these regards and enhances the essential toolkit for conducting model-based inferences using compartmental Dynamic Models.
Matteo Iacoviello - One of the best experts on this subject based on the ideXlab platform.
-
occbin a toolkit for solving Dynamic Models with occasionally binding constraints easily
Research Papers in Economics, 2014Co-Authors: Luca Guerrieri, Matteo IacovielloAbstract:We describe how to adapt a first-order perturbation approach and apply it in a piecewise fashion to handle occasionally binding constraints in Dynamic Models. Our examples include a real business cycle model with a constraint on the level of investment and a New Keynesian model subject to the zero lower bound on nominal interest rates. We compare the piecewise linear perturbation solution with a high-quality numerical solution that can be taken to be virtually exact. The piecewise linear perturbation method can adequately capture key properties of the Models we consider. A key advantage of this method is its applicability to Models with a large number of state variables.
-
occbin a toolkit for solving Dynamic Models with occasionally binding constraints easily
Journal of Monetary Economics, 2014Co-Authors: Luca Guerrieri, Matteo IacovielloAbstract:Abstract The toolkit adapts a first-order perturbation approach and applies it in a piecewise fashion to solve Dynamic Models with occasionally binding constraints. Our examples include a real business cycle model with a constraint on the level of investment and a New Keynesian model subject to the zero lower bound on nominal interest rates. Compared with a high-quality numerical solution, the piecewise linear perturbation method can adequately capture key properties of the Models we consider. A key advantage of the piecewise linear perturbation method is its applicability to Models with a large number of state variables.
Kevin Murphy - One of the best experts on this subject based on the ideXlab platform.
-
a Dynamic bayesian network approach to figure tracking using learned Dynamic Models
International Conference on Computer Vision, 1999Co-Authors: Vladimir Pavlovic, James M Rehg, Tatjen Cham, Kevin MurphyAbstract:The human figure exhibits complex and rich Dynamic behavior that is both nonlinear and time-varying. However most work on tracking and synthesizing figure motion has employed either simple, generic Dynamic Models or highly specific hand-tailored ones. Recently, a broad class of learning and inference algorithms for time-series Models have been successfully cast in the framework of Dynamic Bayesian networks (DBNs). This paper describes a novel DBN-based switching linear Dynamic system (SLDS) model and presents its application to figure motion analysis. A key feature of our approach is an approximate Viterbi inference technique for overcoming the intractability of exact inference in mixed-state DBNs. We present experimental results for learning figure Dynamics from video data and show promising initial results for tracking, interpolation, synthesis, and classification using learned Models.