Bayesian hypothesis testing and experimental design for two-photon imaging data

PLoS Computational Biology
Luke Edward RogersonPhilipp Berens

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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Citations

Oct 23, 2019·PLoS Computational Biology·Luke E RogersonPhilipp Berens
Sep 24, 2019·ELife·Katrin FrankeThomas Euler

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Methods Mentioned

BETA
feature extraction
biosensor
transgenic

Software Mentioned

ScanM
Python
GP
PyMC3
custom
MultiClamp
Image
pySTAN
Scipy
Scikit

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