- Research article
- Open Access
Frequency-specific modulation of population-level frequency tuning in human auditory cortex
- Hidehiko Okamoto†1Email author,
- Henning Stracke†1,
- Pienie Zwitserlood2,
- Larry E Roberts3 and
- Christo Pantev1Email author
© Okamoto et al; licensee BioMed Central Ltd. 2009
- Received: 28 July 2008
- Accepted: 06 January 2009
- Published: 06 January 2009
Under natural circumstances, attention plays an important role in extracting relevant auditory signals from simultaneously present, irrelevant noises. Excitatory and inhibitory neural activity, enhanced by attentional processes, seems to sharpen frequency tuning, contributing to improved auditory performance especially in noisy environments. In the present study, we investigated auditory magnetic fields in humans that were evoked by pure tones embedded in band-eliminated noises during two different stimulus sequencing conditions (constant vs. random) under auditory focused attention by means of magnetoencephalography (MEG).
In total, we used identical auditory stimuli between conditions, but presented them in a different order, thereby manipulating the neural processing and the auditory performance of the listeners. Constant stimulus sequencing blocks were characterized by the simultaneous presentation of pure tones of identical frequency with band-eliminated noises, whereas random sequencing blocks were characterized by the simultaneous presentation of pure tones of random frequencies and band-eliminated noises. We demonstrated that auditory evoked neural responses were larger in the constant sequencing compared to the random sequencing condition, particularly when the simultaneously presented noises contained narrow stop-bands.
The present study confirmed that population-level frequency tuning in human auditory cortex can be sharpened in a frequency-specific manner. This frequency-specific sharpening may contribute to improved auditory performance during detection and processing of relevant sound inputs characterized by specific frequency distributions in noisy environments.
- Test Stimulus
- Source Strength
- Critical Band
- Constant Sequencing
- Auditory Performance
Humans can effortlessly process task-relevant sound signals despite the usual presence of concurrent noises, which are often task-irrelevant. Auditory focused attention eases this perception process. Recent magnetoencephalography (MEG)  and electroencephalography (EEG) [2, 3] studies revealed that auditory focused attention not only amplifies task-relevant ('gain'), but crucially also suppresses task-irrelevant neural activity ('sharpening') in human auditory cortex. Despite extensive research regarding attentional gain effects during auditory processing, the neurophysiological sharpening effects in human auditory cortex remain elusive [4–10].
Neurophysiological studies have uncovered possible underlying neural mechanisms. A functional magnetic resonance imaging (fMRI) study  showed that top-down auditory focused attention enhanced hemodynamic activation mainly in the lateral compared to the mesial auditory cortex. These results indicate that attentional modulation takes place on the cortical level.
However, not only top-down, but also bottom-up auditory inputs play an important role for neural processing of target sound signals in noisy environments. Sams and Salmelin  demonstrated that band-eliminated noises (BENs) containing narrow notches centered at the test stimulus frequency evoked smaller test sound-related N1m response (originating in non-primary auditory cortex [20–22]) compared to BENs with wider notches. Beyond doubt, spectral cues are important for the neural processing in noisy environments. However, if a target sound and concurrent irrelevant sounds have similar frequency distributions, we can nonetheless segregate a specific relevant auditory stream from concurrent irrelevant streams based on temporal cues (auditory scene analysis ). Auditory stream segregation can be accomplished without top-down attention [24, 25], but top-down attention can contribute to the auditory stream segregation process [3, 26].
Based on these results, the goal of the present study was to investigate by means of MEG in awake, behaving humans whether population-level frequency tuning can be modulated by differential stimulus sequencing under auditory focused attention. Previous studies [1, 2, 19, 27] demonstrated that population-level frequency tuning can be measured by the simultaneous presentation of pure tones and broadband noises containing spectral notches of different widths centred at the frequency of the tone (Figure 1B). We hypothesized population-level frequency tuning to be sharper in a condition that invited subjects to focus processing resources on one specific auditory filter (by presenting solely tones of identical frequency), relative to a condition that forced subjects to distribute resources to several different auditory filters at the same time (by randomly presenting tones of several different frequencies).
14 healthy subjects (7 females) between 23 and 30 years of age (mean 26.4 years) participated in the present study. All subjects were right-handed (assessed with the Edinburgh Handedness Inventory ), and their hearing thresholds were within normal hearing level, as tested by means of clinical pure-tone audiometry. Subjects gave written informed consent for their participation in the study in accordance with procedures approved by the Ethics Commission of the Medical Faculty, University of Muenster.
Stimuli and experimental design
The simultaneously presented BENs were prepared as follows: From 8000 Hz low-pass filtered white noise (sampling rate: 48000 Hz), spectral frequency bands with widths of either 1/4 critical band (1/4 CB), 1/2 critical band (1/2 CB), or 1 critical band (1 CB) centred at the frequency of the simultaneously presented TS were eliminated (Figure 2 and additional file 1). All BENs (duration 3000 msec; 10 msec rise and fall times) started 2200 msec prior to TS onset and ceased 200 msec after TS offset. All sound stimuli were prepared as sound files and presented under control of Presentation (Neurobehavioral Systems, Albany, CA, United States). 18000 Hz frequency tags (not perceivable) were attached to the onset of each TS in order to obtain precise timing. SRM-212 electrostatic earphones (Stax, Saitama, Japan) transduced air-conducted sounds which were delivered through silicon tubes (length: 60 cm; inner diameter: 5 mm) and silicon earpieces fitted to each subject's ears. The hearing threshold for the 1000 Hz TS was determined for each ear of each individual at the beginning of the MEG session. The 1000 Hz TS was presented binaurally at intensity of 35 dB above individual sensation level. The power of the other TS, which were also presented binaurally, was adjusted to the power of the 1000 Hz TS. The total power of the binaurally presented BENs was 15 dB larger than TS power, resulting in slightly higher spectrum levels for the BENs containing wider notches compared to the BEN with the narrowest notch (see Additional file 2; please note that the spectrum level difference is nearly invisible and therefore considered to be negligible).
In order to investigate the effects of stimulus sequencing during auditory focused attention, we contrasted two different conditions within subjects: 'constant sequencing' and 'random sequencing'. In the constant sequencing session, 30 TS with identical frequency (either solely 250, 350, 450, 570, 700, 840, 1000, 1170, 1370, 1600, 1850, 2150, 2500, 2900, 3400, or 4000 Hz) were successively (and pseudo-randomly) presented simultaneously with either the 1/4, 1/2, or the 1 CB BEN. In the random sequencing session, 30 TS with different frequencies were presented, pseudo-randomly chosen from the same frequencies that were used in the constant sequencing blocks (250, 350, 450, 570, 700, 840, 1000, 1170, 1370, 1600, 1850, 2150, 2500, 2900, 3400, or 4000 Hz). As in the constant sequencing condition, BENs with notches of either 1/4, 1/2, or 1 CB were presented simultaneously and pseudo-randomly (Figure 2 and additional file 1). Crucially, the overall amount of bottom-up auditory inputs was identical between constant sequencing and random sequencing conditions, while the patterning of stimuli was different. During all conditions, subjects were instructed to focus their attention on the auditory stimuli, and to press a response button as quickly as possible with their left or right index finger (randomized between subjects) whenever a TS with gap was detected. Constant sequencing and random sequencing blocks alternated, and block order was counterbalanced between subjects. In total, 160 trials (10 trials for 16 frequencies) for each BEN condition in each sequencing condition were presented and subjected to data analysis.
Data acquisition and analysis
Auditory evoked fields were measured with a helmet-shaped 275 channel whole head magneto-gradiometer (Omega; CTF Systems, Coquitlam, British Columbia, Canada) in a silent magnetically shielded room. During the measurement, participants were comfortably seated upright, instructed not to move, and to fixate their eyes on the cross in the center of the screen in order to avoid eye movements. Head position was fixed with cotton pads and monitored via video camera. Alertness and compliance were also monitored via button press detecting the deviant TS as described above. The measured magnetic fields were digitally sampled at a rate of 600 Hz. Epochs of data elicited by TS with and without temporal gap, including a 300 msec pre-TS-onset interval and a 300 msec post-TS-onset interval, were averaged selectively for each BEN and attentional condition (irrespective of frequency) after rejection of artifact epochs containing field changes larger than 3 pT. We excluded magnetic fields with latencies longer than 300 msec from the analysis due to the overlap of motor responses and auditory evoked responses elicited by the temporal gap. The evoked field source locations and orientations were determined in a head-based Cartesian coordinate system, with the origin at the midpoint of the medio-lateral axis joining the center of the entrances of the ear canals. The posterior-anterior axis and the inferior-superior axis ran through nasion and origin and the origin perpendicularly to the medio-lateral and posterior-anterior axis.
For the analysis of the major component of the auditory evoked field, the N1m, the averaged magnetic field signals were 30 Hz low-pass filtered, followed by a baseline correction relative to the 300 msec pre-stimulus interval. Initially, the time point of maximal global field power, measured as root-mean square across all sensors around 150 msec after stimulus onset, was identified as N1m response. Afterwards, the 10 msec time window around the peak was used for dipole source estimation. The source locations and orientations were estimated by means of two single equivalent current dipoles (one for each hemisphere) based on the grand-averaged MEG waveforms for each subject. Finally, the estimated source for each hemisphere of each subject was fixed in its location and orientation, and source strengths were calculated for each BEN condition (BEN_1/4CB, BEN_1/2CB, and BEN_1CB) and each stimulus sequencing condition (constant sequencing and random sequencing). For each condition and hemisphere, the N1m source strength was defined as the peak amplitude of the source strength waveform in the time interval between 100 and 300 msec (if there were several peaks, the peak with the latency closest to the average peak latency across single peak cases was selected as N1m response).
In order to evaluate the gain and sharpening effects of frequency-specificity, the maximum source strengths and latencies of the N1m responses elicited by the TS for each condition were analyzed separately via repeated-measures analyses of variance (ANOVA) using the factors BEN-TYPE (BEN_1/4CB, BEN_1/2CB, and BEN_1CB), HEMISPHERE (Left and Right), and SEQUENCING (Constant and Random). Post-hoc comparisons were performed using Bonferroni-Dunn's multiple-comparisons correction yielding a significance threshold of p < 0.0167. The behavioral data collected during MEG recording were analyzed similarly. Error rates (misses + false alarms) and reaction times were analyzed via repeated-measures ANOVAs using the factors BEN-TYPE (BEN_1/4CB, BEN_1/2CB, and BEN_1CB) and SEQUENCING (Constant and Random). Post-hoc comparisons again entailed Bonferroni-Dunn's multiple-comparisons corrections.
N1m source strength and latency
Our present results confirmed the hypothesis that under focused auditory attention and relative to random stimulus sequencing, constant stimulus sequencing sharpens population-level frequency tuning in human auditory cortex in the tonotopic region of the constant frequency. N1m responses were significantly larger when the test stimulus (TS) had a constant frequency than with random TS frequencies, particularly when band-eliminated noises (BENs) with narrow stop-bands were simultaneously presented. Because the total amount of stimulation received at each frequency was identical between the constant and random sequencing conditions, it is the difference in patterning of the stimuli that must be responsible for the findings, with one pattern allowing processing resources to be attracted or allocated to a specific frequency, and the other pattern not.
In order to investigate the mechanism underlying neural population-level frequency tuning, we utilized overlays of TS and BEN and measured auditory evoked fields by means of MEG. Neural activity, which was evoked by TS-BEN overlays, could be schematically divided into three categories: (1) neural activity evoked solely by the TS (dark gray areas in Figure 1B), (2) neural activity triggered merely by the BEN (light gray areas), and (3) neural activity elicitable by both the TS as well as the BEN (black areas). The N1m responses analyzed in this experiment represent neural groups solely activated by TS onset (dark gray area), since distinct neural groups (black and light gray areas) had already been activated and masked by preceding BENs when TS appeared. We found that the smaller notch-width of BEN caused smaller N1m source strength, as shown in Figure 5. The presentation of narrow BENs might result in comparably large overlap between neural populations representing BEN versus TS, and therefore comparably little neural activity was elicited by the late TS onset. Constant stimulus sequencing under focused auditory attention may cause sharper and larger neural activity at the attended (constantly presented) frequency, and broader and smaller neural responses at the other frequencies, compared to the random sequencing condition (as schematized in Figure 1A). This results in little neural activity overlap (black area in Figure 1B) and large neural activity elicited by the TS onset (dark gray area), especially in case of narrow BEN conditions. We confirmed this hypothesis by demonstrating large N1m source strength differences between the constant sequencing and random sequencing conditions in case of narrow BENs, but similar N1m responses between these two sequencing conditions in case of wide BENs (Figure 5).
Our findings cannot be easily explained by invoking attentional gain alone . It is possible that attentional gain may have been higher for the constant sequencing compared to the random sequencing condition, because subjects could allocate their processing resources to a specific frequency in the constant sequencing condition, but had to divide them across frequencies in the random sequencing condition. However, the differential dependence of N1m enhancement on BEN type, with N1m enhancement declining with the bandwidth of the notch more in the random sequencing relative to the constant sequencing condition, implies that the sharpness of tuning was an important additional factor. Inhibitory neural interactions in the auditory system are known to contribute to sharpening frequency tuning [30–33]. Recent animal studies recording single neural activity demonstrated that afferent auditory neurons project broadly tuned inhibitory inputs, in addition to focally tuned excitatory inputs. This results in relatively stronger inhibition of the auditory neurons corresponding to frequencies that neighbour the test frequency [34–36]. Such balanced (excitatory and inhibitory inputs) neural activity contributes to sharpening the frequency tuning and to improving spectral contrasts. In the model of Figure 1, enhanced inhibitory effects on the task-irrelevant neural activity is depicted as reduced activity evoked by the BEN sound in the constant sequencing compared to the random sequencing condition.
In the present design, the subjects rapidly appreciated when a constant sequencing block was presented. Under these conditions, they could focus their attention on a particular stimulus frequency for the duration of the block (30 trials). Similarly, in a random sequencing block, the subjects understood that attention had to be divided across several stimulus frequencies. Because of this evident task knowledge, it is possible that frequency tuning was differentially modulated by "top-down" attentional mechanisms between these two conditions . These top-down neural inputs targeting at one specific region within the tonotopic map may have enhanced and sharpened the neural activity corresponding to the constant test stimulus as compared to the random sequencing condition, where the subjects would have distributed the top-down processing resources across the task-relevant tonotopic area, which was defined by the wide range of presented frequencies.
Alternatively, the cumulative bottom-up inputs within a constant-sequencing block may have driven a dual tuning process. The constant stimulus sequencing could have configured a regular auditory stream, which was perceivable for the listeners as an auditory object , whereas the random sequencing could not configure such an auditory object. The encoding of an auditory object in noisy environments might enhance the corresponding neural activity , and might have resulted in better auditory performance in the present study. Either of these mechanisms ("top-down" or "bottom-up") is compatible with evidence for a "winner take all" strategy of auditory tuning reported by Schulze et al.  and Kurt et al. . Their findings indicated that slightly higher neural activity elicited by one specific sound object ('winner') inhibited neural activity corresponding to other sounds ('losers'). In the present study, the repetition of constant TS within a block might have unconsciously formed a neural representation of an auditory object corresponding to the constant TS sequence in the auditory cortex by means of a bottom-up process. Alternatively, top-down auditory focused attention during constant stimulus sequencing could have defined the neural activity corresponding to the constant TS as 'winner' in advance of the TS onset, dynamically sharpening frequency tuning for the relevant sound in constant sequencing blocks. These neural processes might have lead to sharper population-level frequency tuning and better auditory performance, as evident in the constant sequencing condition during auditory focused attention.
In the present study, we observed larger N1m source strengths in the left compared to the right hemisphere. Noteworthy, it is known that the N1m response elicited by a pure tone in a silent environment has similar or even larger amplitudes  and shorter latencies  in the right hemisphere than in the left hemisphere. Therefore, the results of the present study support the hypothesis that the left hemisphere plays a dominant role in monitoring and processing auditory signals in noisy environments .
Previous studies demonstrated that the repetition of auditory stimuli with an identical or a similar frequency reduces corresponding neural activity ('stimulus-specific adaptation') [45–49]. In the present study, TS were identical in the constant sequencing condition, which theoretically could have lead to larger stimulus-specific adaptation effects and smaller N1m responses than in random sequencing. However, the N1m responses were significantly larger in the constant sequencing condition. The important difference between our study and previous studies is that whereas we presented the BENs between as well as during the presentation of the test sounds, silent intervals between test stimuli were used in previous studies. In our study, all BENs in a constant sequencing block contained a spectral notch around the constant TS frequency, whereas in the random sequencing block most of the preceding BENs (not the simultaneously presented BENs) had a spectrum overlapping with the subsequent TS. The BENs had a power that was 15 dB larger compared to the TS. Therefore, the spectral overlap between a preceding BEN and the subsequent TS in the random sequencing condition might have caused larger N1m decrements as compared to the constant sequencing condition. However, considering the long (2200 msec) time interval between a preceding BEN and the subsequent TS, the adaptation effect on the N1m response should be quite small [49, 50]. Thus, adaptation alone cannot explain the relatively small N1m source strength difference between the constant and the random sequencing conditions in the wide BEN compared to the narrow BEN conditions.
Our findings suggest that constant stimulus sequencing during auditory focused attention can improve population-level frequency tuning in humans in a frequency-specific manner. This effect may be achieved by top-down, bottom-up, or both processes. Interactions between excitatory and inhibitory neural networks, intensified by constant stimulus sequencing, sharpens population-level frequency tuning in a frequency-specific manner, leading to enhanced auditory performance in noisy environments.
We are grateful to Andreas Wollbrink and Karin Berning for technical assistance. This work has been supported by the "Deutsche Forschungsgemeinschaft" (Pa 392/13-1, Pa 392/10-3).
- Okamoto H, Stracke H, Wolters CH, Schmael F, Pantev C: Attention improves population-level frequency tuning in human auditory cortex. J Neurosci. 2007, 27: 10383-10390. 10.1523/JNEUROSCI.2963-07.2007.View ArticlePubMedGoogle Scholar
- Kauramaki J, Jaaskelainen IP, Sams M: Selective attention increases both gain and feature selectivity of the human auditory cortex. PLoS ONE. 2007, 2: e909-10.1371/journal.pone.0000909.PubMed CentralView ArticlePubMedGoogle Scholar
- Bidet-Caulet A, Fischer C, Besle J, Aguera PE, Giard MH, Bertrand O: Effects of selective attention on the electrophysiological representation of concurrent sounds in the human auditory cortex. J Neurosci. 2007, 27: 9252-9261. 10.1523/JNEUROSCI.1402-07.2007.View ArticlePubMedGoogle Scholar
- Ross B, Picton TW, Herdman AT, Pantev C: The effect of attention on the auditory steady-state response. Neurol Clin Neurophysiol. 2004, 2004: 22.PubMedGoogle Scholar
- Naatanen R, Picton T: The N1 wave of the human electric and magnetic response to sound: a review and an analysis of the component structure. Psychophysiology. 1987, 24: 375-425. 10.1111/j.1469-8986.1987.tb00311.x.View ArticlePubMedGoogle Scholar
- Picton TW, Hillyard SA: Human auditory evoked potentials. II. Effects of attention. Electroencephalogr Clin Neurophysiol. 1974, 36: 191-199. 10.1016/0013-4694(74)90156-4.View ArticlePubMedGoogle Scholar
- Hillyard SA, Hink RF, Schwent VL, Picton TW: Electrical signs of selective attention in the human brain. Science. 1973, 182: 177-180. 10.1126/science.182.4108.177.View ArticlePubMedGoogle Scholar
- Grady CL, Van Meter JW, Maisog JM, Pietrini P, Krasuski J, Rauschecker JP: Attention-related modulation of activity in primary and secondary auditory cortex. Neuroreport. 1997, 8: 2511-2516. 10.1097/00001756-199707280-00019.View ArticlePubMedGoogle Scholar
- Woldorff MG, Gallen C, Hampson SW, Hillyard SA, C P, Sobel D, Bloom FE: Modulation of early sensory processing in human auditory cortex during auditory selective attention. Proc Natl Acad Sci USA. 1993, 18: 8722-8726. 10.1073/pnas.90.18.8722.View ArticleGoogle Scholar
- Fritz JB, Elhilali M, David SV, Shamma SA: Auditory attention – focusing the searchlight on sound. Curr Opin Neurobiol. 2007, 17: 437-455. 10.1016/j.conb.2007.07.011.View ArticlePubMedGoogle Scholar
- Calford MB, Webster WR, Semple MM: Measurement of frequency selectivity of single neurons in the central auditory pathway. Hear Res. 1983, 11: 395-401. 10.1016/0378-5955(83)90070-9.View ArticlePubMedGoogle Scholar
- Robles L, Ruggero MA: Mechanics of the mammalian cochlea. Physiol Rev. 2001, 81: 1305-1352.PubMed CentralPubMedGoogle Scholar
- Pantev C, Bertrand O, Eulitz C, Verkindt C, Hampson S, Schuierer G, Elbert T: Specific tonotopic organizations of different areas of the human auditory cortex revealed by simultaneous magnetic and electric recordings. Electroencephalogr Clin Neurophysiol. 1995, 94: 26-40. 10.1016/0013-4694(94)00209-4.View ArticlePubMedGoogle Scholar
- Romani GL, Williamson SJ, Kaufman L: Tonotopic organization of the human auditory cortex. Science. 1982, 216: 1339-1340. 10.1126/science.7079770.View ArticlePubMedGoogle Scholar
- Reale RA, Imig TJ: Tonotopic organization in auditory cortex of the cat. J Comp Neurol. 1980, 192: 265-291. 10.1002/cne.901920207.View ArticlePubMedGoogle Scholar
- Hubner R, Hafter ER: Cuing mechanisms in auditory signal detection. Percept Psychophys. 1995, 57: 197-202.View ArticlePubMedGoogle Scholar
- Hafter ER, Schlauch RS, Tang J: Attending to auditory filters that were not stimulated directly. J Acoust Soc Am. 1993, 94: 743-747. 10.1121/1.408203.View ArticlePubMedGoogle Scholar
- Petkov CI, Kang X, Alho K, Bertrand O, Yund EW, Woods DL: Attentional modulation of human auditory cortex. Nat Neurosci. 2004, 7: 658-663. 10.1038/nn1256.View ArticlePubMedGoogle Scholar
- Sams M, Salmelin R: Evidence of sharp frequency tuning in the human auditory cortex. Hear Res. 1994, 75: 67-74. 10.1016/0378-5955(94)90057-4.View ArticlePubMedGoogle Scholar
- Lutkenhoner B, Steinstrater O: High-precision neuromagnetic study of the functional organization of the human auditory cortex. Audiol Neurootol. 1998, 3: 191-213. 10.1159/000013790.View ArticlePubMedGoogle Scholar
- Eggermont JJ, Ponton CW: The neurophysiology of auditory perception: from single units to evoked potentials. Audiol Neurootol. 2002, 7: 71-99. 10.1159/000057656.View ArticlePubMedGoogle Scholar
- Reite M, Adams M, Simon J, Teale P, Sheeder J, Richardson D, Grabbe R: Auditory M100 component 1: relationship to Heschl's gyri. Brain Res Cogn Brain Res. 1994, 2: 13-20. 10.1016/0926-6410(94)90016-7.View ArticlePubMedGoogle Scholar
- Bregman AS: Auditory scene analysis: the perceptual organization of sound. 1990, Cambridge, Mass.: MIT PressGoogle Scholar
- Sussman E, Horvath J, Winkler I, Orr M: The role of attention in the formation of auditory streams. Percept Psychophys. 2007, 69: 136-152.View ArticlePubMedGoogle Scholar
- Nager W, Teder-Salejarvi W, Kunze S, Munte T: Preattentive evaluation of multiple perceptual streams in human audition. Neuroreport. 2003, 14: 871-874. 10.1097/00001756-200305060-00019.View ArticlePubMedGoogle Scholar
- Cusack R, Deeks J, Aikman G, Carlyon RP: Effects of location, frequency region, and time course of selective attention on auditory scene analysis. J Exp Psychol Hum Percept Perform. 2004, 30: 643-656. 10.1037/0096-15126.96.36.1993.View ArticlePubMedGoogle Scholar
- Stracke H, Okamoto H, Pantev C: Interhemispheric Support during Demanding Auditory Signal-in-Noise Processing. Cereb Cortex.Google Scholar
- Oldfield RC: The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971, 9: 97-113. 10.1016/0028-3932(71)90067-4.View ArticlePubMedGoogle Scholar
- Zwicker E, Fastl H: Psychoacoustics: facts and models. 1999, Berlin; New York: SpringerView ArticleGoogle Scholar
- Okamoto H, Kakigi R, Gunji A, Kubo T, Pantev C: The dependence of the auditory evoked N1m decrement on the bandwidth of preceding notch-filtered noise. Eur J Neurosci. 2005, 21: 1957-1961. 10.1111/j.1460-9568.2005.04022.x.View ArticlePubMedGoogle Scholar
- Okamoto H, Kakigi R, Gunji A, Pantev C: Asymmetric lateral inhibitory neural activity in the auditory system: A magnetoencephalographic study. BMC Neurosci. 2007, 8: 33-10.1186/1471-2202-8-33.PubMed CentralView ArticlePubMedGoogle Scholar
- Pantev C, Okamoto H, Ross B, Stoll W, Ciurlia-Guy E, Kakigi R, Kubo T: Lateral inhibition and habituation of the human auditory cortex. Eur J Neurosci. 2004, 19: 2337-2344. 10.1111/j.0953-816X.2004.03296.x.View ArticlePubMedGoogle Scholar
- von Békésy G: Sensory Inhibition. 1967, Princeton: Princeton University PressGoogle Scholar
- Wehr M, Zador AM: Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex. Nature. 2003, 426: 442-446. 10.1038/nature02116.View ArticlePubMedGoogle Scholar
- Higley MJ, Contreras D: Balanced excitation and inhibition determine spike timing during frequency adaptation. J Neurosci. 2006, 26: 448-457. 10.1523/JNEUROSCI.3506-05.2006.View ArticlePubMedGoogle Scholar
- Wu GK, Arbuckle R, Liu BH, Tao HW, Zhang LI: Lateral sharpening of cortical frequency tuning by approximately balanced inhibition. Neuron. 2008, 58: 132-143. 10.1016/j.neuron.2008.01.035.PubMed CentralView ArticlePubMedGoogle Scholar
- Sarter M, Givens B, Bruno J: The cognitive neuroscience of sustained attention: where top-down meets bottom-up. Brain Res Brain Res Rev. 2001, 35: 146-160. 10.1016/S0165-0173(01)00044-3.View ArticlePubMedGoogle Scholar
- Alain C, Arnott SR: Selectively attending to auditory objects. Front Biosci. 2000, 5: D202-212. 10.2741/Alain.View ArticlePubMedGoogle Scholar
- Micheyl C, Tian B, Carlyon R, Rauschecker J: Perceptual organization of tone sequences in the auditory cortex of awake Macaques. Neuron. 2005, 48: 139-148. 10.1016/j.neuron.2005.08.039.View ArticlePubMedGoogle Scholar
- Schulze H, Hess A, Ohl FW, Scheich H: Superposition of horseshoe-like periodicity and linear tonotopic maps in auditory cortex of the Mongolian gerbil. Eur J Neurosci. 2002, 15: 1077-1084. 10.1046/j.1460-9568.2002.01935.x.View ArticlePubMedGoogle Scholar
- Kurt S, Deutscher A, Crook JM, Ohl FW, Budinger E, Moeller CK, Scheich H, Schulze H: Auditory cortical contrast enhancing by global winner-take-all inhibitory interactions. PLoS ONE. 2008, 3: e1735-10.1371/journal.pone.0001735.PubMed CentralView ArticlePubMedGoogle Scholar
- Kanno A, Nakasato N, Fujita S, Seki K, Kawamura T, Ohtomo S, Fujiwara S, Yoshimoto T: Right hemispheric dominance in the auditory evoked magnetic fields for pure-tone stimuli. Electroencephalogr Clin Neurophysiol Suppl. 1996, 47: 129-132.PubMedGoogle Scholar
- Roberts TPL, Ferrari P, Stufflebeam SM, Poeppel D: Latency of the auditory evoked neuromagnetic field components: Stimulus dependence and insights toward perception. J Clin Neurophysiol. 2000, 17: 114-129. 10.1097/00004691-200003000-00002.View ArticlePubMedGoogle Scholar
- Okamoto H, Stracke H, Ross B, Kakigi R, Pantev C: Left hemispheric dominance during auditory processing in a noisy environment. BMC Biology. 2007, 5: 52-10.1186/1741-7007-5-52.PubMed CentralView ArticlePubMedGoogle Scholar
- Perez-Gonzalez D, Malmierca MS, Covey E: Novelty detector neurons in the mammalian auditory midbrain. Eur J Neurosci. 2005, 22: 2879-2885. 10.1111/j.1460-9568.2005.04472.x.View ArticlePubMedGoogle Scholar
- Ulanovsky N, Las L, Farkas D, Nelken I: Multiple time scales of adaptation in auditory cortex neurons. J Neurosci. 2004, 24: 10440-10453. 10.1523/JNEUROSCI.1905-04.2004.View ArticlePubMedGoogle Scholar
- Ulanovsky N, Las L, Nelken I: Processing of low-probability sounds by cortical neurons. Nat Neurosci. 2003, 6: 391-398. 10.1038/nn1032.View ArticlePubMedGoogle Scholar
- Butler RA: Effect of changes in stimulus frequency and intensity on habituation of the human vertex potential. J Acoust Soc Am. 1968, 44: 945-950. 10.1121/1.1911233.View ArticlePubMedGoogle Scholar
- Brosch M, Schreiner CE: Time course of forward masking tuning curves in cat primary auditory cortex. J Neurophysiol. 1997, 77: 923-943.PubMedGoogle Scholar
- Okamoto H, Ross B, Kakigi R, Kubo T, Pantev C: N1m recovery from decline after exposure to noise with strong spectral contrasts. Hear Res. 2004, 196: 77-86. 10.1016/j.heares.2004.04.017.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.