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?
+% 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 expands on a
-previous functional model framework for song recognition --- including feature
-extraction, evidence accumulation, and categorical decision making --- in both
+song recognition pathway. 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
 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
---- including spectral filtering, envelope extraction, logarithmic compression,
-and intensity adaptation --- which allows the model to operate on unmodified
-recordings of natural grasshopper songs. The result is a comprehensible account
-of the known
-
-
-% Its evolutionary significance makes the grasshopper auditory system ---
-% specifically, the pathway responsible for species-specific song recognition
-% --- an intriguing candidate for attempting to construct a functional model
-% framework.
-
-% Another, more substantial modification is that
-% the feature detector outputs are temporally averaged in a way that does not
-% condense them into single feature values but retains their time-varying
-% structure. This is in line with the fact that songs are no discrete units but
-% part of a continuous acoustic stream that the auditory system has to process in
-% real time. Moreover, a time-varying feature representation only stabilizes
-% after a certain delay following the onset of a song, which emphasizes the
-% 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.
+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,
+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
+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
+elaborate on the computational mechanisms that contribute to the emergence of
+intensity-invariant song representations, the interaction between these
+mechanisms, the overall capacity for intensity invariance in the system, and
+the ethological implications of our findings.
 
 \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)\\