The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Jonathan W Pillow - One of the best experts on this subject based on the ideXlab platform.
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bayesian spike triggered Covariance Analysis
Neural Information Processing Systems, 2011Co-Authors: Il Memming Park, Jonathan W PillowAbstract:Neurons typically respond to a restricted number of stimulus features within the high-dimensional space of natural stimuli. Here we describe an explicit model-based interpretation of traditional estimators for a neuron's multi-dimensional feature space, which allows for several important generalizations and extensions. First, we show that traditional estimators based on the spike-triggered average (STA) and spike-triggered Covariance (STC) can be formalized in terms of the "expected log-likelihood" of a Linear-Nonlinear-Poisson (LNP) model with Gaussian stimuli. This model-based formulation allows us to define maximum-likelihood and Bayesian estimators that are statistically consistent and efficient in a wider variety of settings, such as with naturalistic (non-Gaussian) stimuli. It also allows us to employ Bayesian methods for regularization, smoothing, sparsification, and model comparison, and provides Bayesian confidence intervals on model parameters. We describe an empirical Bayes method for selecting the number of features, and extend the model to accommodate an arbitrary elliptical nonlinear response function, which results in a more powerful and more flexible model for feature space inference. We validate these methods using neural data recorded extracellularly from macaque primary visual cortex.
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dimensionality reduction in neural models an information theoretic generalization of spike triggered average and Covariance Analysis
Journal of Vision, 2006Co-Authors: Jonathan W Pillow, Eero P SimoncelliAbstract:We describe an information-theoretic framework for fitting neural spike responses with a Linear-Nonlinear-Poisson cascade model. This framework unifies the spike-triggered average and spike-triggered Covariance approaches to neural characterization, and recovers a set of linear filters that maximize mean and variance-dependent information between stimuli and spike responses. The resulting approach has several useful properties: (1) it recovers a set of linear filters sorted according to their informativeness about the neural response; (2) it is both computationally efficient and robust, allowing recovery of multiple linear filters from a data set of relatively modest size; (3) it provides an explicit “default” model of the nonlinear stage mapping the filter responses to spike rate, in the form of a ratio of Gaussians. (4) it is equivalent to maximum likelihood estimation of this default model, but also converges to the correct filter estimates whenever the conditions for the consistency of spike-triggered average or Covariance Analysis are met; (5) it can be augmented with additional constraints, such as space-time separability, on the filters. We demonstrate the effectiveness of the method by applying it to simulated responses of a Hodgkin-Huxley neuron, and the recorded extracellular responses of macaque retinal ganglion cells and V1 cells.
Il Memming Park - One of the best experts on this subject based on the ideXlab platform.
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bayesian spike triggered Covariance Analysis
Neural Information Processing Systems, 2011Co-Authors: Il Memming Park, Jonathan W PillowAbstract:Neurons typically respond to a restricted number of stimulus features within the high-dimensional space of natural stimuli. Here we describe an explicit model-based interpretation of traditional estimators for a neuron's multi-dimensional feature space, which allows for several important generalizations and extensions. First, we show that traditional estimators based on the spike-triggered average (STA) and spike-triggered Covariance (STC) can be formalized in terms of the "expected log-likelihood" of a Linear-Nonlinear-Poisson (LNP) model with Gaussian stimuli. This model-based formulation allows us to define maximum-likelihood and Bayesian estimators that are statistically consistent and efficient in a wider variety of settings, such as with naturalistic (non-Gaussian) stimuli. It also allows us to employ Bayesian methods for regularization, smoothing, sparsification, and model comparison, and provides Bayesian confidence intervals on model parameters. We describe an empirical Bayes method for selecting the number of features, and extend the model to accommodate an arbitrary elliptical nonlinear response function, which results in a more powerful and more flexible model for feature space inference. We validate these methods using neural data recorded extracellularly from macaque primary visual cortex.
Ingrid Amara - One of the best experts on this subject based on the ideXlab platform.
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issues for Covariance Analysis of dichotomous and ordered categorical data from randomized clinical trials and non parametric strategies for addressing them
Statistics in Medicine, 1998Co-Authors: Gary G Koch, Catherine M Tangen, Jin Whan Jung, Ingrid AmaraAbstract:Analysis of Covariance is an effective method for addressing two considerations for randomized clinical trials. One is reduction of variance for estimates of treatment effects and thereby the production of narrower confidence intervals and more powerful statistical tests. The other is the clarification of the magnitude of treatment effects through adjustment of corresponding estimates for any random imbalances between the treatment groups with respect to the covariables. The statistical basis of Covariance Analysis can be either non-parametric, with reliance only on the randomization in the study design, or parametric through a statistical model for a postulated sampling process. For non-parametric methods, there are no formal assumptions for how a response variable is related to the covariables, but strong correlation between response and covariables is necessary for variance reduction. Computations for these methods are straightforward through the application of weighted least squares to fit linear models to the differences between treatment groups for the means of the response variable and the covariables jointly with a specification that has null values for the differences that correspond to the covariables. Moreover, such Analysis is similarly applicable to dichotomous indicators, ranks or integers for ordered categories, and continuous measurements. Since non-parametric Covariance Analysis can have many forms, the ones which are planned for a clinical trial need careful specification in its protocol. A limitation of non-parametric Analysis is that it does not directly address the magnitude of treatment effects within subgroups based on the covariables or the homogeneity of such effects. For this purpose, a statistical model is needed. When the response criterion is dichotomous or has ordered categories, such a model may have a non-linear nature which determines how Covariance adjustment modifies results for treatment effects. Insight concerning such modifications can be gained through their evaluation relative to non-parametric counterparts. Such evaluation usually indicates that alternative ways to compare treatments for a response criterion with adjustment for a set of covariables mutually support the same conclusion about the strength of treatment effects. This robustness is noteworthy since the alternative methods for Covariance Analysis have substantially different rationales and assumptions. Since findings can differ in important ways across alternative choices for covariables (as opposed to methods for Covariance adjustment), the critical consideration for studies with Covariance analyses planned as the primary method for comparing treatments is the specification of the covariables in the protocol (or in an amendment or formal plan prior to any unmasking of the study.
Daniel R. Weinberger - One of the best experts on this subject based on the ideXlab platform.
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Regional cerebral blood flow during the wisconsin card sorting test in normal subjects studied by xenon-133 dynamic SPECT: Comparison of absolute values, percent distribution values and Covariance Analysis
Psychiatry Research: Neuroimaging, 1993Co-Authors: Stefano Marenco, Jeffrey R. Zigun, Riccardo Coppola, David G Daniel, Daniel R. WeinbergerAbstract:We studied regional cerebral blood flow (rCBF) by xenon-133 dynamic single photon emission computed tomography (SPECT) in 17 normal volunteers who were performing the Wisconsin Card Sorting Test (WCST), a task that is particularly sensitive to disturbance of the prefrontal cortex, and a simple matching-to-sample task (BAR) as a sensorimotor control. Three methods for statistical Analysis of regional "subtraction" data were used: absolute rCBF values, percent distribution values and means adjusted for global CBF changes (Covariance Analysis). The absolute values had high variance, due to the combination of interindividual differences in global flow and intra-individual variation, and showed no statistically significant regional changes. This variation was greatly reduced by percent values and Covariance Analysis, which had quite similar outcomes. With both methods, significant increases of rCBF during the WCST as compared with the BAR were seen in the right anterior dorsolateral prefrontal and left occipital cortices, and reduction of rCBF in the left pararolandic region. Moreover, significant correlations with performance were found in the medial regions of the frontal lobes, with opposite trends for the right and left hemisphere. The posterior dorsolateral prefrontal region showed a negative correlation with sensory-motor frequency, an index related to the task's difficulty. These results are consistent with previous findings using other rCBF techniques and confirm the statistical advantage of normalization and Covariance methods, which yield practically identical results, at least in this Analysis based on regions of interest. © 1993.
Martin Karplus - One of the best experts on this subject based on the ideXlab platform.
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Collective motions in proteins: A Covariance Analysis of atomic fluctuations in molecular dynamics and normal mode simulations
Proteins: Structure Function and Bioinformatics, 1991Co-Authors: Toshiko Ichiye, Martin KarplusAbstract:A method is described for identifying collective motions in proteins from molecular dynamics trajectories or normal mode simulations. The method makes use of the Covariances of atomic positional fluctuations. It is illustrated by an Analysis of the bovine pancreatic trypsin inhibitor. Comparison of the Covariance and cross-correlation matrices shows that the relative motions have many similar features in the different simulations. Many regions of the protein, especially regions of secondary structure, move in a correlated manner. Anharmonic effects, which are included in the molecular dynamics simulations but not in the normal Analysis, are of some importance in determining the larger scale collective motions, but not the more local fluctuations. Comparisons of molecular dynamics simulations in the present and absence of solvent indicate that the environment is of significance for the long-range motions.
