2016
DOI: 10.1016/j.jneumeth.2016.06.023
|View full text |Cite
|
Sign up to set email alerts
|

A quantitative method for analyzing species-specific vocal sequence pattern and its developmental dynamics

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
20
1

Year Published

2018
2018
2024
2024

Publication Types

Select...
7

Relationship

6
1

Authors

Journals

citations
Cited by 13 publications
(27 citation statements)
references
References 32 publications
0
20
1
Order By: Relevance
“…We found a statistically significant difference in chrm2 expression levels among the six species ( Figure b, Kruskal–Wallis test, *** p < .0001). Although these songbird species exhibit species‐unique vocal patterns (Figure a) (Imai et al, ), we could not detect an apparent link between song phenotypes, particularly the syllable sequence and chrm2 expression in HVC. For example, although both CN and OF produce repetitive sequence‐based song patterns, chrm2 expression in HVC was high in OF but suppressed in CN.…”
Section: Resultscontrasting
confidence: 62%
“…We found a statistically significant difference in chrm2 expression levels among the six species ( Figure b, Kruskal–Wallis test, *** p < .0001). Although these songbird species exhibit species‐unique vocal patterns (Figure a) (Imai et al, ), we could not detect an apparent link between song phenotypes, particularly the syllable sequence and chrm2 expression in HVC. For example, although both CN and OF produce repetitive sequence‐based song patterns, chrm2 expression in HVC was high in OF but suppressed in CN.…”
Section: Resultscontrasting
confidence: 62%
“…First, we compared the song features of ZF and OF reared with conspecific song tutoring in our breeding colony to confirm whether a laboratory-controlled environment could maintain species-specific song features. We compared the songs of the two species regarding syllable acoustics and sequential features (12 parameters) at the adult stage ( Fig 2A ) and identified significant differences in six acoustic syllable parameters (i.e., syllable duration, inter-syllable gap duration, entropy variance, amplitude modulation [AM] variance, mean frequency modulation [FM], and FM variance) and in syllable sequence features (motif and repetition transition rates) ( n = 6 birds each, p < 0.01, one-way ANOVA) ( Fig 2B and 2C and S1 Fig ) [30, 31]. We found that the range but not the pattern of each acoustic feature’s distribution overlapped between ZFs and OFs (3,000 syllables from n = 6 birds each and 500 syllables/bird) ( S1 Fig ), thus suggesting that the species differences in the syllable acoustics were not caused by physical species-specific constraints in the peripheral vocal organs.…”
Section: Resultsmentioning
confidence: 99%
“…Statistical analysis was performed on these acoustic features between ZF and OF by one-way ANOVA. For the analysis of the sequence feature of songs (motif and repetition rates in a song), a syllable similarity matrix (SSM) analysis was performed following a previously reported method [30] ( S1 Fig ). This method calculates the contiguous syllables transition frequency of “paired (motif)” and “repetitive” syllables transitions in the songs.…”
Section: Methodsmentioning
confidence: 99%
“…Introductory notes in a song were not included for analyses. The series of separated syllable files of songs were transferred to the CORRELATOR program of Avisoft SASLab pro (Avisoft Bioacoustics, Berlin, Germany) for calculating the similarity scores between the syllables from pupils’ and tutors’ songs by the round-robin comparison [ 73 ]. The highest similarity score for each syllable of pupil songs against tutor syllables was averaged as the similarity score of total syllables for each individual.…”
Section: Methodsmentioning
confidence: 99%