ArticleJournal of computational neuroscience2016
Flexible models for spike count data with both over- and under- dispersion.
Article in Journal of computational neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Spike count analysis for multiplexing inference (SCAMPI).Journal of computational neuroscience · 2026Article
- Continuous partitioning of neuronal variability.bioRxiv : the preprint server for biology · 2025Article
- Improved inference of latent neural states from calcium imaging data.bioRxiv : the preprint server for biology · 2025Article
- A doubly stochastic renewal framework for partitioning spiking variability.Nature communications · 2025Article
- Article
- Calibrating Bayesian Decoders of Neural Spiking Activity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Efficient Decoding of Large-Scale Neural Population Responses With Gaussian-Process Multiclass Regression.Neural computation · 2024Article
- Article
- Article
- Neuronal variability reflects probabilistic inference tuned to natural image statistics.Nature communications · 2021Article
- Variability and Randomness of the Instantaneous Firing Rate.Frontiers in computational neuroscience · 2021Article
- Fano Factor: A Potentially Useful Information.Frontiers in computational neuroscience · 2020Article
- Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models.Neurons, behavior, data analysis, and theory · 2019Article
- Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity.Journal of neurophysiology · 2018Article
- Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability.Neural computation · 2018Article
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1 author.
Funding
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.
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