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

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 discr

SUBMITTER: Rogerson LE 

PROVIDER: S-EPMC6693774 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature