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Hallucinated the remainders of the introduction draft.

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Some polishing needed.
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j-hartling 2026-01-22 17:45:01 +01:00
parent a6a25d9931
commit 51417ab634
9 changed files with 332 additions and 172 deletions
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@ -964,6 +964,46 @@
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[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 464, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 499, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 508, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 529, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 538, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 549, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 580, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 618, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 627, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 648, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 669, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 678, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 687, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 704, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 713, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 738, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 755, warning: 6 characters of junk seen at toplevel
[295] Biber.pm:133> INFO - WARNINGS: 45
[74] Biber.pm:979> INFO - Found 50 citekeys in bib section 0
[80] Biber.pm:4419> INFO - Processing section 0
[85] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0
[86] bibtex.pm:1713> INFO - LaTeX decoding ...
[115] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib'
[278] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized'
[278] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable'
[278] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US'
[278] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US'
[300] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8'
[310] bbl.pm:763> INFO - Output to main.bbl
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 10, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 21, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 38, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 49, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 58, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 73, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 114, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 123, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 132, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 153, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 174, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 192, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 203, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 214, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 223, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 232, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 243, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 252, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 263, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 274, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 283, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 310, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 354, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 373, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 400, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 409, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 428, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 455, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 464, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 499, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 508, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 529, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 538, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 549, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 580, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 618, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 627, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 648, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 669, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 678, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 687, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 704, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 713, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 738, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_TN1C/347c261ec4135a5723bef5c751f5078f_86192.utf8, line 755, warning: 6 characters of junk seen at toplevel
[310] Biber.pm:133> INFO - WARNINGS: 45

16
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54
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This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Debian) (preloaded format=pdflatex 2025.10.28) 16 JAN 2026 15:47
This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Debian) (preloaded format=pdflatex 2025.10.28) 22 JAN 2026 17:40
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@ -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.