frequency response in the level at which the main paradigm would be presented. Once the recording site was characterized in terms of the best frequency and the minimum threshold, two frequencies, f1 and f2, were selected for the main experimental paradigm. Both tone had to be within the FRA, usually close and symmetric around the best frequency, and to have about the same response amplitude. The possible values of the difference between the two frequencies, defined as: Df = f2/f121, were 10%, 21%, 44% or 96%. We tested the responses to these frequencies in sets of up to seven different sequences. In most cases, each sequence consisted of 30 ms tone beeps with ISI (onset to onset) of 300 ms. A limited amount of data were recorded using sequences with ISIs of 500, 700, 1000, 1200 and 1800 ms. Each set of sequences was presented at a constant sound level, 20 – 40 dB above the minimum threshold of the recording site. The sequences are described in details in the Results section and in Figs. 1A and 3A. Each set of the seven sequences tested the responses to f1 and f2 in six different conditions. In order to get comparable data from many paradigms in the same recording site, the sequences had to be as short as possible. Preliminary experiments showed that 25 presentations were enough to estimate an average response with a reasonable signal to noise ratio. Therefore, with the lowest presentation probability of each frequency being 5%, we used sequences of 500 tone beeps. Data Analysis The data were analyzed with Matlab (The Mathworks, Inc., Natick, MA). To analyze LFP, all the responses to each frequency in a sequence were aligned on stimulus onset, and each was baseline-corrected by subtracting its average during the 5 ms interval starting at stimulus onset (response onset latency was always longer than 8 ms, and we wanted to adjust the baseline as close to the response onset as possible in order to avoid influence of slow waves in the signal). Response strength was quantified by the depth of the maximal (most negative) trough of the average response in the interval 0–70 ms after stimulus onset. We used a long time interval relative to stimulus duration (70 ms compared with 30 ms) because there was a tendency for adapted responses to be also delayed in time. The variability of the response was References 1. Winkler I, Denham SL, Nelken I (2009) Modeling the auditory scene: predictive regularity representations and perceptual objects. Trends Cogn Sci 13: 532–540. 2. Winkler I, van Zuijen TL, Sussman E, Horvath J, Naatanen R (2006) Object representation in the human auditory system. European Journal of Neuroscience 24: 625–634. 3. Picton TW, Woods DL, Proulx GB (1978) Human auditory sustained potentials. II. Stimulus relationships. Electroencephalogr Clin Neurophysiol 45: 198–210. 4. Sams M, Alho K, Naatanen R (1983) Sequential effects on the ERP in discriminating two stimuli. Biol Psychol 17: 41–58. 5. Ulanovsky N, Las L, Farkas D, Nelken I (2004) Multiple time scales of adaptation in auditory cortex neurons. J Neurosci 24: 10440–10453. 6. Ulanovsky N, Las L, Nelken I (2003) Processing of low-probability sounds by cortical neurons. Nat Neurosci 6: 391–398. 7. Malmierca MS, Cristaudo S, Perez-Gonzalez D, Covey E (2009) Stimulus- specific adaptation in the inferior colliculus of the anesthetized rat. J Neurosci 29: 5483–5493. 8. Reches A, Gutfreund Y (2008) Stimulus-specific adaptations in the gaze control system of the barn owl. J Neurosci 28: 1523–1533. 9. Anderson LA, Christianson GB, Linden JF (2009) Stimulus-specific adaptation occurs in the auditory thalamus. J Neurosci 29: 7359–7363. 10. Antunes FM, Nelken I, Covey E, Malmierca MS (2010) Stimulus-specific adaptation in the auditory thalamus of the anesthetized rat. PLoS ONE 5: e14071. 11. von der Behrens W, Bauerle P, Kossl M, Gaese BH (2009) Correlating stimulus- specific adaptation of cortical neurons and local field potentials in the awake rat. J Neurosci 29: 13837–13849. 12. Farley BJ, Quirk MC, Doherty JJ, Christian EP (2010) Stimulus-specific adaptation in auditory cortex is an NMDA-independent process distinct from Deviance Detection in Rat Auditory Cortex quantified by the standard error of the mean (s.e.m.) of the baseline-corrected responses at the time of the maximal trough of the average. Only sets with clear responses and a signal to noise ratio (response size divided by standard error) larger than 2 were included in the analysis. To detect MUA, the raw signals were filtered between 200 and 8000 Hz, and large, fast events were marked as spikes. The threshold for spike detection was set to 12 times the median of the absolute deviations from the median (MAD) of the filtered voltage traces (corresponding to more than 7 standard deviations for Gaussian signals). This conservative criterion ensured that the detected events were indeed spikes and not random fluctuations of the baseline. The resulting spike trains were aligned on stimulus onset, smoothed with a 10 ms Hamming window, and averaged. MUA response amplitude was quantified by the peak response in the interval 0–70 ms after stimulus onset, without baseline corrections. Responses of MUA were included in the data analyzed here only if they were clearly driven by the random-frequency tone sequences, and had statistically significant responses to the deviant- alone conditions in the 50 ms response interval starting at stimulus onset, compared with the 50 ms interval of spontaneous activity just preceding stimulus onset (2-tailed t-test, p,0.01). A few cases with very intense but short onset bursts resulted in no net increase of the average response rate during the 50 ms interval, but were nevertheless included in the data. On the other hand, this criterion rejected about 20% of the MUA data, in which responses were present but were late (deviant-alone response latency .50 ms). These late responses were not included in the population analysis. Supporting Information Text S1 The relationships between the model used in the paper and models of synaptic depletion. (DOC) Author Contributions Conceived and designed the experiments: IN NT. Performed the experiments: NT AY. Analyzed the data: NT. Wrote the paper: NT IN. the sensory novelty encoded by the mismatch negativity. J Neurosci 30: 16475–16484. 13. Nelken I, Ulanovsky N (2007) Change detection, mismatch negativity and stimulus-specific adaptation in animal models. J Psychophysiol 21: 214–223. 14. Naatanen R, Gaillard AW, Mantysalo S (1978) Early selective-attention effect on evoked potential reinterpreted. Acta Psychol (Amst) 42: 313–329. 15. Sams M, Hamalainen M, Antervo A, Kaukoranta E, Reinikainen K, et al. (1985) Cerebral neuromagnetic responses evoked by short auditory stimuli. Electroencephalogr Clin Neurophysiol 61: 254–266. 16. Jacobsen T, Schroger E, Horenkamp T, Winkler I (2003) Mismatch negativity to pitch change: varied stimulus proportions in controlling effects of neural refractoriness on human auditory event-related brain potentials. Neuroscience Letters 344: 79–82. 17. Jacobsen T, Schroger E (2001) Is there pre-attentive memory-based comparison of pitch? Psychophysiology 38: 723–727. 18. Markram H, Gupta A, Uziel A, Wang Y, Tsodyks M (1998) Information processing with frequency-dependent synaptic connections. Neurobiol Learn Mem 70: 101–112. 19. Tsodyks M, Pawelzik K, Markram H (1998) Neural networks with dynamic synapses. Neural Comput 10: 821–835. 20. Lazar R, Metherate R (2003) Spectral interactions, but no mismatch negativity, in auditory cortex of anesthetized rat. Hear Res 181: 51–56. 21. Perez-Gonzalez D, Malmierca MS, Covey E (2005) Novelty detector neurons in the mammalian auditory midbrain. Eur J Neurosci 22: 2879–2885. 22. Szymanski FD, Garcia-Lazaro JA, Schnupp JW (2009) Current source density profiles of stimulus-specific adaptation in rat auditory cortex. J Neurophysiol 102: 1483–1490. 23. Storace DA, Higgins NC, Read HL (2011) Thalamocortical pathway specialization for sound frequency resolution. J Comp Neurol 519: 177–193. PLoS ONE | www.plosone.org 16 August 2011 | Volume 6 | Issue 8 | e23369