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two minor paper changes #1

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Apr 6, 2021
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two minor paper changes #1

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8 changes: 4 additions & 4 deletions paper/paper.md
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Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down