Journal of Computational Neuroscience

, Volume 41, Issue 1, pp 29–43

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

Article

DOI: 10.1007/s10827-016-0603-y

Cite this article as:
Stevenson, I.H. J Comput Neurosci (2016) 41: 29. doi:10.1007/s10827-016-0603-y

Abstract

A key observation in systems neuroscience is that neural responses vary, even in controlled settings where stimuli are held constant. Many statistical models assume that trial-to-trial spike count variability is Poisson, but there is considerable evidence that neurons can be substantially more or less variable than Poisson depending on the stimuli, attentional state, and brain area. Here we examine a set of spike count models based on the Conway-Maxwell-Poisson (COM-Poisson) distribution that can flexibly account for both over- and under-dispersion in spike count data. We illustrate applications of this noise model for Bayesian estimation of tuning curves and peri-stimulus time histograms. We find that COM-Poisson models with group/observation-level dispersion, where spike count variability is a function of time or stimulus, produce more accurate descriptions of spike counts compared to Poisson models as well as negative-binomial models often used as alternatives. Since dispersion is one determinant of parameter standard errors, COM-Poisson models are also likely to yield more accurate model comparison. More generally, these methods provide a useful, model-based framework for inferring both the mean and variability of neural responses.

Keywords

Spike count variability Tuning curves Poisson Conway-Maxwell-Poisson 

Supplementary material

10827_2016_603_MOESM1_ESM.docx (30 kb)
ESM 1(DOCX 30 kb)

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Psychological SciencesUniversity of ConnecticutStorrsUSA
  2. 2.Department of Biomedical EngineeringUniversity of ConnecticutStorrsUSA
  3. 3.Connecticut Institute for Brain and Cognitive ScienceStorrsUSA

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