| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1Max-Planck-Institute of Neurobiology, Martinsried; 2Department of Biology II, Ludwig-Maximilians-University Munich, Martinsried; and 3Bernstein Center for Computational Neuroscience, Munich, Germany
Submitted 19 March 2008; accepted in final form 4 July 2008
 |
ABSTRACT |
|---|
|
|
|
INTRODUCTION |
|---|
|
), pitch perception (Rossing and Houtsma 1986), and stream segregation (Grimault et al. 2002). The behavioral importance of AMs has motivated the use of AM stimuli in many physiological studies of temporal processing in auditory neurons (for reviews, see Frisina 2001; Joris et al. 2004; Langner 1992). However, despite a wealth of experimental data, the representation of AMs in the responses of neurons at higher levels of the auditory pathway remains a topic of debate. The complexity of AMs as an acoustical property may play an important role in this issue.
The majority of physiological studies involving AM stimuli have used sinusoidally amplitude modulated (SAM) stimuli in which the envelope of a high-frequency "carrier" tone is specified by a low-frequency "modulator" tone. These studies have shown that the responses of many neurons in the ascending auditory pathway are strongly dependent on the frequency of the modulator. For example, many neurons in the central nucleus of the inferior colliculus (ICC) display strong tuning to modulation frequency with a diversity of tuning functions (high-pass, band-reject, etc.), leading to the hypothesis that AMs are encoded by their periodicity in the auditory midbrain (Krishna and Semple 2000; Langner and Schreiner 1988).
The use of the fact that neuronal responses are dependent on the modulation frequency of SAM stimuli as evidence that AMs in general are represented by their periodicity in the auditory midbrain is problematic for two reasons. The first reason is that tuning to the modulation frequency of SAM stimuli in the midbrain is not invariant to changes in stimulus context. For example, the tuning functions of neurons in the ICC can change dramatically with changes in the mean level, modulation depth, or background noise level of the stimulus, indicating that AMs with the same periodicity can have different neural representations (Krishna and Semple 2000; Rees and Palmer 1989). The second reason, which is the motivation for this study, is that AMs are a complex acoustical property, defined not only by modulation frequency, but also by a number of other important (and related) parameters. As shown in Fig. 1, changing the modulation frequency (FMOD) in SAM stimuli causes systematic changes in other parameters: the duration of the positive phase of each cycle (DUR), and the pause interval between cycles (IPI). Only the duty cycle [DC = DUR/(DUR + IPI)] remains constant. Thus the relationship between neural responses and modulation frequencyfor AMs in general cannot be unambiguously defined using SAM stimuli alone, because apparent tuning to modulation frequency may in fact result from the covariation between modulation frequency and another parameter. The effects of this ambiguity have already been shown in the auditory brain stem, where comparison of responses to SAM stimuli and pulse trains suggests that, for many neurons, apparent tuning to modulation frequency in SAM stimuli is actually a reflection of tuning to changes in IPI (Grothe et al. 2001).
|
). |
|
METHODS |
|---|
|
We recorded action potentials from single neurons in the ICC of 26 Mongolian gerbils (Meriones unguiculatus). The surgical procedures used in this study have been described in detail previously (Siveke et al. 2006). All experiments were approved according to the German Tierschutzgesetz (Reg. Obb. 211-2531-40/01). Briefly, adult gerbils were anesthetized by an initial intraperitoneal injection (0.5 ml/100 g body weight) of a physiological NaCl solution containing ketamine (20%) and xylazine (2%). During surgery and recordings, a dose of 0.03 ml of the same mixture was applied subcutaneously every 20 min. A small metal rod was mounted on the frontal part of the skull and used to secure the head of the animal in a stereotactic device during recordings. The animal was positioned in a sound-attenuated chamber, and a craniotomy was made over the inferior colliculus, 1.3–2.6 mm lateral from the midline and 0.5–0.8 mm caudal from the bregma. The dura mater overlying the cortex was removed, and glass pipettes filled with 2 M sodium acetate (impedance 10–30 M
) or tungsten electrodes (impedance 5 M) were advanced into the inferior colliculus (2–4 mm below the surface). Recording of action potentials and stimulus generation was controlled by custom-made software (B. Warren, University of Washington, Seattle, WA). Recorded signals were amplified, filtered, discriminated, and fed into a computer via a DSP board (System II, Tucker Davis Technologies). Pressure-injected fluorescent latex beads (Lumafluor) or iontophoretically applied horseradish peroxidase (HRP, 720 nA for 8 min, Sigma) were used to verify that recording sites were in the central nucleus of the inferior colliculus.
Acoustic stimulation
Acoustic stimuli were delivered using Tucker Davis Technologies System II, controlled by custom made software, presented via electrostatic speaker driver ED1 (TDT System 3), and calibrated electrostatic speaker (TDT System 3) connected via a tube to the ear contralateral to the recording site. After electrophysiological isolation of a single neuron, pure tone stimuli of 40-ms duration (rise-fall time adjusted to avoid any spectral artifacts) were presented to the contralateral ear at various frequencies and levels. From responses to these stimuli, best frequency (BF), threshold, and response type (based on the classification scheme outlined in Rees et al. 1997) were determined. Next, SAM tones (carrier at BF, 20 dB above threshold, 100% modulation depth) and noise (20 dB above threshold, 100% modulation depth) were presented. For SAM noise stimuli, the bandwidth of the noise was centered on the BF and was adjusted to evoke to maximum spike rate (but was 
1,000 Hz). SAM stimuli were of 150-, 200-, or 250-ms duration. FMOD ranged from 20 to 1,000 Hz, presented in 11 logarithmic steps.
For some neurons, trains of five or more trapezoidal pulses (linear rise/fall, carrier at BF, 20 dB above threshold, 100% modulation depth) were also presented. The DUR of the pulses and the IPI were systematically varied, which resulted in corresponding changes in DC and FMOD. The values of DUR (time during which the pulse was >50% of its maximum value) and IPI (time during which the pulse was <50% of its maximum value) used for each neuron were adjusted to an appropriate range based on observed response properties. For all neurons, pulse trains with at least five different values of DUR and IPI were presented. Of the 37 cells from which we recorded responses to both SAM tones and pulse trains, 24 had SAM rate modulation tuning functions (rMTFs), for which measurement of a preferred FMOD was appropriate (i.e., a low-pass, band-pass, or high-pass rMTF; for all-pass and band-reject rMTFs, measurement of the preferred modulation frequency is less meaningful). For 19 of these 24 cells, the range of values for IPI and DUR in the pulse trains was such that the FMOD of one or more of the pulse trains was similar to the preferred FMOD for SAM tones (although not necessarily with a 50% DC). The rise/fall time of the pulses was 1 ms. It should be noted that the linear rise/fall of the pulses introduces additional frequencies into the stimulus that are not present in, for example, SAM stimuli. However, of the 37 cells from which we recorded responses to pulse trains, only 5 had a BF between 0 and 3.5 KHz, the range of frequencies for which the bandwidth of the pulse train stimuli exceeds the critical bandwidth of the auditory filter (Schmiedt 1989). Stimuli were presented in a randomized order (interleaved) with 10 repetitions of each stimulus. Stimulus repetition rates were chosen to avoid hysteresis effects (in most cases between 500 and 1,000 ms).
Data analysis
For all periodic stimuli, only the response to the ongoing component was analyzed. The response to the first pulse or first cycle of SAM stimuli and at least the first 10 ms of the response were omitted. For neurons with OFF discharges, the response correlated with the stimulus end was omitted from the analysis. For each neuron, the shape of the rMTF (the tuning function relating the spike rate of the ongoing response component to the FMOD) was classified based on the range of frequencies for which the response was >50% of the maximum. To assess the effects of DUR, IPI, DC, and FMOD on neural responses, partial Kendall rank correlation coefficients were computed. The partial correlation coefficient (CCp) quantifies the effects of each stimulus parameter on the neural response independently, removing any ambiguity that arises because of covariations between different stimulus parameters.
To identify groups of neurons with similar response properties, each neuron was defined in a four-dimensional space by its CCp for DUR, IPI, DC, and FMOD. Neurons were sorted into clusters based on Euclidean distance. We used the median linkage algorithm, which creates clusters based on a weighted center of mass distance (WPGMC). This algorithm yielded robust results in which the mean distance from each neuron to the centroid of its assigned cluster was much smaller than the mean distance between the centroids of different clusters.
|
|
RESULTS |
|---|
|
Responses to SAM stimuli
In 95 of the 115 cells, we observed a response to SAM tone stimuli (pure tone carrier at BF, 20 dB above threshold, 100% modulation depth) that consisted of not only an ON component for the first modulation cycle but also an ongoing component for subsequent cycles. Because there is some evidence that the temporal code for AMs in the brain stem is converted to a rate code in the ICC (Frisina 2001; Joris et al. 2004; Langner 1992), we focused our analysis on the spike rate in the ongoing response (although we do not discount the possibility that additional information about AMs is carried in spike timing). For each neuron with an ongoing response, we classified the shape of the rMTF (the tuning function relating spike rate to FMOD) based on the range of FMOD for which the firing rate was >50% of its maximum value. The results for two typical neurons are shown in Fig. 2 A. For the first neuron, the spike rate of the ongoing response was high for FMOD <150 Hz and low for FMOD >150 Hz, yielding a tuning function with a low-pass shape. The spike rate of the second neuron displayed the opposite trend, with relatively low values for FMOD <150 Hz and relatively high values for FMOD >150 Hz, resulting in a tuning function with a high-pass shape. Across the population, we observed a diversity of tuning function shapes including low-pass (10%, 10/95), high-pass (17%, 16/95), band-pass (5%, 5/95), all-pass (37%, 35/95), and band-reject (31%, 29/95), as summarized in Fig. 2B.
|
Responses to pulse train stimuli
To provide a more detailed characterization of the representation of AMs in midbrain responses, we recorded the responses of 37 cells to a series of pulse train stimuli (trapezoidally amplitude modulated tones, pure tone carrier at BF, 20 dB above threshold, 100% modulation depth) with different DURs and IPIs. As shown in Fig. 3 A, these stimuli contain AMs in which four potentially important parameters are systematically varied: the DUR, the IPI, the DC, and the FMOD. The responses of a typical neuron to SAM tone stimuli and pulse train stimuli are summarized in Fig. 3, B and C. This neuron displays strong tuning to FMOD in SAM tone stimuli, as evidenced by the low-pass rMTF shown in Fig. 3B. The responses of this neuron to the pulse train stimuli with different values for DUR and IPI are summarized in the surface shown in Fig. 3C. The largest response is elicited by pulse trains with large values for DUR and IPI, and the direction along which the variance in the response surface is maximal (pink arrow) is oriented midway between the directions corresponding to changes in DUR and FMOD (see Fig. 3A). This suggests that this neuron is sensitive to changes in both FMOD and DUR.
|
The responses of a second neuron to SAM tone stimuli and pulse train stimuli are summarized in Fig. 3, D and E. This neuron also displays strong tuning to FMOD in SAM tone stimuli, as evidenced by the high-pass rMTF shown in Fig. 3D. For this neuron, the largest response is elicited by pulse trains with large values for DUR and small values for IPI, and the direction along which the variance in the response surface is maximal (pink arrow) nearly orthogonal to the direction corresponding to changes in FMOD. As measured by CCp, the response of this neuron was sensitive to DC (CCp = 0.88), IPI (CCp = –0.64), and DUR (CCp = 0.37) and insensitive to changes in FMOD (CCp = –0.08).
Across the population of 37 cells, we observed a range of sensitivities to the different stimulus parameters, as summarized in Fig. 4 A. The parameter for which we observed the largest number cells with significant sensitivity was DUR (28/37), followed by DC (20/37), IPI (19/37), and FMOD (17/37). These results suggest that AMs are represented in the responses of individual neurons in the auditory midbrain not only by their periodicity but rather by a combination of several stimulus parameters.
|
The results of the cluster analysis are shown in Fig. 4B. We observed two dominant clusters, the first of which (red, n = 15) was, on average, most sensitive to DC (CCp = 0.47) and the second of which (green, n = 14) was most sensitive to DUR (CCp = 0.63). We also observed two additional small clusters, the first of which (blue, n = 4) was most sensitive to FMOD (CCp = –0.71) and the second of which (cyan, n = 3) was also most sensitive FMOD (CCp = 0.41). We also observed one outlier neuron (magenta) that was most sensitive to DC (CCp = 0.81). A summary of the sensitivities of each cluster, along with the number of cells in each cluster with significant sensitivity to each parameter are indicated in the table shown in Fig. 4C. These results suggest the neural representation of AMs in the auditory midbrain may be organized into distinct functional clusters, each of which is sensitive to a complex combination of stimulus parameters.
Partial correlation coefficients provide a useful way to characterize a neuron's sensitivity to different stimulus parameters with a single value. However, it is important to note that CCp may underestimate the importance of a particular parameter if the effects of changes in that parameter on the neural response are nonmonotonic. For example, a neuron with band-pass sensitivity to a particular parameter may be highly sensitive to changes in that parameter, but the nonmonotonic relationship between the parameter and the response may result in a low CCp. To study whether this effect resulted in an underestimation of the importance of periodicity in midbrain responses to AMs, we examined the responses to pulse train stimuli for neurons with band-pass tuning to FMOD in SAM tone stimuli. The results for three typical neurons are summarized in Fig. 5.
|
For the third neuron, the response surface is nonmonotonic, with the largest response corresponding intermediate values of DUR and small values of IPI. The partial correlation coefficients for this neuron indicate that it was sensitive to IPI (CCp = –0.41), DC (CCp = 0.52), and DUR (CCp = 0.37). However, because the response surface is nonmonotonic, it is possible that the measured correlation coefficients underestimate the neuron's sensitivity to certain parameters, including FMOD. Nonetheless, it is clear that this neuron has sensitivity to parameters other than FMOD, because its response varies strongly along those directions corresponding to minimal changes in FMOD (green arrows). Taken together, the examples in Figs. 3 and 5 suggest that, whereas partial correlation coefficients may have the potential to underestimate sensitivity to a certain parameter, in most instances, they provide a useful measure of sensitivity under the stimulus conditions tested in this study.
|
|
DISCUSSION |
|---|
|
Relation to previous studies
The majority of physiological studies involving AMs have used SAM stimuli (for reviews, see Frisina 2001; Joris et al. 2004; Langner 1992) to show that the responses of many neurons in the ascending auditory pathway are strongly dependent on the frequency of the modulation tone. In this study, in addition to SAM stimuli, we presented pulse train stimuli in which not only FMOD but also DUR, IPI, and DC were systematically varied. Our results showed that many neurons in the auditory midbrain are sensitive not only to FMOD but also to other AM parameters (typically DUR and DC). Based on these results and the complexity of AMs as an acoustical property, any conclusions about the representation of AMs in neural responses based on SAM stimuli alone must be carefully evaluated.
Our study is not the first to show that neurons in the auditory midbrain are sensitive to AM parameters other than FMOD. For example, studies in the frog have shown that the responses of some neurons in the torus semicircularis are sensitive to DC (Alder and Rose 2000) and DUR (Gooler and Feng 1992), and studies in the in ICC of bats and mice have shown neurons that are sensitive to DUR (Brand et al. 2000; Casseday et al. 1994; Fremouw et al. 2005). Furthermore, if AMs were indeed represented in the auditory midbrain simply by their periodicity, responses to AM stimuli would be invariant to changes in stimulus parameters that did not affect FMOD. In this study, we showed that many neurons in the ICC displayed different tuning to FMOD in SAM tone and SAM noise stimuli, indicating that the response to AMs is dependent on the spectral energy of the carrier signal. Spectral energy is only one of many examples of stimulus parameters unrelated to periodicity that can cause changes in tuning to FMOD; others include mean level, modulation depth, background noise level, and spatial position (Koch and Grothe 2000; Krishna and Semple 2000; Rees and Palmer 1989).
Technical considerations
Within the ICC at least two distinct subsets of neurons can be distinguished: elongated cells with dendritic trees mainly residing within a single frequency lamina and disc shaped cells with dendrites crossing several frequency laminae (Kuwada et al. 1997; Peruzzi et al. 2000). Additionally, different ICC neurons have distinct intrinsic response properties, with current injections evoking different levels of ongoing spike activity (Peruzzi et al. 2000) and hyperpolarizing currents with different time courses (Koch and Grothe 2003). Thus the possibility that we recorded from some subtypes of ICC cells and missed others that are specialized for periodicity coding has to be considered. Such a bias could be caused by a specific type of recording electrode. However, this is unlikely because we used both glass pipettes and tungsten electrodes. Another possibility is that a bias in the stereotaxic approach led us miss a specific area in the ICC that is sensitive to periodicity. This is also unlikely, not only because we recorded from widely separated electrode tracks in single animals, but also because we are not aware of any previous studies that periodicity sensitivity in the ICC is restricted to only a limited area. Our results could also be affected by our choice of anesthesia. However, ketamine is commonly used in studies of temporal processing in the auditory midbrain (Krishna and Semple 2000), and a recent study comparing the response properties of neurons in the ICC of awake and anesthetized gerbils found only small differences (Ter-Mikaelian et al. 2007). The only major difference between the responses of cells in anesthetized and awake ICC reported by Ter-Mikaelian et al. was in the mean firing rate in response to pure tones >500 ms, which is significantly longer than the stimuli used in our study. Other measures, such as first spike latency and vector strength for SAM stimuli, were remarkably similar in anesthetized and awake animals.
Periodicity maps and pitch perception
The observation that neurons in the ICC are sensitive to changes in the FMOD of SAM stimuli has been cited as evidence that AMs are represented in the midbrain by their periodicity (Langner 1992; Langner and Schreiner 1988), and many theoretical models of auditory temporal processing incorporate a bank of unambiguous periodicity filters as an essential component (e.g., Dau et al. 1997a,b; Dicke et al. 2007). Moreover, because there seems to be a topographic arrangement of the preferred FMOD of SAM tone stimuli in the ICC, it has been hypothesized that the midbrain is organized according to a "periodicity map" (Langner 1992; Schreiner and Langner 1988).
The idea of a periodicity map in the auditory midbrain is attractive for several reasons. First, it implies that the representation of periodicity in the responses of individual neurons is invariant to changes in other stimulus properties, which correlates nicely with the perceptual invariance of pitch to changes in, for example, spectral energy and phase (Houtsma and Smurzynski 1990; Schouten 2008; Seebeck 1841). Second, a topographic arrangement of periodicity allows for simple "read out" downstream. Although the importance of periodicity in responses to AMs is indisputable, our results suggest that the representation of periodicity in the auditory midbrain is not invariant to changes in other AM parameters. However, although the representation of periodicity in individual neurons in the midbrain may be variable, the possibility that an invariant representation of periodicity is created de novo in the cortex cannot be excluded (Langner et al. 1997; Schulze and Langner 1997a,b; Schulze et al. 2002).
|
|
GRANTS |
|---|
|
|
|
ACKNOWLEDGMENTS |
|---|
|
|
|
FOOTNOTES |
|---|
Address for reprint requests and other correspondence: B. Grothe, Department Biology II, Ludwig-Maximilians-University Munich, Grosshademerstr 2, 82152, Martinsried, Germany (E-mail: grothe{at}lmu.de)
|
|
REFERENCES |
|---|
|
Brand A, Urban R, Grothe B. Duration tuning in the mouse auditory midbrain. J Neurophysiol 84: 1790–1799, 2000.
Breutel G, Krebs B, Grothe B. Are auditory midbrain neurons selectively sensitive to periodicity? Soc Neurosci Abstr 725.13, 2001.
Casseday JH, Ehrlich D, Covey E. Neural tuning for sound duration: role of inhibitory mechanisms in the inferior colliculus. Science 264: 847–850, 1994.
Dau T, Kollmeier B, Kohlrausch A. Modeling auditory processing of amplitude modulation. I. Detection and masking with narrow-band carriers. J Acoust Soc Am 102: 2892–2905, 1997a.[CrossRef][Web of Science][Medline]
Dau T, Kollmeier B, Kohlrausch A. Modeling auditory processing of amplitude modulation. II. Spectral and temporal integration. J Acoust Soc Am 102: 2906–2919, 1997b.[CrossRef][Web of Science][Medline]
Dicke U, Ewert SD, Dau T, Kollmeier B. A neural circuit transforming temporal periodicity information into a rate-based representation in the mammalian auditory system. J Acoust Soc Am 121: 310–326, 2007.[CrossRef][Web of Science][Medline]
Fremouw T, Faure PA, Casseday JH, Covey E. Duration selectivity of neurons in the inferior colliculus of the big brown bat: tolerance to changes in sound level. J Neurophysiol 94: 1869–1878, 2005.
Frisina RD. Subcortical neural coding mechanisms for auditory temporal processing. Hear Res 158: 1–27, 2001.[CrossRef][Web of Science][Medline]
Gooler DM, Feng AS. Temporal coding in the frog auditory midbrain: the influence of duration and rise-fall time on the processing of complex amplitude-modulated stimuli. J Neurophysiol 67: 1–22, 1992.
Grimault N, Bacon SP, Micheyl C. Auditory stream segregation on the basis of amplitude-modulation rate. J Acoust Soc Am 111: 1340–1348, 2002.[CrossRef][Web of Science][Medline]
Grothe B, Covey E, Casseday JH. Medial superior olive of the big brown bat: neuronal responses to pure tones, amplitude modulations, and pulse trains. J Neurophysiol 86: 2219–2230, 2001.
Houtsma AJ, Smurzynski J. Pitch identification and discrimination for complex tones with many harmonics. J Acoust Soc Am 87: 304–310, 1990.[CrossRef][Web of Science]
Joris PX, Schreiner CE, Rees A. Neural processing of amplitude-modulated sounds. Physiol Rev 84: 541–577, 2004.
Koch U, Grothe B. Interdependence of spatial and temporal coding in the auditory midbrain. J Neurophysiol 83: 2300–2314, 2000.
Koch U, Grothe B. Hyperpolarization-activated current (Ih) in the inferior colliculus: distribution and contribution to temporal processing. J Neurophysiol 90: 3679–3687, 2003.
Krishna BS, Semple MN. Auditory temporal processing: responses to sinusoidally amplitude-modulated tones in the inferior colliculus. J Neurophysiol 84: 255–273, 2000.
Kuwada S, Batra R, Yin TC, Oliver DL, Haberly LB, Stanford TR. Intracellular recordings in response to monaural and binaural stimulation of neurons in the inferior colliculus of the cat. J Neurosci 17: 7565–7581, 1997.
Langner G. Periodicity coding in the auditory system. Hear Res 60: 115–142, 1992.[CrossRef][Web of Science][Medline]
Langner G, Sams M, Heil P, Schulze H. Frequency and periodicity are represented in orthogonal maps in the human auditory cortex: evidence from magnetoencephalography. J Comp Physiol [A] 181: 665–676, 1997.[CrossRef]
Langner G, Schreiner CE. Periodicity coding in the inferior colliculus of the cat. I. Neuronal mechanisms. J Neurophysiol 60: 1799–1822, 1988.
Peruzzi D, Sivaramakrishnan S, Oliver DL. Identification of cell types in brain slices of the inferior colliculus. Neuroscience 101: 403–416, 2000.[CrossRef][Web of Science][Medline]
Rees A, Palmer AR. Neuronal responses to amplitude-modulated and pure-tone stimuli in the guinea pig inferior colliculus, and their modification by broadband noise. J Acoust Soc Am 85: 1978–1994, 1989.[CrossRef][Web of Science][Medline]
Rees A, Sarbaz A, Malmierca MS, Le Beau FE. Regularity of firing of neurons in the inferior colliculus. J Neurophysiol 77: 2945–2965, 1997.
Rossing TD, Houtsma AJ. Effects of signal envelope on the pitch of short sinusoidal tones. J Acoust Soc Am 79: 1926–1933, 1986.[CrossRef][Web of Science][Medline]
Schmiedt RA. Spontaneous rates, thresholds, and tuning of auditory-nerve fibers in the gerbil: comparison to cat data. Hear Res 42: 23–36, 1989.[CrossRef][Web of Science][Medline]
Schouten JF. The perception of subjective tones. Proc Kon Akad Wetenschap 41: 1086–1093, 2008.
Schreiner CE, Langner G. Periodicity coding in the inferior colliculus of the cat. II. Topographical organization. J Neurophysiol 60: 1823–1840, 1988.
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 15: 1077–1084, 2002.[CrossRef][Web of Science][Medline]
Schulze H, Langner G. Periodicity coding in the primary auditory cortex of the Mongolian gerbil (Meriones unguiculatus): two different coding strategies for pitch and rhythm? J Comp Physiol [A] 181: 651–663, 1997a.[CrossRef]
Schulze H, Langner G. Representation of periodicity pitch in the primary auditory cortex of the Mongolian gerbil. Acta Otolaryngol Suppl 532: 89–95, 1997b.[Medline]
Seebeck A. Beobachtungen über einige bedingungen der entstehung von tönen. Ann Phys Chem 53: 417–436, 1841.
Shannon RV, Zeng FG, Kamath V, Wygonski J, Ekelid M. Speech recognition with primarily temporal cues. Science 270: 303–304, 1995.
Siveke I, Pecka M, Seidl AH, Baudoux S, Grothe B. Binaural response properties of low-frequency neurons in the gerbil dorsal nucleus of the lateral lemniscus. J Neurophysiol 96: 1425–1440, 2006.
Ter-Mikaelian M, Sanes DH, Semple MN. Transformation of temporal properties between auditory midbrain and cortex in the awake Mongolian gerbil. J Neurosci 27: 6091–6102, 2007.
This article has been cited by other articles:
|
|
 |
|
  H.-R. Geis and J. G. G. Borst Intracellular Responses of Neurons in the Mouse Inferior Colliculus to Sinusoidal Amplitude-Modulated Tones J Neurophysiol, April 1, 2009; 101(4): 2002 - 2016. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Visit Other APS Journals Online |
TOP
ABSTRACT
INTRODUCTION
