Bayesian nonparametric analysis of neuronal intensity rates

Kottas, Athanasios; Behseta, Sam; Moorman, David E.; Poynor, Valerie; Olson, Carl R.

doi:10.1016/j.jneumeth.2011.09.017

Cited by 13 publications

(11 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar analyses were performed in the frequency domain by using coherence analysis of neuron pairs using Fourier-transformed neural activity ). There are also a number of multivariate analysis techniques for the investigation of simultaneously recorded populations of neurons (Chapin 1999;Nicolelis 1999;Grün et al 2002;Pillow et al 2008;Harrison et al 2013;Brillinger 1988;Brown et al 2004;Kass et al 2005;West 2007;Rigat et al 2006;Patnaik et al 2008;Diekman et al 2009;Sastry and Unnikrishnan 2010;Kottas et al 2012). There are also a number of multivariate analysis techniques for the investigation of simultaneously recorded populations of neurons (Chapin 1999;Nicolelis 1999;Grün et al 2002;Pillow et al 2008;Harrison et al 2013;Brillinger 1988;Brown et al 2004;Kass et al 2005;West 2007;Rigat et al 2006;Patnaik et al 2008;Diekman et al 2009;Sastry and Unnikrishnan 2010;Kottas et al 2012).…”

Section: Discussionmentioning

confidence: 99%

Nonparametric Bayesian Inference in Biostatistics

Mitra

Müeller²

2015

View full text Add to dashboard Cite

We briefly review some of the nonparametric Bayesians models that are most widely used in biostatistics and bioinformatics. We define the Dirichlet process, Dirichlet process mixtures, the Polya tree, the dependent Dirichlet process and the Gaussian process prior. These few models and variations cover a major part of the models that are used in the literature. The discussion includes references to variations of the basic models that are defined in the chapters of this volume.

show abstract

Section: Discussionmentioning

confidence: 99%

Nonparametric Bayesian Inference in Biostatistics

Mitra

Müeller²

2015

View full text Add to dashboard Cite

show abstract

“…, n and f is an unknown function that maps x into y. Several nonparametric methods have been developed to infer f from noisy data including kernel smoothers, basis function expansions and splines (e.g., Dension et al 2002;Ramsay and Dalzell 1991;Hastie et al 2009), neural networks (Neal 1996), and Bayesian nonparametric mixture models (e.g., Rodriguez and Horst 2008;DeIorio et al 2009;Kottas and Krnjajić 2009;Fronczyk and Kottas 2010;Kottas et al 2012). Another useful nonparametric model for functional data is the Gaussian process model that was developed in a regression context by O'Hagan (1978).…”

Section: Introductionmentioning

confidence: 99%

Combining functional data with hierarchical Gaussian process models

Poynor

Munch

2017

Environ Ecol Stat

Self Cite

View full text Add to dashboard Cite

Gaussian process models have been used in applications ranging from machine learning to fisheries management. In the Bayesian framework, the Gaussian process is used as a prior for unknown functions, allowing the data to drive the relationship between inputs and outputs. In our research, we consider a scenario in which response and input data are available from several similar, but not necessarily identical, sources. When little information is known about one or more of the populations it may be advantageous to model all populations together. We present a hierarchical Gaussian process model with a structure that allows distinct features for each source as well as shared underlying characteristics. Key features and properties of the model are discussed and demonstrated in a number of simulation examples. The model is then applied to a data set consisting of three populations of Rotifer Brachionus calyciflorus Pallas. Specifically, we model the log growth rate of the populations using a combination of lagged population sizes. The various lag combinations are formally compared to obtain the best model inputs. We then formally compare the leading hierarchical Gaussian process model with the inferential results obtained under the independent Gaussian process model.

show abstract

“…However, to capture complex behavior of neural networks, many new statistical methods have been recently proposed for analyzing several simultaneously recoded neurons in order to extract information contained in their temporal interactions under different experimental conditions. For more discussion on analysis of spike trains, refer to Harrison et al (2013); Brillinger (1988); Brown et al (2004); Kass et al (2005); West (2007); Reich et al (1998); Barbieri et al (2001); Kass and Ventura (2001); Grün et al (2002); Kass et al (2005); Rigat et al (2006); Patnaik et al (2008); Pillow et al (2008); Jacobs et al (2009); Diekman et al (2009); Sastry and Unnikrishnan (2010); Kottas et al (2012); Kelly and Kass (2012); Shahbaba et al (2014).…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making

Zhou

Moorman

Behseta

et al. 2016

Journal of the American Statistical Association

Self Cite

View full text Add to dashboard Cite

The goal of this paper is to develop a novel statistical model for studying cross-neuronal spike train interactions during decision making. For an individual to successfully complete the task of decision-making, a number of temporally-organized events must occur: stimuli must be detected, potential outcomes must be evaluated, behaviors must be executed or inhibited, and outcomes (such as reward or no-reward) must be experienced. Due to the complexity of this process, it is likely the case that decision-making is encoded by the temporally-precise interactions between large populations of neurons. Most existing statistical models, however, are inadequate for analyzing such a phenomenon because they provide only an aggregated measure of interactions over time. To address this considerable limitation, we propose a dynamic Bayesian model which captures the time-varying nature of neuronal activity (such as the time-varying strength of the interactions between neurons). The proposed method yielded results that reveal new insight into the dynamic nature of population coding in the prefrontal cortex during decision making. In our analysis, we note that while some neurons in the prefrontal cortex do not synchronize their firing activity until the presence of a reward, a different set of neurons synchronize their activity shortly after stimulus onset. These differentially synchronizing sub-populations of neurons suggests a continuum of population representation of the reward-seeking task. Secondly, our analyses also suggest that the degree of synchronization differs between the rewarded and non-rewarded conditions. Moreover, the proposed model is scalable to handle data on many simultaneously-recorded neurons and is applicable to analyzing other types of multivariate time series data with latent structure. Supplementary materials (including computer codes) for our paper are available online.

show abstract

Bayesian nonparametric analysis of neuronal intensity rates

Cited by 13 publications

References 74 publications

Nonparametric Bayesian Inference in Biostatistics

Nonparametric Bayesian Inference in Biostatistics

Combining functional data with hierarchical Gaussian process models

A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making

Contact Info

Product

Resources

About