diff --git a/cite.bib b/cite.bib index 1df10ef..827520b 100644 --- a/cite.bib +++ b/cite.bib @@ -55,6 +55,17 @@ year={2017}, }# Cited +@article{bolding2018recurrent, + title={Recurrent cortical circuits implement concentration-invariant odor coding}, + author={Bolding, Kevin A and Franks, Kevin M}, + journal={Science}, + volume={361}, + number={6407}, + pages={eaat6904}, + year={2018}, + publisher={American Association for the Advancement of Science} +}# Cited + @article{breckow1985mechanics, title={Mechanics of the transduction of sound in the tympanal organ of adults and larvae of locusts}, author={Breckow, Joachim and Sippel, Martin}, @@ -388,6 +399,17 @@ year={1978}, publisher={Springer} } +@article{ketkar2023multifaceted, + title={Multifaceted luminance gain control beyond photoreceptors in Drosophila}, + author={Ketkar, Madhura D and Shao, Shuai and Gjorgjieva, Julijana and Silies, Marion}, + journal={Current Biology}, + volume={33}, + number={13}, + pages={2632--2645}, + year={2023}, + publisher={Elsevier} +}# Cited + @article{kohler2017morphological, title={{Morphological and colour morph clines along an altitudinal gradient in the meadow grasshopper Pseudochorthippus parallelus}}, author={K{\"o}hler, G{\"u}nter and Samietz, J{\"o}rg and Schielzeth, Holger}, @@ -453,6 +475,15 @@ year={2014}, }# Cited +@article{mann2025resonant, + title={Resonant song recognition and the evolution of acoustic communication in crickets}, + author={Mann, Winston and Erregger, Bettina and Hennig, Ralf Matthias and Clemens, Jan}, + journal={iScience}, + volume={28}, + number={2}, + year={2025}, + publisher={Elsevier} +} @article{meyer1996well, title={How well are frequency sensitivities of grasshopper ears tuned to species-specific song spectra?}, author={Meyer, Jens and Elsner, Norbert}, diff --git a/literature/Bolding_Franks_2018.pdf b/literature/Bolding_Franks_2018.pdf new file mode 100644 index 0000000..d75f0bd Binary files /dev/null and b/literature/Bolding_Franks_2018.pdf differ diff --git a/literature/Ketkar_Shao_Gjorgjieva_Silies_2023.pdf b/literature/Ketkar_Shao_Gjorgjieva_Silies_2023.pdf new file mode 100644 index 0000000..00ec46d Binary files /dev/null and b/literature/Ketkar_Shao_Gjorgjieva_Silies_2023.pdf differ diff --git a/literature/Mann_Erregger_Hennig_Clemens_2025.pdf b/literature/Mann_Erregger_Hennig_Clemens_2025.pdf new file mode 100644 index 0000000..3c57985 Binary files /dev/null and b/literature/Mann_Erregger_Hennig_Clemens_2025.pdf differ diff --git a/main.pdf b/main.pdf index 3c8293f..cc799c2 100644 Binary files a/main.pdf and b/main.pdf differ diff --git a/main.tex b/main.tex index 87ba146..52cc2ae 100644 --- a/main.tex +++ b/main.tex @@ -107,25 +107,42 @@ \newcommand{\muf}{\mu_{f_i}} % Average feature value \section{Introduction} +% % Drosophila/visual/article: +% \bcite{ketkar2023multifaceted} -% 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. +% % Drosophila/auditory/article: +% \bcite{ozeri2018fast} + +% % Primate/auditory/review: +% \bcite{barbour2011intensity} + +% % Cricket/auditory/article: +% \bcite{benda2008spike} + +% % Locust/auditory/article: +% \bcite{clemens2010intensity} + +% % Rodent/olfactory/article: +% \bcite{bolding2018recurrent} + +% Introduction to intensity invariance: +Intensity invariance is a fundamental property of sensory systems across +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. +However, the computational mechanisms underlying intensity invariance are often +difficult to disentangle. Here, we use a physiologically inspired functional +model of the grasshopper song recognition pathway to investigate the emergence +of intensity invariance throughout the auditory processing stream. % 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 +The auditory system of grasshoppers~(\textit{Acrididae}) has been studied +extensively over the years. Grasshoppers rely on their sense of hearing for +intraspecific communication --- including mate attraction~(\bcite{helversen1972gesang}) and evaluation~(\bcite{stange2012grasshopper}), sender localization~(\bcite{helversen1988interaural}), courtship @@ -143,11 +160,6 @@ 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}). -% Could go lower to concluding part: -% 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. % What are the signals that the auditory system is supposed to recognize? Grasshopper songs are amplitude-modulated broad-band acoustic signals. They @@ -163,144 +175,163 @@ 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 sender, the frequency content of the -signal, and the vegetation of the habitat~(\bcite{michelsen1978sound}). The -amplitude dynamics of the song pattern degrade fairly quickly, which limits the -effective communication range of grasshoppers to~\mbox{1\,-\,2\,m} in their -typical grassland habitats~(\bcite{lang2000acoustic}). Moreover, the intensity -of a song at the receiver's position varies with the location of the sender, -which should ideally not affect the recognition of the song. - -This neccessitates that the auditory system achieves a certain degree of -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}; 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}; +which depends on the distance from the sound source, the frequency content of +the signal, and the vegetation of the habitat~(\bcite{michelsen1978sound}). +Sound attenuation has two major consequences for song recognition. 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 neural +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}) and thus likely -contribute to the emergence of intensity-invariant song representations. This -means that 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}). Approximating this process within a -functional model framework thus requires a considerable amount of -simplification. In this work, we demonstrate that even a small number of basic -physiologically inspired signal transformations --- specifically, pairs of -nonlinear and linear operations --- is sufficient to achieve a meaningful -degree of intensity invariance. +\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? -% (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 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 +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 crickets~(\bcite{clemens2013computational}; \bcite{hennig2014time}) 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, which 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, generic set of unfitted Gabor basis functions in order to cover a wide -range of possible song features across different species. Gabor functions -approximate the general structure of the filters used in the existing framework -as well as the filter functions found in various auditory -neurons~(\bcite{rokem2006spike}; \bcite{clemens2011efficient}; -\bcite{clemens2012nonlinear}). The fitted sigmoidal nonlinearities in the -existing framework consistently exhibited very steep slopes and are therefore -replaced by shifted Heaviside step-functions, which results in a binarization -of the feature detector outputs. 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. 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. -% Forgive me, it's friday. -In the following, we outline the structure of the proposed model of the -grasshopper auditory pathway, from the initial reception of sound waves up to -the generation of a high-dimensional, time-varying feature representation that -is suitable for species-specific song recognition. We provide a side-by-side -account of the known physiological processing steps and their functional -approximation by basic mathematical operations. We then elaborate on the key -mechanisms that drive the emergence of intensity-invariant song representations -within the auditory pathway. +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 -% RIPPED FROM RESULTS, MAYBE INTEGRATE SOMEWHERE HERE: -% The robustness of song recognition is tied to the degree of intensity -% invariance of the finalized feature representation. Ideally, the values of each -% feature should depend only on the relative amplitude dynamics of the song -% pattern but not on the overall intensity of the song. In the grasshopper, the -% emergence of intensity-invariant representations along the song recognition -% pathway likely is a distributed process that involves different neuronal -% populations, which raises the question of what the essential computational -% mechanisms are that drive this process. Within the model pathway, we identified -% two key mechanisms that render the song representation more invariant to -% intensity variations. The two mechanisms each comprise a nonlinear signal -% transformation followed by a linear signal transformation but differ in the -% specific operations involved, as outlined in the following sections. -% SCRAPPED UNTIL FURTHER NOTICE: -% Multi-species, multi-individual communally inhabited environments\\ -% - Temporal overlap: Simultaneous singing across individuals/species common\\ -% - Frequency overlap: Little speciation into frequency bands (likely unused)\\ -% - "Biotic noise": Hetero-/conspecifics ("Another one's songs are my noise")\\ -% - "Abiotic noise": Wind, water, vegetation, anthropogenic\\ -% - Effects of habitat structure on sound propagation (landscape - soundscape)\\ -% $\rightarrow$ Sensory constraints imposed by the (acoustic) environment +% 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. -% Cluster of auditory challenges (interlocking constraints $\rightarrow$ tight coupling):\\ -% From continuous acoustic input, generate neuronal representations that...\\ -% 1)...allow for the separation of relevant (song) events from ambient noise floor\\ -% 2)...compensate for behaviorally non-informative song variability (invariances)\\ -% 3)...carry sufficient information to characterize different song patterns, -% recognize the ones produced by conspecifics, and make appropriate behavioral -% decisions based on context (sender identity, song type, mate/rival quality) +% 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. -% How can the auditory system of grasshoppers meet these challenges?\\ -% - What are the minimum functional processing steps required?\\ -% - Which known neuronal mechanisms can implement these steps?\\ -% - Which and how many stages along the auditory pathway contribute?\\ -% $\rightarrow$ What are the limitations of the system as a whole? +%% 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. -% How can a human observer conceive a grasshopper's auditory percepts?\\ -% - How to investigate the workings of the auditory pathway as a whole?\\ -% - How to systematically test effects and interactions of processing parameters?\\ -% - How to integrate the available knowledge on anatomy, physiology, ethology?\\ -% $\rightarrow$ Abstract, simplify, formalize $\rightarrow$ Functional model framework +% % 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 @@ -449,9 +480,13 @@ $i$-th ascending neuron: \label{eq:conv} \end{equation} We use Gabor kernels as basis functions for creating different template -patterns. An arbitrary one-dimensional, real Gabor kernel is generated by -multiplication of a Gaussian envelope with standard deviation or kernel width -$\kwi$ and a sinusoidal carrier with frequency $\kfi$ and phase $\kpi$: +patterns. Gabor functions presumably capture the essential structural +properties of the filter functions found in various auditory +neurons~(\bcite{rokem2006spike}; \bcite{clemens2011efficient}; +\bcite{clemens2012nonlinear}). An arbitrary one-dimensional, real Gabor kernel +is generated by multiplication of a Gaussian envelope with standard deviation +or kernel width $\kwi$ and a sinusoidal carrier with frequency $\kfi$ and phase +$\kpi$: \begin{equation} k_i(t,\,\kwi,\,\kfi,\,\kpi)\,=\,e^{-\frac{t^{2}}{2{\kwi}^{2}}}\,\cdot\,\sin(\kfi\,t\,+\,\kpi), \qquad \kfi\,=\,2\pi f_{\text{sin}_i} \label{eq:gabor} @@ -1596,6 +1631,70 @@ natural song variation. \section{Conclusions \& outlook} +% RIPPED FROM INTRODUCTION: +% Multi-species, multi-individual communally inhabited environments\\ +% - Temporal overlap: Simultaneous singing across individuals/species common\\ +% - Frequency overlap: Little speciation into frequency bands (likely unused)\\ +% - "Biotic noise": Hetero-/conspecifics ("Another one's songs are my noise")\\ +% - "Abiotic noise": Wind, water, vegetation, anthropogenic\\ +% - Effects of habitat structure on sound propagation (landscape - soundscape)\\ +% $\rightarrow$ Sensory constraints imposed by the (acoustic) environment + +% Cluster of auditory challenges (interlocking constraints $\rightarrow$ tight coupling):\\ +% From continuous acoustic input, generate neuronal representations that...\\ +% 1)...allow for the separation of relevant (song) events from ambient noise floor\\ +% 2)...compensate for behaviorally non-informative song variability (invariances)\\ +% 3)...carry sufficient information to characterize different song patterns, +% recognize the ones produced by conspecifics, and make appropriate behavioral +% decisions based on context (sender identity, song type, mate/rival quality) + +% How can the auditory system of grasshoppers meet these challenges?\\ +% - What are the minimum functional processing steps required?\\ +% - Which known neuronal mechanisms can implement these steps?\\ +% - Which and how many stages along the auditory pathway contribute?\\ +% $\rightarrow$ What are the limitations of the system as a whole? + +% How can a human observer conceive a grasshopper's auditory percepts?\\ +% - How to investigate the workings of the auditory pathway as a whole?\\ +% - How to systematically test effects and interactions of processing parameters?\\ +% - How to integrate the available knowledge on anatomy, physiology, ethology?\\ +% $\rightarrow$ Abstract, simplify, formalize $\rightarrow$ Functional model framework + +\textbf{Differences between the model pathway and the previous framework:} +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, generic +set of unfitted Gabor basis functions in order to cover a wide range of +possible song features across different species. Gabor functions approximate +the general structure of the filters used in the existing framework as well as +the filter functions found in various auditory neurons~(\bcite{rokem2006spike}; +\bcite{clemens2011efficient}; \bcite{clemens2012nonlinear}). The fitted +sigmoidal nonlinearities in the existing framework consistently exhibited very +steep slopes and are therefore replaced by shifted Heaviside step-functions, +which results in a binarization of the feature detector outputs. 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. + \textbf{The role of repetitive songs for the feature representation:} Most grasshopper songs are produced by stridulation, which refers to the pulling of the serrated stridulatory file on the hindlegs across a resonating