Figure 8. Responses of individual neurons to multiple song types are not explained by acoustic similarity on the scale of full syllables or individual notes. Spectrographic cross-correlation (Clark et al., 1987; Nowicki et al., 2002) was used to compare the acoustic structure of a representative syllable of each adult, tutor and novel song versus either the syllable of the strongest adult song type in each cell (A), or the syllable of the song type that evoked the strongest response in each cell, regardless of whether that song was part of the birds' adult repertoire (B). In each case, there was no difference between the cross-correlation scores of stimuli that did evoke a significant response (shaded regions in each bar) and the stimuli that did not evoke a significant response (open regions in each bar; A, p = 0.42; B, p = 0.67, Mann–Whitney U test; N = 20 cells, 6 birds). C–F, Because multiple song types could share an acoustic sequence that spanned only a subset of the song syllable, we also compared song structures at a per-note resolution. Comparing individual notes (C), two-note sequences (D), three-note sequences (E), and four-note sequences (F), there was no difference between the cross-correlation scores of the stimuli that did evoke a significant response (shaded regions in each bar) versus the stimuli that did not evoke a significant response (open regions in each bar; C, p = 0.28; D, p = 0.20; E, p = 0.61; F, p = 0.46, Mann–Whitney U test; N = 20 cells, 6 birds). Together, these analyses of acoustic structure, considering the same data at both a per-syllable and a per-note resolution, reveal that individual HVC neurons can represent multiple acoustically distinct song types.