adaptation occurs within channels extending about 1/3 octaves on either side of the center frequency. Adaptation in narrow frequency channels, however, makes two predictions that are not fully born out even in with a cursory look through the data. First, the responses in all conditions should be smaller than the responses in the Deviant-alone condition; and second, that the responses in the Deviant condition should be smaller than in the Diverse-broad condition (Figs. 9A and 9B). The failure of both predictions is illustrated in Figs. 5C and 5D. First, a substantial number of recording sites had responses in the Deviant and Diverse-broad conditions that were larger than in the Deviant-alone condition. While these deviations could be argued away as noise in the measurement of the responses, the same figures also illustrate the near-equality of the responses in the Deviant and in the Diverse-broad condition. This near-equality leads to divergent estimates of the adaptation channel when using each of the two types of responses separately (Fig. 9C). The leave-one-out procedure highlighted some additional features of the data. Four conditions resulted in consistent differences between the predictions to the left-out conditions and the actual measured responses in the same conditions. Two of these are easy to explain: leaving out the standard responses led to over-prediction of these responses, while leaving out the Deviant- alone conditions led to under-prediction of these responses. These effects presumably occurred due to the need to extrapolate the extreme responses (smallest response in the case of the Standard, largest in the case of the Deviant alone) based on the other responses, leading to ‘regression to the mean’ of the predicted responses. The reduction in the predicted responses to the Deviant-alone condition accounts for the effects of removing the Deviant-alone conditions from the training data on the estimated bandwidth (Fig. 12A). It is easy to see that these responses are an increasing function of the scale A and a decreasing function of the bandwidth s. Thus, under-prediction of A (which is roughly the response in the Deviant-alone condition) requires a decrease in the bandwidth s in order to account for the responses that remained in the training set. More interestingly, leaving out the Deviant responses resulted in their under-estimation (Fig. 13D), but also to a significant increase in the estimated bandwidth of the models (Fig. 12A) and the largest reduction in the error of the fit among all left-out conditions for the LFP data (Fig. 12B). For the MUA data, the median reduction in the error was larger in the Diverse-broad condition, although non-significantly so; on the other hand, the 75% percentile of the error reductions of the Diverse-broad left-out models was much smaller than that of the Deviant left-out models. Removing the Diverse-broad responses resulted in the opposite trend: responses were over-predicted and the estimated bandwidth of the models became narrower. Thus, it seems that the Deviant responses are somehow inconsistent with the rest of the data – removing them modified significantly the main parameter of the model, the bandwidth of cross-frequency adaptation, and reduced substantially the error of the fits. At the root of this inconsistency lies the fact that the responses to the deviant and diverse-broad conditions had about the same strength, but that both were typically somewhat smaller than the responses in the Deviant-alone condition. These findings are hard to accommodate in a model based on adaptation in narrow frequency channels (Fig. 9). The contradiction between these two requirements is reflected in the predictions of the left-out conditions. In a model that was fitted without the Diverse-broad responses, there was a tendency of the estimated width of cross-frequency adaptation to be narrower, pulled in this direction by the inclusion of the Deviant Deviance Detection in Rat Auditory Cortex responses in the training data. The reduction in the width of the adaptation channel resulted in predictions of the Diverse-broad responses that were too large, since most stimulus presentations in this condition fell outside the effective width of cross-frequency adaptation. The concomitant decrease in adaptation bandwidth and over-prediction of the Diverse-broad responses can be observed in Fig. 12A and Fig. 13E, respectively. The opposite tendencies can be observed in the models in which the Deviant responses were excluded from the training data. The adaptation bandwidth was pulled to larger values by the reduction in the Diverse-broad responses relative to the Deviant-alone responses. As explained above, a wider adaptation bandwidth results in under-predictions of the deviant responses. The two effects can be observed in Figs. 12A and 13D. SSA, deviance detection and Mismatch Negativity The inconsistency between the responses in the Deviant and in the Diverse-broad conditions suggests that somewhat different mechanisms operate in these two conditions. The Diverse-broad condition is (almost) symmetric with respect to all tone frequencies that appear in it; this is true also for the Diverse-narrow and Equal conditions. Thus, it makes sense to hypothesize that it is the composition of the oddball sequence, with its large asymmetry between the probabilities of the two tones, which engages special mechanisms. This assumption is further supported by Fig. 12B: the error of the fit was reduced more when removing the Deviant responses from the training data than when removing the responses to other conditions (at least for the LFP data, and for the more extreme cases of the MUA data), hinting at the fact that the Deviant responses are special. The contribution of the hypothetical mechanisms that are engaged in the Deviant condition to the response is large: adaptation in narrow frequency channels, fitted to all other conditions, accounted for only half of the average increase in Deviant relative to the Standard responses (Fig. 12D, compare thin and thick red lines). Although we do not know what are the additional mechanisms that are engaged in the Deviant condition, the model presented here suggests one candidate: a dynamic, rather than fixed, adaptation bandwidth. The hypothesis is that the repetition of the standard stimulus causes a reduction not only in the responses to the standard, but also in its effects on the responses to other stimuli, causing the adaptation channel centered on the deviant frequency to become narrower. As a result, the deviant stimulus elicits similar responses to those of a stimulus with the same probability in the Diverse-broad condition, where such narrowing of the adaptation channel does not occur, but where the adaptation channel is stimulated much less. The enhanced responses in the Deviant condition may be interpreted as signaling the detection of the change, or deviance, that a deviant tone represents relative to the regularity set by the standard. An important component of the auditory ERPs, the MMN, is believed to signal deviance detection in humans. There are many similarities between SSA in auditory cortex and MMN [13], suggesting that SSA lies upstream of MMN generation, although the early timing of SSA in auditory cortex indicates that the responses studied here are not those that directly cause the currents which are measured as MMN on the scalp. Furthermore, Farley et al. [12] failed to produce SSA in response to level deviants (but see [6]) and duration deviants, and showed that SSA in auditory cortex does not depend on NMDA receptors, while MMN does. These results emphasize the distance that separates SSA in auditory cortex and MMN. In contrast with the negative results of Farley and co-workers, we demonstrate here that SSA in auditory cortex has one of the PLoS ONE | www.plosone.org 14 August 2011 | Volume 6 | Issue 8 | e23369