added natbib, hyperref, and notes

main.tex

@@ -1,5 +1,17 @@
 \documentclass[a4paper, 12pt]{article}
 
+\title{Emergent intensity invariance vs. signal-to-noise ratio at three consecutive processing stages along the grasshopper song recognition pathway}
+
+\author{Jona Hartling\textsuperscript{1},
+  Ale\v{s} \v{S}korjanc\textsuperscript{2},
+  Jan Benda\textsuperscript{1,3}}
+
+\date{\normalsize
+  \textsuperscript{1} Institute for Neurobiology, Eberhard Karls Universität, 72076 Tübingen, Germany \\ 
+  \textsuperscript{2} Department of Biology, Biotechnical Faculty, University of Ljubljana, Ve\v{c}na pot 111, 1000 Ljubljana, Slovenia\\
+  \textsuperscript{3} Bernstein Center for Computational Neuroscience Tübingen, 72076 Tübingen, Germany}
+
+
 \usepackage[left=2cm,right=2cm,top=2cm,bottom=2cm,includeheadfoot]{geometry}
 % \usepackage[onehalfspacing]{setspace}
 \usepackage{graphicx}
@@ -17,30 +29,38 @@
 \addto\captionsenglish{\renewcommand{\tablename}{Tab.}}
 \usepackage[separate-uncertainty=true, locale=DE]{siunitx}
 \sisetup{output-exponent-marker=\ensuremath{\mathrm{e}}}
+
+%%%%% section style %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage[sf,bf,it,big,clearempty]{titlesec}
+\usepackage{titling}
+\renewcommand{\maketitlehooka}{\sffamily\bfseries}
+\renewcommand{\maketitlehookb}{\rmfamily\mdseries}
+\setcounter{secnumdepth}{-1}
+
+%%%%% bibliography %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage[round,colon]{natbib}
+\renewcommand{\bibsection}{\section{References}}
+\setlength{\bibsep}{0pt}
+\setlength{\bibhang}{1.5em}
+\bibliographystyle{jneurosci}
 % \usepackage[capitalize]{cleveref}
 % \crefname{figure}{Fig.}{Figs.}
 % \crefname{equation}{Eq.}{Eqs.}
 % \creflabelformat{equation}{#2#1#3}
-\usepackage[
-    backend=bibtex,
-    style=authoryear,
-    pluralothers=true,
-    maxcitenames=1,
-    mincitenames=1
-    ]{biblatex}
-\addbibresource{cite.bib}
+%\usepackage[
+%    backend=bibtex,
+%    style=authoryear,
+%    pluralothers=true,
+%    maxcitenames=1,
+%    mincitenames=1
+%    ]{biblatex}
+%\addbibresource{cite.bib}
 %\bibdata
 %\bibstyle
 %\citation
-
-\title{Emergent intensity invariance vs. signal-to-noise ratio at three consecutive processing stages along the grasshopper song recognition pathway}
-\author{Jona Hartling$^1$, Ale\v{s} \v{S}korjanc$^2$, Jan Benda$^{1,3}$}
-\date{$^1$ Institute for Neurobiology, Eberhard Karls Universität, 72076 Tübingen, Germany \\ 
-  $^2$ Department of Biology, Biotechnical Faculty, University of Ljubljana, Ve\v{c}na pot 111, 1000 Ljubljana, Slovenia\\
-  $^3$ Bernstein Center for Computational Neuroscience Tübingen, 72076 Tübingen, Germany}
-
-\begin{document}
-\maketitle{}
+ 
+%%%%% hyperref %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage[breaklinks=true,colorlinks=true,citecolor=blue!30!black,urlcolor=blue!30!black,linkcolor=blue!30!black]{hyperref}
 
 % Text references and citations:
 \newcommand{\bcite}[1]{\cite{#1}}
@@ -110,6 +130,19 @@
 \newcommand{\tstat}{T_{\text{total}}} % Time interval where c(t) is stationary
 \newcommand{\muf}{\mu_{f_i}} % Average feature value
 
+%%%%% notes %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\newcommand{\note}[2][]{\textcolor{red}{[#1: #2]}}
+%\newcommand{\note}[2][]{}
+\newcommand{\notejh}[1]{\note[JH]{#1}}
+\newcommand{\notejb}[1]{\note[JB]{#1}}
+\newcommand{\noteas}[1]{\note[AS]{#1}}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
+\begin{document}
+
+\maketitle
+
 \section{Introduction}
 % % Drosophila/visual/article:
 % \bcite{ketkar2023multifaceted}
@@ -130,22 +163,16 @@
 % \bcite{bolding2018recurrent}
 
 % Introduction to intensity invariance:
-Intensity invariance is a fundamental property of sensory systems across
-modalities and species, from fruit flies~(\bcite{ozeri2018fast};
-\bcite{ketkar2023multifaceted}) over crickets~(\bcite{benda2008spike}) and
-grasshoppers~(\bcite{clemens2010intensity}) to
-rodents~(\bcite{bolding2018recurrent}) and
-primates~(\bcite{barbour2011intensity}). It allows for the robust recognition
+Intensity invariance is a fundamental property of sensory systems across different modalities. For example, it has been shown in auditory systems of drosophila \citep{ozeri2018fast}, crickets \citep{benda2008spike}, grasshoppers \citep{clemens2010intensity}, and primates \citep{barbour2011intensity}, visual systems in drosophila \citep{ketkar2023multifaceted}, and olfactory systems of rodents \citep{bolding2018recurrent}. It allows for the robust recognition
 of behaviorally relevant stimuli despite variations in stimulus intensity.
 However, the computational mechanisms underlying intensity invariance are often
 difficult to disentangle. Here, we use a physiologically inspired functional
-model of the grasshopper song recognition pathway to investigate the emergence
-of intensity invariance throughout the auditory processing stream.
+model of the grasshopper (\textit{Acrididae}) song recognition pathway to investigate the emergence
+of intensity invariance at different levels of the auditory processing stream, which has been studied extensively.
 
 % Why the grasshopper auditory system?
 % Why focus on song recognition among other auditory functions?
-The auditory system of grasshoppers~(\textit{Acrididae}) has been studied
-extensively over the years. Grasshoppers rely on their sense of hearing for
+Grasshoppers rely on their hearing for
 intraspecific communication --- including mate
 attraction~(\bcite{helversen1972gesang}) and
 evaluation~(\bcite{stange2012grasshopper}), sender
@@ -157,7 +184,7 @@ contexts~(\bcite{otte1970comparative}). The most conspicuous acoustic signals
 of grasshoppers are their species-specific calling songs, which broadcast the
 presence of the singing individual to potential mates within range. These songs
 are usually more characteristic of a species than morphological
-traits~(\bcite{tishechkin2016acoustic}; \bcite{tarasova2021eurasius}), which
+traits (\bcite{tishechkin2016acoustic}; \bcite{tarasova2021eurasius}), which
 can vary greatly within species~(\bcite{rowell1972variable};
 \bcite{kohler2017morphological}). The reliance on songs to mediate reproduction
 represents a strong evolutionary driving force that resulted in a massive
@@ -182,7 +209,7 @@ Grasshopper songs, like all acoustic signals, are subject to sound attenuation,
 which depends on the distance from the sound source, the frequency content of
 the signal, and the vegetation of the habitat~(\bcite{michelsen1978sound}).
 Sound attenuation has two major consequences for song recognition. First, the
-amplitude dynamics of the song pattern degrade with increasing distance to the
+amplitude dynamics of the song pattern degrades with increasing distance to the
 sender, which limits the effective communication range of grasshoppers
 to~\mbox{1\,--\,2\,m} in their typical grassland
 habitats~(\bcite{lang2000acoustic}). Second, the intensity of a song at the
@@ -203,9 +230,9 @@ degrees~(\bcite{clemens2010intensity}) and by different
 mechanisms~(\bcite{hildebrandt2009origin}).
 
 % How did we expand on the previous framework (feat. Clemens et al.)?
-In the current study, we leverage functional modelling to trace the emergence
-of intensity invariance through individual processing steps of the grasshopper
-song recognition pathway. The model pathway we propose here is based on a
+In the current study, we use functional modelling of the grasshopper
+song recognition pathway to identify individual processing steps that
+contribute to intensity invariance of the auditory system. The model pathway we propose here is based on a
 previous functional model framework for song recognition in both
 crickets~(\bcite{clemens2013computational}; \bcite{hennig2014time}) and
 grasshoppers~(\bcite{clemens2013feature}; review on
@@ -216,14 +243,14 @@ It includes feature extraction by a bank of linear-nonlinear feature detectors,
 evidence accumulation by temporal averaging of each feature, and categorical
 decision making by a weighted linear combination of feature values. We adopted
 the general structure of the existing framework and extended it by a
-physiologically plausible preprocessing stage --- including spectral filtering,
+physiologically plausible preprocessing stage --- spectral filtering,
 envelope extraction, logarithmic compression, and intensity adaptation ---
 which allows the model to operate on unmodified recordings of natural
 grasshopper songs. The resulting model pathway thus covers the entire auditory
 processing stream from the initial reception of airborne sound waves to the
 generation of a high-dimensional feature representation that allows for the
 categorical recognition of conspecific songs. It incorporates anatomical,
-physiological, and ethological evidence from several decades of research on the
+physiological, and ethological evidence from research on the
 grasshopper auditory system. In the following, we provide a side-by-side
 account of the known physiological processing steps along the song recognition
 pathway and their functional approximations in the model pathway. We then
@@ -1927,7 +1954,8 @@ habitat.
 % - How to integrate the available knowledge on anatomy, physiology, ethology?\\
 % $\rightarrow$ Abstract, simplify, formalize $\rightarrow$ Functional model framework
 
-\printbibliography
+%\printbibliography
+\bibliography{cite}
 
 \newpage
 \section{Appendix}