ArticleNeural computation2018
Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability.
Article in Neural computation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Partitioning Neural Co-Variability.ArXiv · 2026Article
- 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
- Global and local origins of trial-to-trial spike count variability in visual cortex.bioRxiv : the preprint server for biology · 2025Article
- Sparse-Coding Variational Autoencoders.Neural computation · 2024Article
- Thalamocortical interactions shape hierarchical neural variability during stimulus perception.iScience · 2024Article
- A unified framework for perceived magnitude and discriminability of sensory stimuli.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Response sub-additivity and variability quenching in visual cortex.Nature reviews. Neuroscience · 2024Review
- What does the mean mean? A simple test for neuroscience.PLoS computational biology · 2024Article
- Trial-by-trial variability in cortical responses exhibits scaling of spatial correlations predicted from critical dynamics.Cell reports · 2024Article
- Efficient Decoding of Large-Scale Neural Population Responses With Gaussian-Process Multiclass Regression.Neural computation · 2024Article
- Activation of M1 cholinergic receptors in mouse somatosensory cortex enhances information processing and detection behaviour.Communications biology · 2024Article
- Transformations of sensory information in the brain suggest changing criteria for optimality.PLoS computational biology · 2024Article
- Unsupervised approach to decomposing neural tuning variability.Nature communications · 2023Article
- Review of data processing of functional optical microscopy for neuroscience.Neurophotonics · 2022Review
- Trial-to-Trial Variability of Spiking Delay Activity in Prefrontal Cortex Constrains Burst-Coding Models of Working Memory.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Stochastic Simulations as a Tool for Assessing Signal Fidelity in Gene Expression in Synthetic Promoter Design.Biology · 2021Article
- Neuronal variability reflects probabilistic inference tuned to natural image statistics.Nature communications · 2021Article
- Fano Factor: A Potentially Useful Information.Frontiers in computational neuroscience · 2020Article
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5 authors.
Funding
Abstract
Neurons in many brain areas exhibit high trial-to-trial variability, with spike counts that are overdispersed relative to a Poisson distribution. Recent work (Goris, Movshon, & Simoncelli, 2014 ) has proposed to explain this variability in terms of a multiplicative interaction between a stochastic gain variable and a stimulus-dependent Poisson firing rate, which produces quadratic relationships between spike count mean and variance. Here we examine this quadratic assumption and propose a more flexible family of models that can account for a more diverse set of mean-variance relationships. Our model contains additive gaussian noise that is transformed nonlinearly to produce a Poisson spike rate. Different choices of the nonlinear function can give rise to qualitatively different mean-variance relationships, ranging from sublinear to linear to quadratic. Intriguingly, a rectified squaring nonlinearity produces a linear mean-variance function, corresponding to responses with a constant Fano factor. We describe a computationally efficient method for fitting this model to data and demonstrate that a majority of neurons in a V1 population are better described by a model with a nonquadratic relationship between mean and variance. Finally, we demonstrate a practical use of our model via an application to Bayesian adaptive stimulus selection in closed-loop neurophysiology experiments, which shows that accounting for overdispersion can lead to dramatic improvements in adaptive tuning curve estimation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.