Finished drafting the introduction.

main.tex

@@ -198,140 +198,35 @@ process, in which different neuronal populations contribute to varying
 degrees~(\bcite{clemens2010intensity}) and by different
 mechanisms~(\bcite{hildebrandt2009origin}).
 
-% How can song recognition be modelled functionally (feat. Jan Clemens & Co.)?
+% How did we expand on the previous framework (feat. Clemens et al.)?
-% How did we expand on the previous framework?
 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 expands on a
+song recognition pathway. The model pathway we propose here is based on a
-previous functional model framework for song recognition --- including feature
+previous functional model framework for song recognition in both
-extraction, evidence accumulation, and categorical decision making --- in both
 crickets~(\bcite{clemens2013computational}; \bcite{hennig2014time}) and
 grasshoppers~(\bcite{clemens2013feature}; review on
 both:~\bcite{ronacher2015computational}). The exisiting framework relies on
 pulse trains as input signals, which were designed to capture the essential
 structural properties of natural song envelopes~(\bcite{clemens2013feature}).
-We extended this framework by a physiologically plausible preprocessing stage
+It includes feature extraction by a bank of linear-nonlinear feature detectors,
---- including spectral filtering, envelope extraction, logarithmic compression,
+evidence accumulation by temporal averaging of each feature, and categorical
-and intensity adaptation --- which allows the model to operate on unmodified
+decision making by a weighted linear combination of feature values. We adopted
-recordings of natural grasshopper songs. The result is a comprehensible account
+the general structure of the existing framework and extended it by a
-of the known
+physiologically plausible preprocessing stage --- including spectral filtering,
-
+envelope extraction, logarithmic compression, and intensity adaptation ---
-
+which allows the model to operate on unmodified recordings of natural
-% Its evolutionary significance makes the grasshopper auditory system ---
+grasshopper songs. The resulting model pathway thus covers the entire auditory
-% specifically, the pathway responsible for species-specific song recognition
+processing stream from the initial reception of airborne sound waves to the
-% --- an intriguing candidate for attempting to construct a functional model
+generation of a high-dimensional feature representation that allows for the
-% framework.
+categorical recognition of conspecific songs. It incorporates anatomical,
-
+physiological, and ethological evidence from several decades of research on the
-% Another, more substantial modification is that
+grasshopper auditory system. In the following, we provide a side-by-side
-% the feature detector outputs are temporally averaged in a way that does not
+account of the known physiological processing steps along the song recognition
-% condense them into single feature values but retains their time-varying
+pathway and their functional approximations in the model pathway. We then
-% structure. This is in line with the fact that songs are no discrete units but
+elaborate on the computational mechanisms that contribute to the emergence of
-% part of a continuous acoustic stream that the auditory system has to process in
+intensity-invariant song representations, the interaction between these
-% real time. Moreover, a time-varying feature representation only stabilizes
+mechanisms, the overall capacity for intensity invariance in the system, and
-% after a certain delay following the onset of a song, which emphasizes the
+the ethological implications of our findings.
-% temporal dynamics of evidence accumulation towards a final categorical
-% decision. The most notable difference between our model pathway and the
-% existing framework, however, lays in the addition of a physiologically inspired
-% preprocessing stage, whose starting point corresponds to the initial reception
-% of airborne sound waves. This allows the model to operate on unmodified
-% recordings of natural grasshopper songs instead of condensed pulse train
-% approximations, which widens its scope towards more realistic, ecologically
-% relevant scenarios.
-
-%% BACKUP OF PREVIOUS ITERATION:
-% % Why functional models of sensory systems?
-% Our scientific understanding of sensory processing systems is based on the
-% distributed accumulation of specific anatomical, physiological, and ethological
-% evidence. This leaves us with the challenge of integrating the available
-% knowledge fragments into a coherent whole in order to address more and more
-% far-reaching questions, from the interaction between individual processing
-% steps to comparisons between similar systems across different species. One way
-% to deal with this challenge is to build a unified framework that captures the
-% essential functional aspects of a sensory system. However, building such a
-% framework is a challenging task in itself. It requires a wealth of existing
-% knowledge of the system and the stimuli it operates on, a clearly defined
-% scope, and careful abstraction of the underlying structures and mechanisms.
-
-% % Why the grasshopper auditory system?
-% % Why focus on song recognition among other auditory functions?
-% One sensory system that has been extensively studied over the years is the
-% auditory system of grasshoppers~(\textit{Acrididae}). Grasshoppers rely on
-% their sense of hearing for intraspecific communication --- including mate
-% attraction~(\bcite{helversen1972gesang}) and
-% evaluation~(\bcite{stange2012grasshopper}), sender
-% localization~(\bcite{helversen1988interaural}), courtship
-% display~(\bcite{elsner1968neuromuskularen}), and rival
-% deterrence~(\bcite{greenfield1993acoustic}) --- and have evolved a variety of
-% acoustic signals for different behavioral
-% 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
-% 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
-% species diversification~(\bcite{vedenina2011speciation};
-% \bcite{sevastianov2023evolution}), with over 6800 recognized species in the
-% \textit{Acrididae} family~(\bcite{cigliano2024orthoptera}).
-
-% % What are the signals that the auditory system is supposed to recognize?
-% Grasshopper songs are amplitude-modulated broad-band acoustic signals. They
-% consist of a series of noisy syllables and relatively quiet pauses, which form
-% a characteristic repetitive pattern~(\bcite{helversen1977stridulatory};
-% \bcite{stumpner1994song}). Song recognition depends on certain structural
-% parameters of this pattern --- such as the duration of syllables and
-% pauses~(\bcite{helversen1972gesang}), the slope of pulse
-% onsets~(\bcite{helversen1993absolute}), and the accentuation of syllable onsets
-% relative to the preceeding pause~(\bcite{balakrishnan2001song};
-% \bcite{helversen2004acoustic}) --- which are sufficiently conveyed by the
-% amplitude modulation of the song alone~(\bcite{helversen1997recognition}).
-
-% % Why is intensity invariance important for song recognition?
-% 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}).
-% This has two major consequences for the receiving auditory system. First, the
-% amplitude dynamics of the song pattern degrade 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
-% receiver's position varies with the position of the sender, which should
-% ideally not affect song recognition. The auditory system thus needs to achieve
-% a certain degree of intensity invariance --- a time scale-selective sensitivity
-% to faster amplitude dynamics and simultaneous insensitivity to more sustained
-% amplitude dynamics. Intensity invariance is commonly associated with neuronal
-% adaptation~(\bcite{benda2008spike}; \bcite{barbour2011intensity};
-% \bcite{ozeri2018fast}; more general:~\bcite{benda2021neural}). Different neuron
-% types in the grasshopper auditory system exhibit spike-frequency adaptation in
-% response to sustained stimulation~(\bcite{romer1976informationsverarbeitung};
-% \bcite{gollisch2004input}; \bcite{hildebrandt2009origin};
-% \bcite{clemens2010intensity}; \bcite{fisch2012channel}). Accordingly, intensity
-% invariance is not the result of a single processing step but rather a gradual
-% process, in which different neuronal populations contribute to varying
-% degrees~(\bcite{clemens2010intensity}) and by different
-% mechanisms~(\bcite{hildebrandt2009origin}).
-
-% % How can song recognition be modelled functionally (feat. Jan Clemens & Co.)?
-% % How did we expand on the previous framework?
-% In the current study, we use a physiologically inspired functional model of the
-% grasshopper song recognition pathway to investigate the emergence of intensity
-% invariance along the auditory processing stream. The model pathway expands on a
-% previous functional model framework for song recognition --- including feature
-% extraction, evidence accumulation, and categorical decision making --- in both
-% crickets~(\bcite{clemens2013computational}; \bcite{hennig2014time}) and
-% grasshoppers~(\bcite{clemens2013feature}; review on
-% both:~\bcite{ronacher2015computational}). The exisiting framework relies on
-% pulse trains as input signals, which were designed to capture the essential
-% structural properties of natural song envelopes~(\bcite{clemens2013feature}).
-% The extracted song features are compressed into a single preference value for
-% each presented input signal, which serves as predictor for the behavioral
-% response of the animal. We extended this framework by a physiologically
-% plausible preprocessing stage --- including envelope extraction, logarithmic
-% compression, and intensity adaptation --- that allows the model to operate on
-% recordings of natural grasshopper songs. Furthermore, we modified the feature
-% extraction stage in a way that retains the time-varying structure of individual
-% song features.
 
 \section{Methods}
 % This maybe does not quite fit here, but it is the most general part of the
@@ -1632,6 +1527,21 @@ natural song variation.
 \section{Conclusions \& outlook}
 
 % RIPPED FROM INTRODUCTION:
+
+% Why functional models of sensory systems?
+% Our scientific understanding of sensory processing systems is based on the
+% distributed accumulation of specific anatomical, physiological, and
+% ethological evidence. This leaves us with the challenge of integrating the
+% available knowledge fragments into a coherent whole in order to address more
+% and more far-reaching questions, from the interaction between individual
+% processing steps to comparisons between similar systems across different
+% species. One way to deal with this challenge is to build a unified framework
+% that captures the essential functional aspects of a sensory system. However,
+% building such a framework is a challenging task in itself. It requires a
+% wealth of existing knowledge of the system and the stimuli it operates on, a
+% clearly defined scope, and careful abstraction of the underlying structures
+% and mechanisms.
+
 % Multi-species, multi-individual communally inhabited environments\\
 % - Temporal overlap: Simultaneous singing across individuals/species common\\
 % - Frequency overlap: Little speciation into frequency bands (likely unused)\\