Rate coding model for discrimination of simple tones in the presence of noise.
The predictions of a rate coding model for frequency and amplitude jnd's in the presence of noise are presented for a 1-kHz, 100-ms tone. The model for the neural response incorporates physiological data on dynamic range distribution and rate suppression. A central processor is assumed to estimate the tone frequency, or amplitude, from the tone-evoked rate increment profile. This central processor acts like an ideal detector with respect to the neural noise. The effects of the neural noise as well as the signal variability on the discrimination performance level are evaluated, and the signal variability is found to be significant. The combined effect of threshold distribution, rate suppression, and signal variability make the jnd's practically invariant with noise level, in accordance with published psychophysical data. The values of the frequency jnd at high signal-to-noise ratio, however, are borderline in their consistency with the data. A more obvious discrepancy exists between the model and the psychophysical data regarding the ratio of frequency to amplitude Weber fractions, which can be resolved only by modifying the model auditory filters to be five times sharper than those measured in cats.
Erell
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Department of Electronic Systems, Faculty of Engineering, Tel Aviv University, Ramat Aviv, Israel.
D000161:Acoustic Stimulation; D000222:Adaptation, Physiological; D001307:Auditory Perception / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D009475:Neurons, Afferent / Q000502:physiology*