From e0ab0ca148bd25c68e52445a908595e4140fdcf9 Mon Sep 17 00:00:00 2001 From: "Daniel S. Katz" Date: Tue, 6 Apr 2021 15:06:11 -0500 Subject: [PATCH] two minor paper changes --- paper/paper.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index d37e5b9..a27fd31 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -22,10 +22,10 @@ affiliations: Should the need to model the relationship between bivariate data and a response variable arise, two-dimensional (2D) Gaussian models are often the most -appropriate choice. For example, Priebe et. al. characterized motion-sensitive +appropriate choice. For example, @Priebe2003 characterized motion-sensitive neurons in the brains of macaques by fitting 2D-Gaussian functions to neurons' -response rates as spatial and temporal frequencies of visual stimuli were varied -[@Priebe2003]. The width and orientation of these fitted 2D-Gaussian surfaces +response rates as spatial and temporal frequencies of visual stimuli were varied. +The width and orientation of these fitted 2D-Gaussian surfaces provides insight on whether a neuron is "tuned" to particular spatial or temporal domains. Two-dimensional Gaussians are also used in other scientific disciplines such as physics [@Wu1998; @Kravtsov2004], materials sciences @@ -82,7 +82,7 @@ choices is designed for a specific use case. The most generic method (and the default) is `method = "elliptical"`. This allows the fitted 2D-Gaussian to take an ellipsoid shape, and this will likely be the best option for most use cases. A slightly-altered method to fit an ellipsoid 2D-Gaussian is available in -`method = "elliptical_log"`. This method follows Priebe et al., [@Priebe2003] +`method = "elliptical_log"`. This method follows @Priebe2003 and is geared towards use with log2-transformed data. A third option is `method = "circular"`. This produces a very simple 2D-Gaussian that is constrained to have to have a roughly circular shape (i.e. spread in X- and Y- are roughly