The raw neural signal was amplified (1,000×–10,000×) and band-pass filtered (1 Hz–15 kHz). Multiunit activity was recorded from up to four sites
from each bird over 4–6 weeks. Because multiday stability of the recordings was crucial for our analysis, all subsequent analysis was done on buy RG7204 data collected from the most stable recording site in each bird. All song and HVC recording analysis was performed offline using custom-written software (LabVIEW and MATLAB). Songs were sampled at 44.15 kHz and band-pass filtered (0.3–7 kHz). The dominant song motif for each bird was determined by visual inspection. Once a motif was chosen, it was identified in the sound recordings using a semiautomated routine, which included visual inspection of the segmented songs to verify that they indeed matched the chosen motif. These segmented motifs constituted the data for subsequent analysis. Song analysis was done on catch trials, i.e., songs recorded with the CAF protocol turned off, in the early morning (a.m. session) and evening (p.m. session). Approximately 100–200 songs/day were analyzed for each bird. Baseline data were analyzed for ∼200 songs recorded 1–2 days before the start of CAF at comparable times to the CAF catch trials. Pitch estimates for the catch trials were calculated as described in
Supplemental Experimental Procedures. Since pitch can be defined robustly only for harmonic Selleckchem Y27632 stacks, we computed pitch variability for harmonic stack syllables in birds that had them. If a bird did not have any harmonic stack syllable, we analyzed pitch variability in a subsyllabic harmonic stack (see the latter half of syllable S4 in Figure 1F for an example). Offline duration estimates from the catch trials were obtained by dynamically time warping (DTW) the songs to an average template (Glaze and Troyer, 2006). We implemented our DTW algorithm on spectrograms, using the L2-norm of the difference in the log-transformed
secondly spectrogram at each time point as the local distance metric. Slopes of the warping paths were constrained to be between 0.5 and 2. Template start and end points were not constrained to align to the start and end points in the rendition. For details on how interval durations were estimated using DTW, see Supplemental Experimental Procedures. Temporal variability in interval (i.e., syllable and gap) durations was estimated as described previously (Glaze and Troyer, 2012). Briefly, rendition-to-rendition variability of interval durations in the song was parsed into local, global, and jitter components by factor analysis. Local variability refers to independent variations in interval lengths, global variability captures correlated variability across intervals (due to e.g., temperature [Aronov and Fee, 2012 and Long and Fee, 2008] or circadian [Glaze and Troyer, 2006] effects), and jitter is the variance in determining an interval’s boundary.