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\documentclass[a4paper, 12pt]{article} \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[left=2cm,right=2cm,top=2cm,bottom=2cm,includeheadfoot]{geometry}
% \usepackage[onehalfspacing]{setspace} % \usepackage[onehalfspacing]{setspace}
\usepackage{graphicx} \usepackage{graphicx}
@@ -17,30 +29,38 @@
\addto\captionsenglish{\renewcommand{\tablename}{Tab.}} \addto\captionsenglish{\renewcommand{\tablename}{Tab.}}
\usepackage[separate-uncertainty=true, locale=DE]{siunitx} \usepackage[separate-uncertainty=true, locale=DE]{siunitx}
\sisetup{output-exponent-marker=\ensuremath{\mathrm{e}}} \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} % \usepackage[capitalize]{cleveref}
% \crefname{figure}{Fig.}{Figs.} % \crefname{figure}{Fig.}{Figs.}
% \crefname{equation}{Eq.}{Eqs.} % \crefname{equation}{Eq.}{Eqs.}
% \creflabelformat{equation}{#2#1#3} % \creflabelformat{equation}{#2#1#3}
\usepackage[ %\usepackage[
backend=bibtex, % backend=bibtex,
style=authoryear, % style=authoryear,
pluralothers=true, % pluralothers=true,
maxcitenames=1, % maxcitenames=1,
mincitenames=1 % mincitenames=1
]{biblatex} % ]{biblatex}
\addbibresource{cite.bib} %\addbibresource{cite.bib}
%\bibdata %\bibdata
%\bibstyle %\bibstyle
%\citation %\citation
\title{Emergent intensity invariance vs. signal-to-noise ratio at three consecutive processing stages along the grasshopper song recognition pathway} %%%%% hyperref %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\author{Jona Hartling$^1$, Ale\v{s} \v{S}korjanc$^2$, Jan Benda$^{1,3}$} \usepackage[breaklinks=true,colorlinks=true,citecolor=blue!30!black,urlcolor=blue!30!black,linkcolor=blue!30!black]{hyperref}
\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{}
% Text references and citations: % Text references and citations:
\newcommand{\bcite}[1]{\cite{#1}} \newcommand{\bcite}[1]{\cite{#1}}
@@ -110,6 +130,19 @@
\newcommand{\tstat}{T_{\text{total}}} % Time interval where c(t) is stationary \newcommand{\tstat}{T_{\text{total}}} % Time interval where c(t) is stationary
\newcommand{\muf}{\mu_{f_i}} % Average feature value \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} \section{Introduction}
% % Drosophila/visual/article: % % Drosophila/visual/article:
% \bcite{ketkar2023multifaceted} % \bcite{ketkar2023multifaceted}
@@ -130,22 +163,16 @@
% \bcite{bolding2018recurrent} % \bcite{bolding2018recurrent}
% Introduction to intensity invariance: % Introduction to intensity invariance:
Intensity invariance is a fundamental property of sensory systems across 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
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
of behaviorally relevant stimuli despite variations in stimulus intensity. of behaviorally relevant stimuli despite variations in stimulus intensity.
However, the computational mechanisms underlying intensity invariance are often However, the computational mechanisms underlying intensity invariance are often
difficult to disentangle. Here, we use a physiologically inspired functional difficult to disentangle. Here, we use a physiologically inspired functional
model of the grasshopper song recognition pathway to investigate the emergence model of the grasshopper (\textit{Acrididae}) song recognition pathway to investigate the emergence
of intensity invariance throughout the auditory processing stream. of intensity invariance at different levels of the auditory processing stream, which has been studied extensively.
% Why the grasshopper auditory system? % Why the grasshopper auditory system?
% Why focus on song recognition among other auditory functions? % Why focus on song recognition among other auditory functions?
The auditory system of grasshoppers~(\textit{Acrididae}) has been studied Grasshoppers rely on their hearing for
extensively over the years. Grasshoppers rely on their sense of hearing for
intraspecific communication --- including mate intraspecific communication --- including mate
attraction~(\bcite{helversen1972gesang}) and attraction~(\bcite{helversen1972gesang}) and
evaluation~(\bcite{stange2012grasshopper}), sender 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 of grasshoppers are their species-specific calling songs, which broadcast the
presence of the singing individual to potential mates within range. These songs presence of the singing individual to potential mates within range. These songs
are usually more characteristic of a species than morphological 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}; can vary greatly within species~(\bcite{rowell1972variable};
\bcite{kohler2017morphological}). The reliance on songs to mediate reproduction \bcite{kohler2017morphological}). The reliance on songs to mediate reproduction
represents a strong evolutionary driving force that resulted in a massive 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 which depends on the distance from the sound source, the frequency content of
the signal, and the vegetation of the habitat~(\bcite{michelsen1978sound}). the signal, and the vegetation of the habitat~(\bcite{michelsen1978sound}).
Sound attenuation has two major consequences for song recognition. First, the 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 sender, which limits the effective communication range of grasshoppers
to~\mbox{1\,--\,2\,m} in their typical grassland to~\mbox{1\,--\,2\,m} in their typical grassland
habitats~(\bcite{lang2000acoustic}). Second, the intensity of a song at the habitats~(\bcite{lang2000acoustic}). Second, the intensity of a song at the
@@ -203,9 +230,9 @@ degrees~(\bcite{clemens2010intensity}) and by different
mechanisms~(\bcite{hildebrandt2009origin}). mechanisms~(\bcite{hildebrandt2009origin}).
% How did we expand on the previous framework (feat. Clemens et al.)? % How did we expand on the previous framework (feat. Clemens et al.)?
In the current study, we leverage functional modelling to trace the emergence In the current study, we use functional modelling of the grasshopper
of intensity invariance through individual processing steps of the grasshopper song recognition pathway to identify individual processing steps that
song recognition pathway. The model pathway we propose here is based on a 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 previous functional model framework for song recognition in both
crickets~(\bcite{clemens2013computational}; \bcite{hennig2014time}) and crickets~(\bcite{clemens2013computational}; \bcite{hennig2014time}) and
grasshoppers~(\bcite{clemens2013feature}; review on 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 evidence accumulation by temporal averaging of each feature, and categorical
decision making by a weighted linear combination of feature values. We adopted decision making by a weighted linear combination of feature values. We adopted
the general structure of the existing framework and extended it by a 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 --- envelope extraction, logarithmic compression, and intensity adaptation ---
which allows the model to operate on unmodified recordings of natural which allows the model to operate on unmodified recordings of natural
grasshopper songs. The resulting model pathway thus covers the entire auditory grasshopper songs. The resulting model pathway thus covers the entire auditory
processing stream from the initial reception of airborne sound waves to the processing stream from the initial reception of airborne sound waves to the
generation of a high-dimensional feature representation that allows for the generation of a high-dimensional feature representation that allows for the
categorical recognition of conspecific songs. It incorporates anatomical, 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 grasshopper auditory system. In the following, we provide a side-by-side
account of the known physiological processing steps along the song recognition account of the known physiological processing steps along the song recognition
pathway and their functional approximations in the model pathway. We then pathway and their functional approximations in the model pathway. We then
@@ -1979,7 +2006,8 @@ habitat.
% - How to integrate the available knowledge on anatomy, physiology, ethology?\\ % - How to integrate the available knowledge on anatomy, physiology, ethology?\\
% $\rightarrow$ Abstract, simplify, formalize $\rightarrow$ Functional model framework % $\rightarrow$ Abstract, simplify, formalize $\rightarrow$ Functional model framework
\printbibliography %\printbibliography
\bibliography{cite}
\newpage \newpage
\section{Appendix} \section{Appendix}