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L Nizami

Publications and source records attributed to L Nizami.

4 recordsLinked to original sources

The intensity-difference limen for Gaussian-enveloped stimuli as a function of level: tones and broadband noise.

Van Schijndel et al. [J. Acoust. Soc. Am. 105, 3425-3435 (1999)] have proposed that the internal excitation evoked by an auditory stimulus is segmented into "windows" according to the stimulus spectrum and stimulus length. This "multiple looks" model accounts for the mid-duration hump they observed in plots of intensity-difference limens (DLs) versus pip duration for Gaussian-shaped 1- and 4-kHz tones, an effect replicated by Baer et al. [J. Acoust. Soc. Am. 106, 1907-1916 (1999)]. However, van Schijndel et al. and Baer et al. used few levels. A greater number of levels were used by Nizami (1999) for Gaussian-shaped 2-kHz tone-pips whose equivalent rectangular duration (D) was 1.25 ms. The DLs show the mid-level hump known for clicks [Raab and Taub, J. Acoust. Soc. Am. 46, 965-968 (1969)]. At some duration this pattern must become the "near-miss to Weber's law." To determine this duration, as well as the level-dependence of the mid-duration hump, DLs were established for Gaussian-shaped 2-kHz tone-pips of D = 1.25, 2.51, and 10.03 ms at levels of 30-90 dB SPL. The across-subject average DLs for the tone-pips rise up at mid-levels for D= 1.25 and D = 2.51 ms. The DLs for D=2.51 ms are larger, creating the mid-duration hump. At all durations, the new DLs are smaller at high levels than at low levels, consistent with the near-miss to Weber's law. DLs were also obtained here for Gaussian-shaped broadband-noise pips of D=0.63, 1.25, 2.51, 5.02, and 10.03 ms. The DLs for the noise-pip show a mid-level hump for all pip durations. The noise-pip DLs decrease as the pip lengthens, such that the plot of DL versus log duration shows a linear decline, with no mid-duration hump. Analysis of variance reveals that the mid-level hump coexists with the classical patterns of level-dependence, perhaps reflecting the existence of two level-encoding mechanisms, one that depends on firing-rates counted over single neurons and which is responsible for the classical patterns, and one that depends on the initial coordinated burst of neuronal spikes caused by rapid ramping, and which presumably causes the mid-level hump.

Adult↗

The fine structure of the recovering auditory detection threshold.

Detection thresholds were gathered for a 2 kHz Gaussian-shaped probe (standard deviation = 0.5 ms), centered at intervals of as little as half a millisecond over 0-30 ms following a 200 ms, 97 dB SPL, 2 kHz tone. Surprisingly, there were small, sudden rises and falls superimposed on each subject's generally smooth recovery. Even more obvious were nonmonotonicities in the standard deviation of the cumulative normal fitted to each threshold's psychometric function.

Adult↗

Auditory dynamic range derived from the mean rate-intensity function in the cat.

Loudness change is perceived over a far greater range than the discrimination range of single afferents-the "dynamic range problem" [1]. However, earlier neural count models have not fully considered how variability in the dynamic ranges, thresholds, spontaneous rates, and saturation rates of these afferents may affect overall dynamic range. In the present model, all four of these characteristics appear in a logistic rate-intensity function that fits well with sigmoidally firing cat neurons (a double logistic fits well with sloping-saturating units). These equations were averaged statistically over parameter distributions for each of three spontaneous rate groups. An average over these groups (weighted by relative group size) was used to compute discriminability for a patch of basilar membrane. The upper limit on dynamic range for a patch one critical band wide at 8 kHz was 89 dB SPL, less than implied by psychophysics [2]. A two-channel model extended the upper limit by only 3 dB, and splitting the population into four channels did not improve this limit. Seven channels, optimally combined, provided a dynamic range nearly equal to that of an ideal observer that treats each fiber as a separate channel. Moreover, the contribution of sloping-saturating units to discriminability was not as great as popularly expected. Changing the variances of fiber dynamic ranges did not strongly affect overall dynamic range in any of these computations. In sum, when the distributions of neural parameters are taken into account, local pooling can greatly mitigate the "dynamic range problem."

Animals↗