Updating paper structure and references for JOSS submission. (#89)

paper/paper.md

@@ -1,27 +1,27 @@
 ---
 title: 'Pyret: A Python package for analysis of neurophysiology data'
 tags:
-	- neuroscience
-	- sensory
-	- retina
+ - neuroscience
+ - sensory
+ - retina
 authors:
-	- name: Benjamin Naecker
-	  orcid: 0000-0002-7525-1635
-  	affiliation: 1
-	- name: Niru Maheswaranathan
-    orcid: 0000-0002-3946-4705
-	  affiliation: 1
-	- name: Surya Ganguli
-	  affiliation: 2, 3
-	- name: Stephen Baccus
-	  affiliation: 3
+ - name: Benjamin Naecker
+   orcid: 0000-0002-7525-1635
+   affiliation: 1
+ - name: Niru Maheswaranathan
+   orcid: 0000-0002-3946-4705
+   affiliation: 1
+ - name: Surya Ganguli
+   affiliation: 2, 3
+ - name: Stephen Baccus
+   affiliation: 3
 affiliations:
-	- name: Neurosciences Graduate Program, Stanford University
-  	index: 1
-	- name: Department of Applied Physics, Stanford University
-	  index: 2
-	- name: Department of Neurobiology, Stanford University
-	  index: 3
+ - name: Neurosciences Graduate Program, Stanford University
+   index: 1
+ - name: Department of Applied Physics, Stanford University
+   index: 2
+ - name: Department of Neurobiology, Stanford University
+   index: 3
 date: 01 Dec 2016
 bibliography: paper.bib
 ---
@@ -30,7 +30,7 @@ bibliography: paper.bib
 
 The *pyret* package contains tools for analyzing neural electrophysiology data.
 It focuses on applications in sensory neuroscience, broadly construed as any experiment in which one would like to characterize neural responses to a sensory stimulus.
-Pyret contains methods for manipulating spike trains (e.g. binning and smoothing), pre-processing experimental stimuli (e.g. resampling), computing spike-triggered averages and ensembles [@pchwartz2006], estimating linear-nonlinear cascade models to predict neural responses to different stimuli [@phichilnisky2001], part of which follows the scikit-learn API [@pedregosa2011], as well as a suite of visualization tools for all the above.
+Pyret contains methods for manipulating spike trains (e.g. binning and smoothing), pre-processing experimental stimuli (e.g. resampling), computing spike-triggered averages and ensembles [@schwartz2006], estimating linear-nonlinear cascade models to predict neural responses to different stimuli [@chichilnisky2001], part of which follows the scikit-learn API [@pedregosa2011], as well as a suite of visualization tools for all the above.
 We designed *pyret* to be simple, robust, and efficient with broad applicability across a range of sensory neuroscience analyses.
 
 Full API documentation and a short tutorial can be found at [http://pyret.readthedocs.io/](http://pyret.readthedocs.io/)