Учебники / Hearing - From Sensory Processing to Perception Kollmeier 2007
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tones by holding the signal level to the ipsilateral ear (20 dB re: threshold) constant and varying the signal level at the contralateral ear in 5-dB steps from ~25 dB below to 25 dB above ipsilateral threshold. Second, the virtual acoustic space technique was used to manipulate the azimuth of a 200-ms “frozen” noise that was filtered by the head-related transfer functions of a standard cat (Tollin and Yin 2002). The range of azimuths was ±90° along the horizontal plane in the frontal hemisphere in 9° steps. In both conditions, mean rate and SD over the stimulus duration were computed from 20 repetitions.
The ability of LSO neurons to signal changes in ILD or azimuth via changes in discharge rate was examined. To facilitate the analyses, descriptive functions were fitted to the data for each neuron. Rate-ILD data was fitted with a four-parameter sigmoid, rate(ILD) = yo + a/(1+exp(-(ILD-ILDo)/b)) (e.g., Fig. 2A) and rate-azimuth data was fitted with a five-parameter Gaussian, rate(az) = yo + aexp(-0.5( az-azo /b)c) (Fig. 4A). These equations have no functional significance; they simply allowed the determination of a rate for any arbitrary ILD or azimuth, in steps of 0.1. These functions were also used to determine the SD. First, the empirical variance (e.g., SD2) was characterized by a two-parameter power function of rate, var(rate)=a*(rate)b. To simplify the modeling, instead of using data for each neuron, a single power function was fit to the response data from the population of neurons, separately for tone and for noise stimulation (Fig. 1). The variance was computed for each neuron by inputting to the power function the appropriate sigmoid or Gaussian rate function; SD was computed from the square root of the variance.
For each LSO neuron, the standard separation, D (Sakitt 1973), was used to compute the smallest increment in ILD or azimuth (az) just necessary to discriminate that increment based on the change in rate and response variability:
D (DILD)= |
r (ILD +DILD)-r (ILD) |
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d (ILD +DILD)) d (ILD) |
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where r(ILD) refers to rate and δ(ILD) the SD at the base ILD (or az). r(ILD + DILD) and d(ILD + ∆ILD) represent the rate and SD after a ILD (or az) increment. At each base ILD (or az), ILD (or az) was incremented and decremented until D reached 1.0 or −1.0. The increment or decrement that first resulted in a D of ±1.0 was taken as the threshold ILD (or az) at that base value. Figure 2B shows for one neuron how D changed as ILD was varied from a base of 0 dB. A D of −1 was first reached for an increment of 1.0 dB, which was taken as the threshold at that base ILD. Base ILD (or az) was then changed and the process repeated. Figure 1C shows for the same neuron the ILD thresholds as a function of base ILD, from which three values were computed (Fig. 2D): threshold ILD (or az) at 0 dB ILD (or 0°), minimum threshold ILD (or az), and the base ILD (or az) where this minimum occurred.
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A
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Fig. 1 Response variance is a power function of discharge rate. Stimuli were: A CF tones; B broadband noise filtered by HRTFs. N indicates the number of neural responses used
3Results
Results are based on 45 high-CF (>3 kHz) LSO neurons. Only one side of the brain is modeled in this chapter.
3.1 Response Variance is Characterized by a Power Function of Discharge Rate
Figure 1 shows for the population of LSO neurons a scatterplot of discharge rate and response variance. A power function provided a good fit for both tone (r2 = 0.9) and noise stimuli (r2 = 0.88) while a linear function did not (r2 of 0.5 and 0.51 for tones and noise, respectively). The power functions for the population of neurons shown in Fig. 1 were similar to that for the individual neurons themselves (e.g., insets, Figs. 2A and 4A) so the SDs were also well
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Fig. 2 A Mean rate ( filled circles) and SD (open circles) as a function of ILD for one LSO neuron. Four-parameter sigmoid fit to the rate (black line, r2 = 0.99) and SD computed from the power function in Fig. 1A (grey line). Variance vs rate for this neuron and for the population completely overlap (Fig 1A) (inset). B Standard separation, D, as a function of ∆–ILD at a 0-dB base. Threshold is the smallest ∆–ILD to first yield a D of ±1. C Threshold as a function of base ILD. D Three threshold metrics
modeled by this simplification (e.g., grey lines, Figs. 2A and 4A). Use of each neurons’ own variance-rate relationships did not alter the data presented in this chapter.
3.2Neural and Psychophysical Threshold ILDs
Figure 2 shows an example of threshold ILDs for an individual neuron (see Methods). For the population of neurons, the sigmoid function provided a good fit to the rate-ILD functions (mean r2 = 0.98 ± 0.035, n = 32 neurons). SD was computed as described above. The relationship between variance and rate for this neuron (inset, Fig. 2A) was virtually the same as that for the population of neurons in Fig. 1A. The power fit to the individual data completely overlapped the population function; this was generally true for all neurons tested with tones. The neuron in Fig. 2 yielded a threshold ILD of 1 dB at a base of 0 dB. The minimum threshold ILD was 0.9 dB at a base of −3.6 dB.
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In general, the base ILD at which the minimum occurred did not always correspond to the steepest portion of the rate-ILD function but rather tended to occur nearer to the positive inflection point.
Figure 3A plots the minimum thresholds and the threshold ILDs computed at a base of 0 dB as a function of the CF of 32 neurons. The thresholds at 0 dB ILD (range = 1–16.6 dB, mean = 5.2 ± 3.46 dB) were significantly larger than the minimum (range 0.8–13.6 dB, mean = 3.1 ± 2.38 dB) [paired t(31) = 3.89, p = 0.0005]. There was no obvious trend in the thresholds with CF. The psychophysical threshold ILD of ~1.5 dB for cats, measured at a base of 0 dB, is also shown (Fig. 3A, horizontal line, Wakeford and Robinson 1974).
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Fig. 3 A Minimum and threshold ILDs at 0 dB as a function of CF. Behavioral threshold for cats (horizontal line). B Minimum thresholds for each neuron as a function of the base ILD where they occurred (circles). Minimum population thresholds (line)
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Figure 3B shows the minimum threshold ILD for each neuron as a function of the base ILD at which it occurred. Across the population, the minimum thresholds did not necessarily occur at 0 dB, but rather at an ILD slightly favoring the ipsilateral ear. However, mean base ILD (−1.6 ± 13.2 dB, n =32) corresponding to minimum threshold was not significantly different from 0 dB [t(31) = −0.69, p>0.05]. The minimum thresholds computed across the population as a function of base ILD (Fig. 3B, line) were between 1 and 1.5 dB and were virtually invariant to changes in base ILD over ±25 dB.
3.3Neural and Psychophysical Minimum Audible Angles
Figure 4 shows a characteristic example of threshold azimuths for one neuron. For the population, the Gaussian function provided good descriptions of the rate-azimuth data (mean r2= 0.975 ± 0.037, n=32 neurons). The relationship between response variance and rate for this neuron (inset, Fig. 4A) was
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Fig. 4 A,B Example of threshold azimuth computation. Same as in Fig. 2, but for virtual space azimuth and different neuron
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virtually the same as that for the population of neurons (Fig. 1B). This was generally true of all neurons tested with the virtual space stimuli; hence, the modeled SD (grey line) provided a good description of the empirical SD. This neuron yielded a minimum threshold of 4.9° at −5°. A threshold of 4.9° was also obtained at 0°.
Figure 5A plots threshold azimuths at 0° as well as the minimum threshold as a function of CF for 30 neurons. Threshold azimuths at 0° (range 2.6–21.5°, mean=7.9±5.25°) were significantly larger than the minimum (range 2.1°–17°, mean = 5.7±3.67°) [paired t(29) =3.1, p = 0.004]. The behavioral MAAs of cats from three studies (Heffner and Heffner 1988; Martin and Webster 1987; Huang and May 1996) for noise stimuli with a base of 0° ranged from 3° to 6° (Fig. 5A, right).
Figure 5B shows the minimum threshold azimuth for each neuron as a function of the base azimuth at which it occurred. Across the population, the minimum thresholds occurred at base azimuths slightly favoring the ipsilateral
Fig. 5 A Minimum (triangles) and threshold azimuths computed at a base of 0° (circles) as a function of CF. Behavioral MAAs of cats (right). B Minimum threshold azimuths as a function of the base azimuth at which they occurred (open circles). Minimum thresholds computed across the population of neurons (line). Behavioral MAAs from cats (filled circles) and humans (squares)
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ear; the mean base azimuth (−7.7±15.25°, n = 30) corresponding to the minimum threshold was significantly different from 0° [t(29) = −2.75, p = 0.008]. The solid line shows the minimum threshold azimuth computed across the population of neurons as a function of base azimuth. As base azimuth was moved contralateral from 0°, where the population minimum threshold was 2.6°, thresholds increased substantially. But when moved ipsilateral from 0°, minimum thresholds dipped to a minimum of 2.1° at a base of −11.9°. Both human and cat MAAs for noise stimuli (Heffner and Heffner 1988) are shown as a function of base azimuth.
4Discussion
These are the first estimates of the abilities of auditory neurons to discriminate changes in the spatial locations of sounds and changes in the ILD cue to location based on discharge rate. The lowest ILD and azimuth thresholds of 0.8 dB and 2.1°, respectively, are comparable to not only cat psychophysical thresholds, but also human thresholds. Even more remarkable is the fact that the data were obtained from one of the most peripheral stages of binaural interaction in the auditory system, the LSO. The data indicate that there is sufficient information in the discharge rates of some LSO neurons to permit discrimination of azimuth and ILD at psychophysical levels without having to pool information across neurons. While such selectivity is important, it does not by itself establish a specific role for the LSO in the extraction and encoding of ILDs. However, in combination with the experimental evidence from neurophysiology, behavioral psychology, and comparative neuroanatomy cited in the Introduction, the demonstration that individual LSO neurons can discriminate ILD and azimuth with a resolution comparable to psychophysical abilities strongly reinforces the hypothesis that the functional role of the LSO is to encode the ILD cue to sound source location.
Threshold ILDs and azimuths of some individual neurons were comparable to human and cat psychophysics, even at a base of 0 dB and 0°, respectively. Human threshold ILDs have been shown to vary little with tone frequency (Grantham 1984) or changes of base ILD over ±24 dB (Hafter et al. 1977). The neural data in Fig. 3 also show relative invariance to stimulus frequency and base ILD over a range of ±25 dB, provided that the best thresholds are allowed to be taken from different neurons. Indeed, the insensitivity of threshold ILDs to changes in these parameters occurs because different neurons have their regions of best acuity at different frequencies and base ILDs. A similar finding was found for threshold azimuths (Fig. 5); the best thresholds across the population were consistent with psychophysical data for azimuths near and slightly contralateral to the midline, but failed to show the systematic increase in threshold for large base azimuths. In general, these data are consistent with the “lower envelope” hypothesis, which states that
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psychophysical performance is limited by the most sensitive neurons (Parker and Newsome 1998). On this point, our results are in agreement with observations that interaural time difference discrimination (ITD) thresholds of the most sensitive neurons in the inferior colliculus are also comparable to human psychophysical abilities (Shackleton et al. 2003). However, Hancock and Delgutte (2004) reported that the increases in ITD thresholds for noise with increases in base ITD exhibited by human observers could not be accounted for by the most sensitive neurons, but by pooling across neurons, these data could be modeled. Pooling across LSO neurons may be needed to account for the general increase in human and cat MAAs with increasing base azimuth shown in Fig. 5B.
Acknowledgements. Supported by NIH NIDCD grants DC006865 to DJT and DC00116 and DC02840 to Tom C.T. Yin.
References
Grantham DW (1984) Interaural intensity discrimination: insensitivity at 1000 Hz. J Acoust Soc Am 75:1191–1194
Hafter ER, Dye RH, Nuetzel JM, Aronow H (1977) Difference thresholds for interaural intensity. J Acoust Soc Am 61:829–834
Hancock KE, Delgutte B (2004) A physiologically based model of interaural time difference discrimination. J Neurosci 24:7110–7117
Heffner RS, Heffner HE (1988) Sound localization acuity in the cat: effect of azimuth, signal duration, and test procedure. Hear Res 36:221–232
Huang AY, May BJ (1996) Spectral cues for sound localization in cats: effects of frequency on minimum audible angles in the median and horizontal planes. J Acoust Soc Am 100:2341–2348
Martin RL, Webster WR (1987) The auditory spatial acuity of the domestic cat in the interaural horizontal and median vertical planes. Hear Res 30:239–252
Parker AJ, Newsome WT (1998) Sense and the single neuron: probing the physiology of perception. Ann Rev Neurosci 21:227–277
Sakitt B (1973) Indices of discriminability. Nature 241:133–134
Shackleton TM, Skotton BC, Arnott RH, Palmer AR (2003) Interaural time difference discrimination thresholds for single neurons in the inferior colliculus of Guinea pigs. J Neurosci 23:716–724
Tollin DJ (2003) The lateral superior olive: a functional role in sound source localization. Neuroscientist 9:127–143
Tollin DJ, Yin TCT (2002) The coding of spatial location by single units in the lateral superior olive of the cat. I. Spatial receptive fields in azimuth. J Neurosci 22:1454–1467
Wakeford OS, Robinson DE (1974) Lateralization of tonal stimuli by the cat. J Acoust Soc Am 55:649–652
47 Responses in Inferior Colliculus to Dichotic Harmonic Stimuli: The Binaural Integration
of Pitch Cues
TREVOR M. SHACKLETON1, LIANG-FA LIU2, AND ALAN R. PALMER1
1Introduction
Humans have the ability to integrate harmonics which alternate between the ears into a single percept with a pitch corresponding either to the fundamental of the entire complex or the spacing of the harmonics in each ear, depending upon the resolvability of the harmonics (Bernstein and Oxenham 2003; Houstma and Goldstein 1972). Additionally, it is possible to binaurally extract a pitch from dichotic stimuli which posses no pitch when the signal to either ear is presented alone (e.g. Bilsen et al. 1998; Cramer and Huggins 1958; Culling et al. 1998; Fourcin 1970, amongst many others). These two facts suggest that pitch percepts are generated after integration of the information from both ears (c.f. Bilsen et al. 1998; Zurek 1979). In the auditory system, whilst there is also some binaural processing in the cochlear nucleus (e.g. Mast 1970), the first major site of binaural processing is in the superior olivary complex (e.g. Goldberg and Brown 1969). The inferior colliculus is an obligatory ascending relay from these nuclei, as well as being a de novo site of binaural convergence (see, e.g. Winer and Schreiner 2005 for reviews).
In the experiments reported here we determined the extent to which pitch cues from each of the two ears are combined at the level of the inferior colliculus.
2Methods
Details of the methods have been previously published (see Shackleton et al. 2003 for details). All experiments were performed in accordance with the United Kingdom Animals (Scientific Procedures) Act of 1986. Briefly, recordings were made in the right inferior colliculus (IC) of pigmented guinea pigs
1MRC Institute of Hearing Research, University Park, Nottingham, NG7 2RD, UK, alan.palmer@ihr.mrc.ac.uk, trevor.shackleton@ihr.mrc.ac.uk
2Department of Otolaryngology, Head and Neck Surgery, Chinese PLA General Hospital 28 Fuxing Road, Beijing, P.R. China 100853, liangfaliu@hotmail.com
Hearing – From Sensory Processing to Perception
B. Kollmeier, G. Klump, V. Hohmann, U. Langemann, M. Mauermann, S. Uppenkamp, and J. Verhey (Eds.) © Springer-Verlag Berlin Heidelberg 2007
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anesthetized with urethane and Hypnorm (Janssen, High Wycombe, UK), supplemented with Hypnorm on indication by pedal withdrawal reflex. A premedication of atropine sulphate reduced bronchial secretions. The animals were placed inside a sound attenuating room in a stereotaxic frame in which hollow plastic speculae replaced the ear bars to allow sound presentation and direct visualization of the tympanic membrane.
A craniotomy was performed over the right IC, the dura reflected and recordings made using a linear array of eight glass-insulated tungsten electrodes (Bullock et al. 1988) nominally spaced at 200 m, advanced through the intact cortex by a piezoelectric motor (Burleigh Inchworm IW-700/710, Scientifica, Uckfield, UK). Extracellular action potentials were amplified and filtered between 300 Hz and 3 kHz (RA16AC, RA16PA, 4xRA16BA, Tucker-Davis Technologies, Alachua, FL). Spike sorting software (Brainware, v7.43, Jan Schnupp, Oxford University) was used to separate the responses into single units and unit-clusters.
Stimuli were delivered to each ear through sealed acoustic systems comprising custom-modified tweeters (Radioshack 40-1377; M. Ravicz, Eaton Peabody Laboratory, Boston, MA), which fitted into the hollow speculum. The output was calibrated a few millimeters from the tympanic membrane using a microphone fitted with a calibrated probe tube.
Stimuli were digitally synthesized (RP2.1, Tucker-Davis Technologies) at 50 kHz sampling rate and were output through 24-bit sigma-delta converters and a waveform reconstruction filter set at 40 kHz (135 dB/octave elliptic: Kemo 1608/500/01 modules supported by custom electronics). Stimuli were of 100 ms duration, switched on and off simultaneously in the two ears with cosine-squared gates with 2-ms rise/fall times (10% to 90%). A response area was first obtained using tonal stimuli (0 to 100 dB in 5-dB steps, 200 Hz to 20 kHz in 0.1-octave steps) followed by a rate vs level function (0 to 100 dB in 5-dB steps) for harmonic complexes with 100 Hz fundamental frequency, presented to the left, right and both ears.
Once these preliminary data had been obtained, the main experiment was run in a single block comprising 100 repeats of all 7 fundamental frequency and 8 condition combinations in random order. Stimuli consisted of harmonic series containing all of the harmonics up to 10 kHz with fundamental frequencies (F0) from 50 Hz to 400 Hz in half-octave steps. The level of individual harmonics was 50 dB SPL. Eight conditions were presented: 1) all harmonics in the left ear; 2) all harmonics in the right ear; 3) all harmonics in both ears; 4) even harmonics in the left ear and odd harmonics in the right; and 5) odd harmonics in the left ear and even harmonics in the right. The final three conditions comprised alternating phase harmonics and will not be considered here.
Data were analysed using the peri-stimulus time histograms (PSTHs), the Fourier transform of the PSTH, and the period-histogram synchronized to the F0. Of particular interest was the response-rate averaged over the stimulus duration, and the rate synchronized to either F0 or 2F0 (obtained from the Fourier transformed PSTH).Vector strength was also obtained from the periodhistogram using the method of Goldberg and Brown (1969).