Hallucinated the remainders of the introduction draft.

Some polishing needed.

main.tex

@@ -76,6 +76,7 @@
 
 \section{Exploring a grasshopper's sensory world}
 
+% Why functional models of sensory systems?
 Our scientific understanding of sensory processing systems results from the
 distributed accumulation of anatomical, physiological and ethological evidence.
 This process is undoubtedly without alternative; however, it leaves us with the
@@ -90,6 +91,8 @@ requires a wealth of existing knowledge of the system and the signals it
 operates on, a clearly defined scope, and careful reduction, abstraction, and
 formalization of the underlying structures and mechanisms.
 
+% Why the grasshopper auditory system?
+% Why focus on song recognition among other auditory functions?
 One sensory system about which extensive information has been gathered over the
 years is the auditory system of grasshoppers~(\textit{Acrididae}). Grasshoppers
 rely on their sense of hearing primarily for intraspecific communication, which
@@ -118,10 +121,13 @@ its emergence, that makes the grasshopper auditory system an intriguing
 candidate for attempting to construct a functional model framework. As a
 necessary reduction, the model we propose here focuses on the pathway
 responsible for the recognition of species-specific calling songs, disregarding
-other auditory functions such as directional
+other essential auditory functions such as directional
 hearing~(\bcite{helversen1984parallel}, \bcite{ronacher1986routes},
 \bcite{helversen1988interaural}).
 
+% What are the signals the auditory system is supposed to recognize?
+% Why is intensity invariance important for song recognition?
+% (Obviously, split this paragraph)
 To understand the functional challenges faced by the grasshopper auditory
 system, one has to understand the properties of the songs it is designed to
 recognize. Grasshopper songs are amplitude-modulated broad-band acoustic
@@ -162,9 +168,8 @@ intensity invariance --- a time scale-selective sensitivity to faster amplitude
 dynamics and simultaneous insensitivity to slower, more sustained amplitude
 dynamics. Intensity invariance in different auditory systems is often
 associated with neuronal adaptation~(\bcite{benda2008spike},
-\bcite{barbour2011intensity}, \bcite{ozeri2018fast}), which represents an
-important principle of dynamic sensory systems in
-general~(\bcite{benda2021neural}). In the grasshopper auditory system, a number
+\bcite{barbour2011intensity}, \bcite{ozeri2018fast}, more
+general:~\bcite{benda2021neural}). In the grasshopper auditory system, a number
 of neuron types along the processing chain exhibit spike-frequency adaptation
 in response to sustained stimulus
 intensities~(\bcite{romer1976informationsverarbeitung},
@@ -181,16 +186,89 @@ physiologically inspired signal transformations --- specifically, pairs of
 nonlinear and linear operations --- is sufficient to achieve a meaningful
 degree of intensity invariance.
 
-Invariance to non-informative signal variations is a crucial property of song
-representations that are suitable for the purpose of song recognition. However,
-it is likely not sufficient on its own. The auditory system also needs to
-extract sufficiently informative song features in order to reliably
-discriminate between conspecific and heterospecific song patterns. Other
-authors have proposed a comprehensive physiologically inspired framework
-to describe the process of feature extraction based on linear-nonlinear
-modelling~(\cite{clemens2013computational}, \cite{clemens2013feature},
-\cite{ronacher2015computational}), which represents an important precursor and
-cornerstone for the model we present here.
+% How can song recognition be modelled functionally (feat. Jan Clemens & Co.)?
+% How did we expand on the previous framework?
+% (Still can't stand some of this paragraph's structure and wording...)
+Invariance to non-informative song variations is crucial for reliable song
+recognition; however, it is not sufficient to this end. In order to recognize a
+conspecific song as such, the auditory system also needs to extract
+sufficiently informative features of the song pattern and then integrate the
+gathered information into a final categorical percept. Previous authors have
+proposed a functional model framework that describes this process --- feature
+extraction, evidence accumulation, and categorical decision making --- in both
+crickets~(\bcite{clemens2013computational}) and
+grasshoppers~(\bcite{clemens2013feature}, review on
+both:~\bcite{ronacher2015computational}). Their framework provides a
+comprehensible and biologically plausible account of the computational
+mechanisms required for species-specific song recognition. As such, it has
+served as the inspiration for the development of the model pathway we propose
+here. The existing framework relies on pulse trains as input signals, which
+were designed to capture the essential structural properties of natural song
+envelopes~(\bcite{clemens2013feature}). In the first step, a bank of parallel
+linear-nonlinear feature detectors is applied to the input signal. Each feature
+detector consists of a convolutional filter and a subsequent sigmoidal
+nonlinearity. The outputs of these feature detectors are temporally averaged to
+obtain a single feature value per detector, which is then assigned a specific
+weight. The linear combination of weighted feature values results in a single
+preference value, that serves as predictor for the behavioral response of the
+animal to the presented input signal. Our model pathway adopts the general
+structure of the existing framework but modifies it in several key aspects. The
+convolutional filters, which have previously been fitted to behavioral data for
+each individual species~(\bcite{clemens2013computational}), are replaced by a
+larger, more general set of unfitted Gabor kernels in order to cover a wide
+range of possible song features for as many species as possible. Gabor kernels
+closely resemble the structure of the filters used in previous
+models~(\bcite{clemens2013computational}, \bcite{clemens2013feature},
+\bcite{hennig2014time}) as well as the measured spike-triggered averages of
+higher-order interneurons in the grasshopper auditory
+system~(\bcite{clemens2011efficient}). The fitted sigmoidal nonlinearities in
+the existing framework consistently exhibited very steep
+slopes~(\bcite{clemens2013computational}, \bcite{clemens2013feature}) and are
+therefore approximated by simpler shifted Heaviside step-functions in our
+model. Another, more substantial modification is that the outputs of the
+feature detectors are temporally averaged in a way that does not condense them
+into single feature values but retains their time-varying structure. A
+time-varying feature representation introduces a certain time constant until
+the representation stabilizes after the onset of a song. This reflects the
+continuous nature of acoustic input and auditory perception, as songs are not
+received as discrete units but processed continuously prior to the final
+categorical decision to initate a behavioral response~(SOURCE LOL). The
+most notable difference between our model pathway and the existing framework,
+however, lays in the addition of a physiologically inspired preprocessing
+portion, 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.
+For instance, we were able to investigate the contribution of different
+processing stages to the emergence of intensity-invariant song representations
+based on actual field recordings of songs at different distances from the
+sender.
+
+
+
+
+% Invariance to non-informative song variations is crucial for reliable song
+% recognition; however, it is not sufficient to this end. In order to recognize a
+% conspecific song as such, the auditory system needs to extract sufficiently
+% informative features of the song pattern and then integrate the gathered
+% information into a final categorical percept. Previous authors have proposed a
+% biologically plausible functional framework that describes this process ---
+% feature extraction, evidence accumulation, and categorical decision making ---
+% in both crickets~(\bcite{clemens2013computational}) and
+% grasshoppers~(\bcite{clemens2013feature}, review on
+% both:~\bcite{ronacher2015computational}). Their framework provides a
+% comprehensive, generalizable account of the computational mechanisms required
+% for species-specific song recognition, which has served as inspiration for the development of our own model of the grasshopper auditory
+% pathway, where it now constitutes the foundation of the recognition mechanism.
+% According to the existing framework, a bank of parallel linear-nonlinear
+% feature detectors is initially applied to the input signal. Each feature
+% detector consists of a convolutional filter and a subsequent sigmoidal
+% nonlinearity. The outputs of these feature detectors are temporally averaged to
+% obtain a single feature value per detector, which is assigned a specific
+% weight. The linear combination of weighted feature values results in a single
+% preference value, which then serves as predictor for the behavioral response of
+% the animal. The model we propose here adopts this general structure but
+% modifies and several key aspects.