Surya Tapas Tokdar

Journal Article Trends in cognitive sciences · July 2024 Our ability to perceive multiple objects is mysterious. Sensory neurons are broadly tuned, producing potential overlap in the populations of neurons activated by each object in a scene. This overlap raises questions about how distinct information is retain ... Full text Cite

Multiple objects evoke fluctuating responses in several regions of the visual pathway.

Journal Article eLife · March 2024 How neural representations preserve information about multiple stimuli is mysterious. Because tuning of individual neurons is coarse (e.g., visual receptive field diameters can exceed perceptual resolution), the populations of neurons potentially responsiv ... Full text Cite

Heavy-Tailed Density Estimation

Journal Article Journal of the American Statistical Association · January 1, 2024 A novel statistical method is proposed and investigated for estimating a heavy tailed density under mild smoothness assumptions. Statistical analyses of heavy-tailed distributions are susceptible to the problem of sparse information in the tail of the dist ... Full text Open Access Cite

Multiple objects evoke fluctuating responses in several regions of the visual pathway.

Preprint · July 19, 2023 Full text Link to item Cite

Coordinated multiplexing of information about separate objects in visual cortex.

Journal Article eLife · November 2022 Sensory receptive fields are large enough that they can contain more than one perceptible stimulus. How, then, can the brain encode information about each of the stimuli that may be present at a given moment? We recently showed that when more than o ... Full text Open Access Cite

Variable selection consistency of Gaussian process regression

Journal Article Annals of Statistics · October 1, 2021 Bayesian nonparametric regression under a rescaled Gaussian process prior offers smoothness-adaptive function estimation with near minimax-optimal error rates. Hierarchical extensions of this approach, equipped with stochastic variable selection, are known ... Full text Cite

Joint quantile regression for spatial data

Journal Article Journal of the Royal Statistical Society. Series B: Statistical Methodology · September 1, 2021 Linear quantile regression is a powerful tool to investigate how predictors may affect a response heterogeneously across different quantile levels. Unfortunately, existing approaches find it extremely difficult to adjust for any dependency between observat ... Full text Cite

ANALYZING SECOND ORDER STOCHASTICITY OF NEURAL SPIKING UNDER STIMULI-BUNDLE EXPOSURE.

Journal Article The annals of applied statistics · March 2021 Conventional analysis of neuroscience data involves computing average neural activity over a group of trials and/or a period of time. This approach may be particularly problematic when assessing the response patterns of neurons to more than one simultaneou ... Full text Cite

Sensitivity and specificity of a Bayesian single trial analysis for time varying neural signals.

Journal Article Neurons, behavior, data analysis, and theory · January 2020 We recently reported the existence of fluctuations in neural signals that may permit neurons to code multiple simultaneous stimuli sequentially across time [1]. This required deploying a novel statistical approach to permit investigation of neural activity ... Cite

Coordinated multiplexing of information about separate objects in visual cortex

Journal Article · September 23, 2019 AbstractSensory receptive fields are large enough that they can contain more than one perceptible stimulus. How, then, can the brain encode information abouteachof the stimuli that may be present ... Full text Cite

Bayesian analysis of dynamic linear topic models

Journal Article Bayesian Analysis · January 1, 2019 Discovering temporal evolution of themes from a time-stamped collection of text poses a challenging statistical learning problem. Dynamic topic models offer a probabilistic modeling framework to decompose a corpus of text documents into "topics", i.e., pro ... Full text Cite

A vignette on model-based quantile regression: Analysing excess zero response

Chapter · January 1, 2019 Quantile regression is widely seen as an ideal tool to understand complex predictor-response relations. Its biggest promise rests in its ability to quantify whether and how predictor effects vary across response quantile levels. But this promise has not be ... Full text Cite

Single neurons may encode simultaneous stimuli by switching between activity patterns.

Journal Article Nature communications · July 2018 How the brain preserves information about multiple simultaneous items is poorly understood. We report that single neurons can represent multiple stimuli by interleaving signals across time. We record single units in an auditory region, the inferior collicu ... Full text Open Access Cite

Sensorimotor abilities predict on-field performance in professional baseball.

Journal Article Sci Rep · January 8, 2018 Baseball players must be able to see and react in an instant, yet it is hotly debated whether superior performance is associated with superior sensorimotor abilities. In this study, we compare sensorimotor abilities, measured through 8 psychomotor tasks co ... Full text Open Access Link to item Cite

Visual abilities distinguish pitchers from hitters in professional baseball.

Journal Article J Sports Sci · January 2018 This study aimed to evaluate the possibility that differences in sensorimotor abilities exist between hitters and pitchers in a large cohort of baseball players of varying levels of experience. Secondary data analysis was performed on 9 sensorimotor tasks ... Full text Open Access Link to item Cite

Joint estimation of quantile planes over arbitrary predictor spaces

Journal Article Journal of the American Statistical Association · September 15, 2017 Featured Publication In spite of the recent surge of interest in quantile regression, joint estimation of linear quantile planes remains a great challenge in statistics and econometrics. We propose a novel parametrization that characterizes any collection of non-crossing quant ... Full text Link to item Cite

Evidence for time division multiplexing: Single neurons may encode simultaneous stimuli by switching between activity patterns

Journal Article · February 9, 2017 ABSTRACT How the brain preserves information about multiple simultaneous items is poorly understood. We report that single neurons can represent multiple different stimuli by interleaving different signals across time. We record single units in an auditory ... Full text Cite

Computer emulation with non-stationary Gaussian processes

Journal Article SIAM/ASA Journal of Uncertainty Quantification · 2016 Gaussian process (GP) models are widely used to emulate propagation uncertainty in computer experiments. GP emulation sits comfortably within an analytically tractable Bayesian framework. Apart from propagating uncertainty of the input variables, a GP emul ... Link to item Cite

Minimax-optimal nonparametric regression in high dimensions

Journal Article Annals of Statistics · 2015 Featured Publication © Institute of Mathematical Statistics, 2015.Minimax L2 risks for high-dimensional nonparametric regression are derived under two sparsity assumptions: (1) the true regression surface is a sparse function that depends only on d = O(log n) important predict ... Full text Cite

Spike Count Analysis for MultiPlexing Inference (SCAMPI).

Preprint · September 15, 2024 Full text Link to item Cite

Signal switching may enhance processing power of the brain.

Multiple objects evoke fluctuating responses in several regions of the visual pathway.

Heavy-Tailed Density Estimation

Multiple objects evoke fluctuating responses in several regions of the visual pathway.

Preprint · July 19, 2023 Full text Link to item Cite

Coordinated multiplexing of information about separate objects in visual cortex.

Variable selection consistency of Gaussian process regression

Joint quantile regression for spatial data

ANALYZING SECOND ORDER STOCHASTICITY OF NEURAL SPIKING UNDER STIMULI-BUNDLE EXPOSURE.

Sensitivity and specificity of a Bayesian single trial analysis for time varying neural signals.

Coordinated multiplexing of information about separate objects in visual cortex

Bayesian analysis of dynamic linear topic models

A vignette on model-based quantile regression: Analysing excess zero response

Single neurons may encode simultaneous stimuli by switching between activity patterns.

Sensorimotor abilities predict on-field performance in professional baseball.

Visual abilities distinguish pitchers from hitters in professional baseball.

Joint estimation of quantile planes over arbitrary predictor spaces

Evidence for time division multiplexing: Single neurons may encode simultaneous stimuli by switching between activity patterns

Computer emulation with non-stationary Gaussian processes

Minimax-optimal nonparametric regression in high dimensions

Posterior consistency in conditional distribution estimation.

Journal Article Journal of multivariate analysis · April 2013 A wide variety of priors have been proposed for nonparametric Bayesian estimation of conditional distributions, and there is a clear need for theorems providing conditions on the prior for large support, as well as posterior consistency. Estimation of an u ... Full text Cite

Efficient Gaussian process regression for large datasets

Journal Article Biometrika · 2013 Gaussian processes are widely used in nonparametric regression, classification and spatiotemporal modelling, facilitated in part by a rich literature on their theoretical properties. However, one of their practical limitations is expensive computation, typ ... Full text Open Access Cite

Adaptive Bayesian multivariate density estimation with Dirichlet mixtures

Journal Article Biometrika · 2013 Featured Publication We show that rate-adaptive multivariate density estimation can be performed using Bayesian methods based on Dirichlet mixtures of normal kernels with a prior distribution on the kernel's covariance matrix parameter. We derive sufficient conditions on the p ... Full text Cite

Bayesian latent factor regression for functional and longitudinal data.

Journal Article Biometrics · December 2012 In studies involving functional data, it is commonly of interest to model the impact of predictors on the distribution of the curves, allowing flexible effects on not only the mean curve but also the distribution about the mean. Characterizing the curve fo ... Full text Cite

A nonparametric empirical Bayes framework for large-scale multiple testing

Journal Article Biostatistics · 2012 Featured Publication We propose a flexible and identifiable version of the 2-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the nonnull cases. We use a computationally efficient predictive re ... Full text Cite

Simultaneous linear quantile regression: A semiparametric bayesian approach

Journal Article Bayesian Analysis · 2012 Featured Publication We introduce a semi-parametric Bayesian framework for a simultaneous analysis of linear quantile regression models. A simultaneous analysis is essential to attain the true potential of the quantile regression framework, but is computa-tionally challenging ... Full text Cite

Impact of beliefs about atlantic tropical cyclone detection on conclusions about trends in tropical cyclone numbers

Journal Article Bayesian Analysis · December 1, 2011 Whether the number of tropical cyclones (TCs) has increased in the last 150 years has become a matter of intense debate. We investigate the effects of beliefs about TC detection capacities in the North Atlantic on trends in TC num-bers since the 1870s. Whi ... Full text Cite

A censored-data multiperiod inventory problem with newsvendor demand distributions

Journal Article Manufacturing and Service Operations Management · September 1, 2011 We study the stochastic multiperiod inventory problem in which demand in excess of available inventory is lost and unobserved so that demand data are censored. A Bayesian scheme is employed to dynamically update the demand distribution for the problem with ... Full text Cite

Semiparametric inference in mixture models with predictive recursion marginal likelihood

Journal Article Biometrika · September 1, 2011 Predictive recursion is an accurate and computationally efficient algorithm for nonparametric estimation of mixing densities in mixture models. In semiparametric mixture models, however, the algorithm fails to account for any uncertainty in the additional ... Full text Cite

Bayesian density regression with logistic Gaussian process and subspace projection

Journal Article Bayesian Analysis · December 1, 2010 We develop a novel Bayesian density regression model based on logistic Gaussian processes and subspace projection. Logistic Gaussian processes provide an attractive alternative to the popular stick-breaking processes for modeling a family of conditional de ... Full text Cite

Detection of bursts in extracellular spike trains using hidden semi-Markov point process models.

Journal Article Journal of computational neuroscience · August 2010 Neurons in vitro and in vivo have epochs of bursting or "up state" activity during which firing rates are dramatically elevated. Various methods of detecting bursts in extracellular spike trains have appeared in the literature, the most widely used apparen ... Full text Cite

Importance sampling: A review

Journal Article Wiley Interdisciplinary Reviews: Computational Statistics · 2010 Featured Publication We provide a short overview of importance sampling - a popular sampling tool used for Monte Carlo computing. We discuss its mathematical foundation and properties that determine its accuracy in Monte Carlo approximations.We review the fundamental developme ... Full text Cite

Consistency of a recursive estimate of mixingdistributions

Journal Article Annals of Statistics · October 1, 2009 Mixture models have received considerable attention recently and Newton [Sankhya Ser. A 64 (2002) 306-322] proposed a fast recursive algorithm for estimating a mixing distribution. We prove almost sure consistency of this recursive estimate in the weak top ... Full text Cite

Asymptotic properties of predictive recursion: Robustness and rate of convergence

Journal Article Electronic Journal of Statistics · January 1, 2009 Here we explore general asymptotic properties of Predictive Recursion (PR) for nonparametric estimation of mixing distributions. We prove that, when the mixture model is mis-specified, the estimated mixture converges almost surely in total variation to the ... Full text Cite

A comparison of the Benjamini-Hochberg procedure with some Bayesian rules for multiple testing

Journal Article · 2008 Full text Cite

On the empirical bayes approach to the problem of multiple testing

Journal Article Quality and Reliability Engineering International · October 1, 2007 We discuss the Empirical Bayes approach to the problem of multiple testing and compare it with a very popular frequentist method of Benjamini and Hochberg aimed at controlling the false discovery rate. Our main focus is the 'sparse mixture' case, when only ... Full text Cite

Towards a faster implementation of density estimation with logistic gaussian process priors

Journal Article Journal of Computational and Graphical Statistics · September 1, 2007 A novel method is proposed to compute the Bayes estimate for a logistic Gaussian process prior for density estimation. The method gains speed by drawing samples from the posterior of a finite-dimensional surrogate prior, which is obtained by imputation of ... Full text Cite

Posterior consistency of logistic Gaussian process priors in density estimation

Journal Article Journal of Statistical Planning and Inference · January 1, 2007 We establish weak and strong posterior consistency of Gaussian process priors studied by Lenk [1988. The logistic normal distribution for Bayesian, nonparametric, predictive densities. J. Amer. Statist. Assoc. 83 (402), 509-516] for density estimation. Wea ... Full text Cite

Posterior consistency of dirichlet location-scale mixture of normals in density estimation and regression

Journal Article Sankhya: The Indian Journal of Statistics · February 1, 2006 We provide sufficient conditions under which a Dirichlet location-scale mixture of normal prior achieves weak and strong posterior consistency at a true density. Our conditions involve both the prior and the true density from which observations are obtaine ... Cite

Convergence and consistency of Newton’s algorithm for estimating mixing distribution

Chapter · January 1, 2006 We provide a new convergence and consistency proof of Newton’s algorithm for estimating a mixing distribution under some rather strong conditions. An auxiliary result used in the proof shows that the Kullback Leibler divergence between the estimate and the ... Full text Cite