Figure 12. Comparing model error and half-bandwidth be- tween all-data and leave-one-out models. A. Distribution of the ratios of the widths of the adaptation channel derived from a model for which responses to one condition were left out of the training data, and the width derived from a model that was fitted to all the data. The colors indicate the left-out condition. B. Distribution of ratios between the sum of squared errors of the model with the corresponding condition left out and the sum of squared errors of the model that was fitted to all the data (sums of squared error computed for the training data only). doi:10.1371/journal.pone.0023369.g012 opposite effect was evident when removing the Deviant-alone or the Diverse-broad responses from the training data, resulting in an overall decrease of the bandwidth of the adaptation channel. Such consistent effects were not seen for any of the other conditions. The mean error over the training data was lower, as expected, when part of the data was left out (Fig. 12B). The two conditions that reduced the error most when left out, relative to the fit using the full data, were the Deviant and Diverse-broad conditions. In fact, the reduction in error when removing the Deviant responses from the fit was if anything larger than when removing the Diverse-broad responses (one-tailed t-test, p,0.05 for LFP, ns for MUA). The predictions of the leave-one-out procedures showed some consistent bias relative to the actual measurements. A 2-way ANOVA on condition and Df confirmed that the bias showed a significant effect of condition (LFP: F(5,1735) = 31, p,0; MUA: F(5,615) = 12,p,0), a non-significant main effect of Df (LFP: F(3,1735) = 0.99, n.s.; MUA: F(3,615) = 0.95, n.s.), but a highly significant interaction between condition and Df (LFP: F(15,1735) = 4.9, p,0; MUA: F(15,615) = 2.38, p = 0.0024). Figure 13 compares the average measured responses with the predicted responses based on each of the leave-one-out models for the LFP responses. The general pattern of results is very similar for the MUA models. In each panel, the full-colored lines compare the measured (thin line) and predicted (thick line) responses to one left- out condition (indicated both in the title of the panel and through Deviance Detection in Rat Auditory Cortex the color code). The pastel-colored lines show the same information for the five conditions that have been used to estimate the parameter of the model. As a rule, these responses are well- estimated, as expected from the ability of the model to account well for the data. Taking out the responses in the Equal condition (Fig. 13B) and the diverse narrow condition (Fig. 13C) did not affect much the predictions, which fitted the measured responses very well on average. Taking out the Standard responses from the training set (Fig. 13A) resulted in over-prediction of these responses (thick blue line, representing the predictions, is overall above the thin blue line, representing the average responses). Conversely, taking out the Deviant alone responses from the training set resulted in predictions that were on average too small (Fig. 13F). Most importantly, there were consistent consequences to removing the Deviant or the Diverse-broad responses from the training data. When the Deviant condition was left out, the resulting predictions of the responses in the left-out Deviant condition were consistently smaller than the actual responses (Fig. 13D, compare thin and thick red lines). On the other hand, when the Diverse-broad responses were left out, the predictions of the responses in the left-out Diverse-broad condition were consistently too high (Fig. 13E, compare thin and thick yellow lines). Discussion Summary of the results and relationships with previous studies In this paper we studied the responses to low probability sounds in the auditory cortex of halothane-anaesthetized rats and the influence of the auditory context on these responses. The strength of the response to a given frequency depended on its probability of appearance in the sequence, as well as on the frequency composition of the rest of the sequence and on the ISI. Thus, when a frequency appeared in 5% of the tone presentations the responses were stronger than when it appeared in 95% of the tone presentations in 2-tone sequences (Figs. 1–7). This effect was large and robust for frequency differences of 21% and more, and significant at 10% (Figs. 6,7). Furthermore, responses depended on tone probability even with ISIs as long as 1800 ms, although this effect decreased somewhat at the longest ISIs (Fig. 8). As a result, as long as the frequency separation between nearby tones was larger than about 20%, tones in low probability conditions elicited large responses (Deviant-alone, Deviant and Diverse-broad conditions). In contrast, high probability conditions (Standard and Equal-probability) gave rise to smaller responses. Even in a low probability condition, significant reduction in the responses could occur when the frequencies in the sequence were densely spaced (Diverse-narrow condition). This pattern of results was apparent in both LFP and MUA. These results are consistent with previous studies of oddball responses. Ulanovsky et al. [5,6] studied the responses of single neurons in cat auditory cortex to oddball sequences. The results of these papers are similar to those presented here, although the tone duration was 230 ms, substantially longer than the 30 ms used here. The ISI in the main data of the papers of Ulanovsky and colleagues, 730 ms, lies within the range of ISIs in which we showed significant effects, and the longest ISI at which they found significant effects was 2 s (while here we show significant responses at 1.8 s, but didn’t test longer ISIs). Lazar and Metherate [20] demonstrated SSA in epidural potentials recorded from rat auditory cortex with Df of 100% and higher, but had full cross-frequency adaptation (‘spectral PLoS ONE | www.plosone.org 12 August 2011 | Volume 6 | Issue 8 | e23369