The Journal of Neuroscience, January 19, 2005, ():
Ensemble Coding of Vocal Control in Birdsong
J. Neurosci. Leonardo and Fee
25: 652
Supplemental data
Files in this Data Supplement:
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Supplementary Figure 1 – Temporal evolution of RA activity patterns and their relation to song acoustic structure. Shown in this figure is the analysis of Figure 6, but carried out with a set of univariate acoustic features instead of the song spectrogram. A) Spectrogram (top) and set of univariate acoustic features (bottom) of the song of Bird 9. The song is divided into two sections, one with identically repeated syllables (d2-d3 — green bar), and another with different syllables that contain regions with similar subsyllables (a-b-c-d1 — red bar). B,C) Acoustic feature correlation matrix and neural correlation matrix for syllables a-b-c-d1. D) Conditional probability distribution of neural correlations at different levels of acoustic feature correlation. Each column represents the distribution of neural correlations associated with the level of acoustic correlation indicated on the x-axis. E,F,G) Song correlation matrix, neural correlation matrix, and conditional probability distribution of neural correlations for syllables d2-d3. During repeated syllables, the highly correlated sounds are associated with highly correlated neural ensembles. In contrast, during the production of different syllables with similar subsyllables, the highly correlated sounds are associated with uncorrelated neural ensembles. H) Average neural correlation as a function of song correlation for each bird for the portion of the song motif containing repeated subsyllables but not repeated syllables. J) Average neural correlation as a function of song correlation for repeated syllables d2-d3 in Bird 9, and for repeated song motifs in Bird 12.
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