Critical Song Features for Auditory Pattern Recognition in Crickets
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{"title"=>"Critical Song Features for Auditory Pattern Recognition in Crickets", "type"=>"journal", "authors"=>[{"first_name"=>"Gundula", "last_name"=>"Meckenhäuser", "scopus_author_id"=>"55605402500"}, {"first_name"=>"R. Matthias", "last_name"=>"Hennig", "scopus_author_id"=>"7005302364"}, {"first_name"=>"Martin P.", "last_name"=>"Nawrot", "scopus_author_id"=>"7004343639"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"368400202", "doi"=>"10.1371/journal.pone.0055349", "isbn"=>"0011-3891", "pmid"=>"1000090690", "issn"=>"19326203", "scopus"=>"2-s2.0-84874245179", "arxiv"=>"1203.2655", "sgr"=>"84874245179"}, "id"=>"2848a90d-c9ea-37a8-97fd-013a22e5bfc8", "abstract"=>"Many different invertebrate and vertebrate species use acoustic communication for pair formation. In the cricket Gryllus bimaculatus, females recognize their species-specific calling song and localize singing males by positive phonotaxis. The song pattern of males has a clear structure consisting of brief and regular pulses that are grouped into repetitive chirps. Information is thus present on a short and a long time scale. Here, we ask which structural features of the song critically determine the phonotactic performance. To this end we employed artificial neural networks to analyze a large body of behavioral data that measured females’ phonotactic behavior under systematic variation of artificially generated song patterns. In a first step we used four non-redundant descriptive temporal features to predict the female response. The model prediction showed a high correlation with the experimental results. We used this behavioral model to explore the integration of the two different time scales. Our result suggested that only an attractive pulse structure in combination with an attractive chirp structure reliably induced phonotactic behavior to signals. In a further step we investigated all feature sets, each one consisting of a different combination of eight proposed temporal features. We identified feature sets of size two, three, and four that achieve highest prediction power by using the pulse period from the short time scale plus additional information from the long time scale.", "link"=>"http://www.mendeley.com/research/critical-song-features-auditory-pattern-recognition-crickets", "reader_count"=>27, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Librarian"=>1, "Researcher"=>6, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>1, "Student > Master"=>4, "Other"=>1, "Student > Bachelor"=>4, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Librarian"=>1, "Researcher"=>6, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>1, "Student > Master"=>4, "Other"=>1, "Student > Bachelor"=>4, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Agricultural and Biological Sciences"=>12, "Medicine and Dentistry"=>1, "Philosophy"=>1, "Neuroscience"=>1, "Arts and Humanities"=>1, "Psychology"=>1, "Chemistry"=>1, "Social Sciences"=>1, "Computer Science"=>5}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>12}, "Computer Science"=>{"Computer Science"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Denmark"=>1, "Switzerland"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/956950"], "description"=>"<p>Typically, a calling song consists of repetitive pulses that are grouped into chirps. The temporal structure of an artificial song pattern is fully determined by four descriptors, e.g. the duration and pause for both pulses and chirps. Four additional descriptors are frequently used to characterize cricket songs, namely the period (the sum of duration and pause), and the duty cycle (the ratio of duration and period) for both, the short and the long time scale.</p>", "links"=>[], "tags"=>["cricket", "temporal"], "article_id"=>627208, "categories"=>["Biological Sciences", "Neuroscience", "Evolutionary Biology"], "users"=>["Gundula Meckenhäuser", "R. Matthias Hennig", "Martin P. Nawrot"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055349.g001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Artificial_song_pattern_of_the_cricket_Gryllus_bimaculatus_and_its_temporal_features_/627208", "title"=>"Artificial song pattern of the cricket <i>Gryllus bimaculatus</i> and its temporal features.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:30:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/956958"], "description"=>"<p>(A) Sketch of a logical AND-operation (central square) and an OR-operation (gray shading). (B) Chirp period - pulse period response field predicted by the best 4-feature model (pulse duration, pulse pause, chirp duration, chirp period). The dominant circular area of highest response values suggests an AND-operation. Circles indicate experimentally measured phonotactic scores.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "Evolutionary biology"], "article_id"=>627214, "categories"=>["Biological Sciences", "Neuroscience", "Evolutionary Biology"], "users"=>["Gundula Meckenhäuser", "R. Matthias Hennig", "Martin P. Nawrot"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055349.g003", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Interaction_of_the_short_and_long_time_scale_/627214", "title"=>"Interaction of the short and long time scale.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:31:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/956954"], "description"=>"<p>(A) The network diagram consists of four input neurons representing temporal calling song features, which project to input-evaluating neurons in the hidden layer. These in turn project to the output neuron mimicking the relative phonotactic score; abbreviations: Pdur - pulse duration, Ppau - pulse pause, Cdur - chirp duration, Cper - chirp period. (B) Correlation between the phonotactic score of 18 test samples predicted by the best 4-feature model and the experimentally measured scores. Each dot shows the mean phonotactic score for a given song pattern that was presented to on average 31 females and tested for 100 times with the model. The errorbars indicate standard deviation across individual females (horizontal) and across 100 repeated model simulations (vertical). The solid regression line has a slope of 0.73. The performance: and .</p>", "links"=>[], "tags"=>["diagram", "predictive", "4-feature"], "article_id"=>627211, "categories"=>["Biological Sciences", "Neuroscience", "Evolutionary Biology"], "users"=>["Gundula Meckenhäuser", "R. Matthias Hennig", "Martin P. Nawrot"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055349.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_diagram_and_predictive_performance_of_the_best_4_feature_model_/627211", "title"=>"Network diagram and predictive performance of the best 4-feature model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:31:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/956967"], "description"=>"<p>(A) The pulse response field of the best 4-feature model shows highest phonotactic scores for patterns that are accumulated in an oval bounded by pulse periods of 30 and 45 ms and pulse duty cycles of 0.4 and 0.7. The pulse response fields of the best 3-feature model (B) and the best 2-feature model (C) are clearly independent of the pulse duty cycle and show an extension on the diagonal defined by a pulse period of 40 ms. The chirp response field of the best 4-feature model (D) and the best 3-feature model (E) are qualitatively similar and reveal best scores for patterns with chirp durations and pauses between 200 and 300 ms. The best 2-feature model predicts highest scores for patterns with a chirp pause between 100 and 250 ms, irrespective of the chirp duration (F). Circles indicate experimentally measured phonotactic scores.</p>", "links"=>[], "tags"=>["chirp", "fields", "3-feature", "2-feature"], "article_id"=>627220, "categories"=>["Biological Sciences", "Neuroscience", "Evolutionary Biology"], "users"=>["Gundula Meckenhäuser", "R. Matthias Hennig", "Martin P. Nawrot"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055349.g005", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pulse_and_chirp_response_fields_predicted_by_the_best_4_feature_3_feature_and_2_feature_model_/627220", "title"=>"Pulse and chirp response fields predicted by the best 4-feature, 3-feature and 2-feature model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:32:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/956961"], "description"=>"<p>Model of size four (light gray), three (dark gray), and two (black edging) are ranked top ten. The overall best performing model uses the pulse period, chirp duration, and chirp duty cycle. The best 2-feature model (pulse period and chirp pause) did not perform significantly different ( for a two-sided Wilcoxon rank-sums test; significance level of 0.01). The best 4-feature model (pulse duration, pulse pause, chirp duration, and chirp period) performed significantly worse than the best 3-feature model ( for a two-sided Wilcoxon rank-sums test). Abbreviations: Pdur - pulse duration, Ppau - pulse pause, Pper - pulse period, Pdc - pulse duty cycle, Cdur - chirp duration, Cpau - chirp pause, Cper - chirp period, Cdc - chirp duty cycle. The models were validated 100 times and errorbars indicate standard deviation.</p>", "links"=>[], "tags"=>["performing"], "article_id"=>627215, "categories"=>["Biological Sciences", "Neuroscience", "Evolutionary Biology"], "users"=>["Gundula Meckenhäuser", "R. Matthias Hennig", "Martin P. Nawrot"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0055349.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ten_best_performing_models_/627215", "title"=>"Ten best performing models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:31:59"}

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