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\field{labeltitlesource}{title} + \field{journaltitle}{Front Physiol} + \field{title}{Time and timing in the acoustic recognition system of crickets} + \field{volume}{5} + \field{year}{2014} + \field{pages}{286} + \range{pages}{1} + \endentry \entry{hildebrandt2009origin}{article}{} \name{author}{3}{}{% {{un=0,uniquepart=base,hash=926054ddd1bb3cdee2f0d2f723c64e9d}{% diff --git a/main.bcf b/main.bcf index 3e84f50..615f3a6 100644 --- a/main.bcf +++ b/main.bcf @@ -2405,26 +2405,34 @@ clemens2013computational clemens2013feature ronacher2015computational - clemens2013computational - clemens2013feature - ronacher2015computational - rehbein1974structure - kalmring1975afferent - rehbein1976auditory - eichendorf1980projections - clemens2011efficient - clemens2011efficient - clemens2011efficient - michelsen1971frequency - windmill2008time - malkin2014energy - machens2001discrimination - machens2001representation - suga1960peripheral - gollisch2002energy - hildebrandt2009origin - clemens2010intensity - fisch2012channel + clemens2013feature + clemens2013computational + clemens2013computational + clemens2013feature + hennig2014time + clemens2011efficient + clemens2013computational + clemens2013feature + clemens2013computational + clemens2013feature + ronacher2015computational + rehbein1974structure + kalmring1975afferent + rehbein1976auditory + eichendorf1980projections + clemens2011efficient + clemens2011efficient + clemens2011efficient + michelsen1971frequency + windmill2008time + malkin2014energy + machens2001discrimination + machens2001representation + suga1960peripheral + gollisch2002energy + hildebrandt2009origin + clemens2010intensity + fisch2012channel diff --git a/main.blg b/main.blg index 7869147..3811184 100644 --- a/main.blg +++ b/main.blg @@ -1,61 +1,61 @@ [0] Config.pm:307> INFO - This is Biber 2.19 [0] Config.pm:310> INFO - Logfile is 'main.blg' -[36] biber:340> INFO - === Fr Jan 16, 2026, 15:46:34 +[37] biber:340> INFO - === Do Jan 22, 2026, 16:53:42 [44] Biber.pm:419> INFO - Reading 'main.bcf' -[72] Biber.pm:979> INFO - Found 49 citekeys in bib section 0 -[78] Biber.pm:4419> INFO - Processing section 0 -[82] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0 -[83] bibtex.pm:1713> INFO - LaTeX decoding ... -[112] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib' -[266] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable' -[266] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized' -[266] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US' -[266] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US' -[286] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8' -[295] bbl.pm:763> INFO - Output to main.bbl -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 10, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 21, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 38, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 49, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 58, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 73, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 114, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 123, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 132, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 153, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 174, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 192, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 203, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 214, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 223, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 232, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 243, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 252, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 263, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 274, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 283, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 310, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 354, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 373, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 400, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 409, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 428, warning: 6 characters of junk seen at toplevel -[295] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_IuZr/347c261ec4135a5723bef5c751f5078f_15189.utf8, line 455, warning: 6 characters of junk seen at toplevel -[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 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file:line:error style messages enabled. @@ -636,54 +636,64 @@ LaTeX Font Info: Trying to load font information for U+msb on input line 33. 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[] + +Overfull \hbox (3.27615pt too wide) in paragraph at lines 96--127 +\OT1/cmr/m/n/12 di-rec-tional hear-ing ([], [], + [] + [1 -{/var/lib/texmf/fonts/map/pdftex/updmap/pdftex.map}] [2] [3] -Overfull \hbox (13.04964pt too wide) in paragraph at lines 270--272 +{/var/lib/texmf/fonts/map/pdftex/updmap/pdftex.map}] +Overfull \hbox (0.27292pt too wide) in paragraph at lines 131--188 +\OT1/cmr/m/n/12 and by dif-fer-ent mech-a-nisms ([]). Ap-prox-i-mat-ing this pro-cess within + [] + +[2] [3] [4] +Overfull \hbox (13.04964pt too wide) in paragraph at lines 348--350 []\OT1/cmr/m/n/12 Similar re-sponse/filter prop-er-ties within re-cep-tor/interneuron pop-u-la-tions ([]Clemens, Kutzki, [] -[4] (./figures/fig_auditory_pathway.pdf_tex - +[5] (./figures/fig_auditory_pathway.pdf_tex + File: figures/fig_auditory_pathway.pdf Graphic file (type pdf) Package pdftex.def Info: figures/fig_auditory_pathway.pdf , page1 used on input line 56. (pdftex.def) Requested size: 455.24411pt x 378.38292pt. -) [5 <./figures/fig_auditory_pathway.pdf>] -Overfull \hbox (40.4313pt too wide) in paragraph at lines 322--333 +) [6 <./figures/fig_auditory_pathway.pdf>] +Overfull \hbox (40.4313pt too wide) in paragraph at lines 400--411 []\OT1/cmr/m/n/12 ) and, to a lesser ex-tent, the re-cep-tors them-selves ([]) [] -[6] -Overfull \hbox (9.43962pt too wide) in paragraph at lines 391--391 +[7] +Overfull \hbox (9.43962pt too wide) in paragraph at lines 469--469 []\OT1/cmr/bx/n/17.28 Two mech-a-nisms driv-ing the emer-gence of intensity- [] -[7] [8] [9] [10] [11] (./main.aux) +[8] [9] [10] [11] [12] (./main.aux) *********** LaTeX2e <2023-11-01> patch level 1 L3 programming layer <2024-01-22> @@ -693,18 +703,18 @@ Package logreq Info: Writing requests to 'main.run.xml'. ) Here is how much of TeX's memory you used: - 17841 strings out of 474222 - 384295 string characters out of 5748732 - 1935975 words of memory out of 5000000 - 39870 multiletter control sequences out of 15000+600000 + 17850 strings out of 474222 + 384434 string characters out of 5748732 + 1936975 words of memory out of 5000000 + 39879 multiletter control sequences out of 15000+600000 567179 words of font info for 71 fonts, out of 8000000 for 9000 1141 hyphenation exceptions out of 8191 94i,19n,93p,713b,992s stack positions out of 10000i,1000n,20000p,200000b,200000s -Output written on main.pdf (11 pages, 1114236 bytes). +Output written on main.pdf (12 pages, 1117104 bytes). PDF statistics: - 1094 PDF objects out of 1200 (max. 8388607) - 694 compressed objects within 7 object streams + 1097 PDF objects out of 1200 (max. 8388607) + 696 compressed objects within 7 object streams 0 named destinations out of 1000 (max. 500000) 18 words of extra memory for PDF output out of 10000 (max. 10000000) diff --git a/main.pdf b/main.pdf index 9bfe350..cbdeda5 100644 Binary files a/main.pdf and b/main.pdf differ diff --git a/main.synctex.gz b/main.synctex.gz index fc2c571..34df5ba 100644 Binary files a/main.synctex.gz and b/main.synctex.gz differ diff --git a/main.tex b/main.tex index 30b192b..d328eec 100644 --- a/main.tex +++ b/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.