Deviants are stimuli that violate one's prediction about the incoming stimuli. Studying deviance detection helps us understand how nervous system learns temporal patterns between stimuli and forms prediction about the future. Detecting deviant stimuli is also critical for animals' survival in the natural environment filled with complex sounds and patterns. Using natural songbird vocalizations as stimuli, we recorded multi-unit and single-unit activity from the zebra finch auditory forebrain while presenting rare repeated stimuli after regular alternating stimuli (alternating oddball experiment) or rare deviant among multiple different common stimuli (context oddball experiment). The alternating oddball experiment showed that neurons were sensitive to rare repetitions in regular alternations. In the absence of expectation, repetition suppresses neural responses to the 2nd stimulus in the repetition. When repetition violates expectation, neural responses to the 2nd stimulus in the repetition were stronger than expected. The context oddball experiment showed that a stimulus elicits stronger neural responses when it is presented infrequently as a deviant among multiple common stimuli. As the acoustic differences between deviant and common stimuli increase, the response enhancement also increases. These results together showed that neural encoding of a stimulus depends not only on the acoustic features of the stimulus but also on the preceding stimuli and the transition patterns between them. These results also imply that the classical oddball effect may result from a combination of repetition suppression and deviance enhancement. Classification analyses showed that the difficulties in decoding the stimulus responsible for the neural responses differed for deviants in different experimental conditions. These findings suggest that learning transition patterns and detecting deviants in natural sequences may depend on a hierarchy of neural mechanisms, which may be involved in more complex forms of auditory processing that depend on the transition patterns between stimuli, such as speech processing.

visualized with an epifluorescence microscopy. Figure 1B shows the location of recording probes along with the two main structures of the auditory forebrain: field L2 and Caudomedial Nidopallium (NCM). Statistical Analyses. In the analysis using surprise index (SI), each sample is one site/unit and different statistical tests were used for comparisons based on MUA and SUA data. For MUA, we performed within-subject comparisons because comparisons are mostly within the same group of electrodes; for SUA, we performed between-subject comparisons because spike sorting was done separately for different experimental conditions and units from different conditions cannot be guaranteed to be the same. For within-subject comparisons, we used the paired sample t-test; for between-subject comparisons, we used the independent sample t-test; for comparisons with hypothesized population means, we used the one sample t-test. When the normality assumption was not met (Shapiro-Wilk test), corresponding non-parametric statistical tests (Wilcoxon test and Mann Whitney U-test) were used. The significance level was set at 0.05 (Bonferroni adjusted p-values were reported in the context oddball analysis using SI as 7 comparisons were conducted). Because we did not find any significant differences between the left and right hemisphere (t = 0.044, p = 0.965; generalized linear mixed model, with hemisphere as fixed effects and other factors as random effects), results were pooled together in all analyses. All analyses were conducted using customized scripts in Spike2, Matlab, R, and Python.

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