sequences may be large simply because the auditory system is stimulated overall at a much slower rate than in the oddball sequence. This problem is alleviated, but not fully solved, by the ‘deviant among many standards’ control [16,17], which keeps the rate of deviant tone presentations low but replaces the standard tone presentations with a number of different stimuli, each with a low probability of appearance, thus canceling the special status of the deviant. Responses to the deviant in this case presumably reflect its rarity while controlling for the overall activation of the auditory system, and therefore serve as an appropriate baseline for establishing the presence of a component which is sensitive to deviance in the responses to deviants in the oddball sequences. Studies of human ERPs using both types of control sequences strongly suggest that the MMN is indeed an index of true deviance detection. However, the assumptions underlying both types of control sequences are not transferable in a simple way from the study of gross potentials such as MMN to neuronal responses in auditory cortex. In this study, we examined deviance detection in auditory cortex of rats. To do so, we used a variety of stimulus conditions, including the control conditions used in human experiments. These data made it possible to disentangle the effects of tone rarity and the effects of tone deviance by using a simple model that fitted well the responses. The main result of the paper is the demonstration of a response component that is due specifically to deviance detection in rat auditory cortex. Results We presented sound stimuli to the right ear of halothane anaesthetized rats and recorded local field potentials (LFP) and extracellular spiking activity from left auditory cortex using tungsten electrodes. Based on the responses to pure tones spanning the range of 1–64 kHz, two frequencies evoking large responses were selected for further study. The lower frequency was denoted f1, the higher was denoted f2, and they were selected such that the frequency difference between them, defined as Df = (f22f1)/f1, was 10%, 21%, 44% or 96%. These values correspond to 0.141, 0.275, 0.526 and 0.971 of an octave, respectively. We presented f1 and f2 in oddball sequences (as in [6]), but we also embedded them in several control sequences that have not been used in previous studies of SSA in animals. For the sake of clarity, we first discuss the responses to the oddball sequences at a single Df, then introduce the control sequences, and finally discuss the effects of Df and inter-stimulus interval on the responses. In order to reduce ambiguity, the word ‘frequency’ will refer in this paper only to the acoustic frequency of the pure tones. Other meanings of ‘frequency’ will be designated by ‘rate’ (of stimulus presentation) or ‘probability of occurrence’ (of a tone of a given frequency within a test sequence). Stimulus-specific adaptation The oddball sequences consisted of 500 pure tone beeps (30 ms duration, 5 ms rise/fall time), presented at an inter-stimulus interval (ISI) of 300 ms unless explicitly stated otherwise. Two oddball sequences were used. In one sequence, 95% of the stimuli, at random, had frequency f1, and the other 5% had frequency f2, so that f1 was frequent (‘standard’) while f2 was rare (‘deviant’). This sequence is called ‘Deviant f2’. To determine whether the difference in the responses to the standard and to the deviant tones was due to their different probability of presentation or to the frequency difference between them, we used another sequence in which the roles of the two frequencies were switched, so that f1 was deviant and appeared in 5% of the trials, while f2 was the standard. This sequence is called ‘Deviant f1’. For comparison, we Deviance Detection in Rat Auditory Cortex presented also a third sequence in which the two frequencies appeared with equal probability (half of the tone presentations each, randomly). Figure 1A describes schematically the two oddball sequences, as well as the equal-probability sequence. Figures 1B and 1C display the average LFP as well as the multiunit activity (MUA) recorded simultaneously from a typical recording site (f1 = 13.3 kHz, f2 = 19.2 kHz, corresponding to Df of 44%, 70 dB SPL). In the ‘Deviant f1’ sequence, the LFP response to f1 (the deviant) was considerably larger than the response to f2 (the standard). On the other hand, in the ‘Deviant f2’ sequence, the response to f1 (now the standard) was smaller than the response to f2. The LFP responses to the two frequencies in the equal-probability sequence were comparable, and matched the corresponding responses to the same tones when standard, but were substantially smaller than the responses to the same tones when deviant. Thus, the LFP responses were similarly depressed by tones with probabilities of occurrence of 50% and 95%, but this depression did not generalize to other tones with probability of occurrence of 5%, at least for Df = 44%. In order to quantify the effect of presentation probability on tone response we used the contrast between the responses to the same frequency when it was standard and when it was deviant, called the ‘stimulus-specific adaptation index’ or SI [5]: SI1~ d(f 1){s(f1) d(f 1)zs(f1) ; SI2~ d(f 2){s(f 2) d(f 2)zs(f 2) where d(fi) and s(fi) represent the peak responses to frequency fi when it was deviant and standard, respectively (see Methods for details of the measurement of peak responses for LFP and multiunit activity). For the LFP responses in Fig. 1B, SI1 was 0.27, and SI2 was 0.35. Both contrasts were positive, demonstrating the appreciable effect of stimulus probability on the sensory responses. The MUA responses measured at the same site are displayed in Fig. 1C. Similarly to the LFP, the MUA response to each of the tones was smaller when standard than when deviant. Remarkably, while in the equal-probability sequence the MUA response evoked by f2 was substantially weaker than that evoked by f1, in the Deviant f2 sequence the MUA evoked by the deviant f2 was actually larger than that evoked by the standard f1, the opposite of what one would predict from the frequency selectivity of this site. Figure 2A shows the frequency-specific contrasts between the standard and the deviant responses, SI1 and SI2, for all the recording sites tested with Df = 44%. As shown in Ulanovsky et al. [5], if the change in response size were due to a general decrease in the excitability of the neural signal (‘fatigue’), data would have SI1+SI2 = 0, corresponding to points along the reverse diagonal. In fact, in virtually all cases not only SI1+SI2.0 held, but also both SI1 and SI2 were individually positive. Thus, the example in Fig. 1C is typical. The common contrast between the deviant and standard responses was used to characterize the average effect of adaptation for this specific pair of frequencies [6,10]: CSI~ d(f 1)zd(f2){s(f 1){s(f2) d(f 1)zd(f2)zs(f 1)zs(f2) For the responses in Fig. 1, CSI = 0.31. The distribution of the common contrast CSI for all the data with Df = 44% is shown in PLoS ONE | www.plosone.org 2 August 2011 | Volume 6 | Issue 8 | e23369