ArticlePLoS computational biology2019
Bayesian hypothesis testing and experimental design for two-photon imaging data.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- A presynaptic source drives differing levels of surround suppression in two mouse retinal ganglion cell types.Nature communications · 2024Article
- Center-surround interactions underlie bipolar cell motion sensitivity in the mouse retina.Nature communications · 2022Article
- A deep-learning approach for online cell identification and trace extraction in functional two-photon calcium imaging.Nature communications · 2022Article
- Correction: Bayesian hypothesis testing and experimental design for two-photon imaging data.PLoS computational biology · 2019Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
Variability, stochastic or otherwise, is a central feature of neural activity. Yet the means by which estimates of variation and uncertainty are derived from noisy observations of neural activity is often heuristic, with more weight given to numerical convenience than statistical rigour. For two-photon imaging data, composed of fundamentally probabilistic streams of photon detections, the problem is particularly acute. Here, we present a statistical pipeline for the inference and analysis of neural activity using Gaussian Process regression, applied to two-photon recordings of light-driven activity in ex vivo mouse retina. We demonstrate the flexibility and extensibility of these models, considering cases with non-stationary statistics, driven by complex parametric stimuli, in signal discrimination, hierarchical clustering and other inference tasks. Sparse approximation methods allow these models to be fitted rapidly, permitting them to actively guide the design of light stimulation in the midst of ongoing two-photon experiments.
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Registered trials
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.