Updated paper.md

paper/paper.md

@@ -2,20 +2,22 @@
 title: 'Pyret: A Python package for analysis of neurophysiology data'
 tags:
 	- neuroscience
+	- sensory
 	- retina
 authors:
 	- name: Benjamin Naecker
 	  orcid: 0000-0002-7525-1635
-  	  affiliation: 1
+  	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
+  	index: 1
 	- name: Department of Applied Physics, Stanford University
 	  index: 2
 	- name: Department of Neurobiology, Stanford University
@@ -26,13 +28,11 @@ bibliography: paper.bib
 
 # Summary
 
-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 [@Schwartz2006]; estimating linear-nonlinear cascade models 
-[@Chichilnisky2001,@Pedregosa2011] to predict neural responses to different stimuli; 
-as well as a suite of visualization tools for all the above.
+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.
+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/)
 
 # References