Flexible models for spike count data with both over- and under- dispersion

2016 ◽  
Vol 41 (1) ◽  
pp. 29-43 ◽  
Author(s):  
Ian H. Stevenson
Keyword(s):  
Count Data ◽  
Spike Count ◽  
Flexible Models

scholarly journals Flexible models for overdispersed and underdispersed count data

2021 ◽  
Author(s):  
Dexter Cahoy ◽  
Elvira Di Nardo ◽  
Federico Polito
Keyword(s):  
Poisson Distribution ◽  
Count Data ◽  
Hypergeometric Functions ◽  
Natural Generalization ◽  
Model Parameters ◽  
Probability Models ◽  
Limiting Behavior ◽  
Poisson Models ◽  
Special Cases ◽  
Flexible Models

AbstractWithin the framework of probability models for overdispersed count data, we propose the generalized fractional Poisson distribution (gfPd), which is a natural generalization of the fractional Poisson distribution (fPd), and the standard Poisson distribution. We derive some properties of gfPd and more specifically we study moments, limiting behavior and other features of fPd. The skewness suggests that fPd can be left-skewed, right-skewed or symmetric; this makes the model flexible and appealing in practice. We apply the model to real big count data and estimate the model parameters using maximum likelihood. Then, we turn to the very general class of weighted Poisson distributions (WPD’s) to allow both overdispersion and underdispersion. Similarly to Kemp’s generalized hypergeometric probability distribution, which is based on hypergeometric functions, we analyze a class of WPD’s related to a generalization of Mittag–Leffler functions. The proposed class of distributions includes the well-known COM-Poisson and the hyper-Poisson models. We characterize conditions on the parameters allowing for overdispersion and underdispersion, and analyze two special cases of interest which have not yet appeared in the literature.

scholarly journals Adjusted regularization in latent graphical models: Application to multiple-neuron spike count data

2018 ◽  
Vol 12 (2) ◽  
pp. 1068-1095 ◽  
Author(s):  
Giuseppe Vinci ◽  
Valérie Ventura ◽  
Matthew A. Smith ◽  
Robert E. Kass
Keyword(s):  
Graphical Models ◽  
Count Data ◽  
Spike Count ◽  
Neuron Spike

Using Tweedie distributions for fitting spike count data

2014 ◽  
Vol 225 ◽  
pp. 13-28 ◽  
Author(s):  
Dina Moshitch ◽  
Israel Nelken
Keyword(s):  
Count Data ◽  
Spike Count

scholarly journals Analysis of Multineuron Activity Using the Kernel Method

2007 ◽  
Vol 19 (4) ◽  
pp. 364-368
Author(s):  
Masaki Nomura ◽  
◽  
Yoshio Sakurai ◽  
Toshio Aoyagi ◽  
◽  
...  
Keyword(s):  
Count Data ◽  
Time Series Data ◽  
Kernel Method ◽  
Principal Component ◽  
Spike Count ◽  
Series Data ◽  
Neuron Activity ◽  
Activity Data ◽  
Multineuron Activity ◽  
Single Neuron Activity

We recorded multineuron spike time-series data from rat hippocampus region CA1 during a conditional discrimination task. We separated out individual single-neuron activity from multineuron activity data and prepared spike count data and calculated a kernel matrix using a Spikernel function, then applied k-means clustering and principal component analysis (PCA). Comparing spike count data to an appropriate time, we divided data into clusters and found the correspondence between the obtained cluster and rat activity. We discuss information expression in nervous-system activity expected from the kernel function.

Regression Analysis of Count Data

1998 ◽  
Author(s):  
A. Colin Cameron ◽  
Pravin K. Trivedi
Keyword(s):  
Regression Analysis ◽  
Count Data

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

Exploring Attendance at a Traditional Cultural Event: The Case of a Holy Week Celebration

2020 ◽  
Vol 24 (1) ◽  
pp. 153-168
Author(s):  
Víctor Lafuente ◽  
José Ángel Sanz ◽  
María Devesa
Keyword(s):  
Cultural Capital ◽  
Cultural Heritage ◽  
Count Data ◽  
Data Model ◽  
Social Aspects ◽  
Social Nature ◽  
The World ◽  
Local Residents ◽  
The City ◽  
Complex Expression

Holy Week is one of the most important traditions in many parts of the world and a complex expression of cultural heritage. The main goal of this article is to explore which factors determine participation in Holy Week celebrations in the city of Palencia (Spain), measured through the number of processions attended. For this purpose, an econometric count data model is used. Variables included in the model not only reflect participants' sociodemographic features but other factors reflecting cultural capital, accumulated experience, and social aspects of the event. A distinction is drawn between three types of participants: brotherhood members, local residents, and visitors, among whom a survey was conducted to collect the information required. A total of 248 surveys were carried out among brotherhood members, 209 among local residents, and 259 among visitors. The results confirm the religious and social nature of this event, especially in the case of local participants. However, in the case of visitors, participation also depends on aspects reflecting the celebration's cultural and tourist dimension—such as visiting other religious and cultural attractions—suggesting the existence of specific tourism linked to the event. All of this suggests the need to manage the event, ensuring a balance is struck between the various stakeholders' interests and developing a tourist strategy that prioritizes public-private cooperation.

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