A non-linear circuit for simulating OHC of the cochlea

Comparison of TMJ vibration frequencies under different joint conditions

OBJECTIVE

To compare 10 Hz wide segments of the 0 to 1000 Hz frequency distributions of vibrations recorded from five stages of internally deranged and asymptomatic temporomandibular joints (TMJs).

METHODS

TMJ vibrations were recorded from 236 patients with five stages of TMJ dysfunction: (1) reducing partial disc displacement (PDDR, n=39); (2) acute reducing complete disc displacement (A-DDR, n=39); (3) chronic reducing complete disc displacement (C-DDR, n=55); (4) un-adapted, non-reducing, complete disc displacement (DDUA, n=57); and (5) well adapted, non-reducing, complete disc displacement (DDWA, n=46). A totally asymptomatic control group with quiet TMJs (AQ, n=43) and a group with vibrating TMJs (AWV, n=93), but otherwise asymptomatic were also recorded. Frequency distributions were calculated for each group using discrete Fourier transform methods in 10 Hz increments (0-10 Hz, 10-20 Hz,…, 990-1000 Hz). The 10 Hz segments were compared between the seven groups using Student's t test with Bonferroni adjustment.

RESULTS

There were significant differences (P<0.05) in all 21 comparisons for the three segments between 80 Hz and 110 Hz, in 20 of 21 comparisons between 180 and 200 Hz, in 19 of 21 comparisons between 110 and 180 Hz and between 50 and 80 Hz.

DISCUSSION

The segments of the frequency distributions from 80 to 110 Hz provided the best differentiation between all seven groups.

An adaptive panoramic filter bank as a qualitative model of the filtering system of the cochlea: the peculiarities in linear and nonlinear mode

Outer hair cells in the cochlea of the ear, together with the local structures of the basilar membrane, reticular lamina and tectorial membrane constitute the adaptive primary filters (PF) of the second order. We used them for designing a serial-parallel signal filtering system. We determined a rational number of the PF included in Gaussian channels of the system, summation weights of the output signals, and distribution of the PF along the basilar membrane. A Gaussian panoramic filter bank each channel of which consists of five PF is presented as an example. The properties of the PF, the channel and the filter bank operating in the linear and nonlinear modes are determined during adaptation and under efferent control. The results suggest that application of biological filtering principles can be useful for designing cochlear implants with new speech encoding strategies.

Efferent-induced change in human cochlear compression and its influence on masking of tones

Several lines of evidence suggest that medial olivocochlear (MOC) efferent neurons modify cochlear output to improve signal detection in noise. In animal models, stimulation of MOC efferents reduces the amount of compression in basilar membrane (BM) growth functions. Linearization of BM growth functions may assist in extending the neural response to the signal above that of noise, leading to a decrease in masking. In order to test this hypothesis, effects of MOC efferent neurons on BM compression were studied indirectly in humans by examining the effects of contralateral noise on distortion-product otoacoustic emission (DPOAE) input-output functions at 1.0 and 2.0kHz. Compression threshold estimates from a three-segment linear regression model applied to the DPOAE functions were derived in order to determine correlations with psychophysical measurements of masking of tones at 1.0 and 2.0kHz. Contralateral noise shifted the DPOAE compression threshold to a significantly higher level at 1.0kHz, but not at 2.0kHz. A significant negative correlation between the change in DPOAE compression threshold and the amount of masking at 1.0kHz was observed, but no correlation between these variables was detected at 2.0kHz. The results of this experiment at the lower test frequency indicated that contralateral noise linearized DPOAE input-output functions, and individuals with larger DPOAE compression threshold shifts tended to exhibit less masking. Under certain conditions, decreases in cochlear compression induced by MOC efferent neurons may lead to unmasking of tones presented in noise.

An adaptive model simulating the somatic motility and the active hair bundle motion of the OHC

The outer hair cells (OHC) of the mammalian inner ear change the sensitivity and frequency selectivity of the filtering system of the cochlea using two kinds of mechanical activity: the somatic motility and the active hair bundle motion. We designed a non-linear adaptive model of the OHC employing both mechanisms of the mechanical activity. The modeling results show that the high sensitivity and frequency selectivity of the filtering system of the cochlea depend on the somatic motility of the OHC. However, both mechanisms of mechanical activity are involved in the adaptation to sound intensity and efferent-synaptic influence. The fast (alternating) component (AC) of the mechanical-electrical transduction signal controls the motor protein prestin and fast changes in axial length of the cell. The slow (direct) component (DC) appearing at high signal intensity affects the axial stiffness, the cell length and the position of the hair bundle. The efferent influence is realized by the same mechanism.

Compression, adaptation and efferent control in a revised outer hair cell functional model

In the cochlea of the inner ear, outer hair cells (OHC) together with the local passive structures of the tectorial and basilar membranes comprise non-linear resonance circuits with the local and central (afferent-efferent) feedback. The characteristics of these circuits and their control possibilities depend on the mechanomotility of the OHC. The main element of our functional model of the OHC is the mechanomotility circuit with the general transfer characteristic y=ktanh(x-a). The parameter k of this characteristic reflects the axial stiffness of the OHC, and the parameter a working position of the hair bundle. The efferent synaptic signals act on the parameter k directly and on the parameter a indirectly through changes in the membrane potential. The dependences of the sensitivity and selectivity on changes in the parameters a and k are obtained by the computer simulation. Functioning of the model at low-level input signals is linear. Due to the non-linearity of the transfer characteristic of the mechanomotility circuit the high-level signals are compressed. For the adaptation and efferent control, however, the transfer characteristic with respect to the initial operating point should be asymmetrical (a>0). The asymmetry relies on the deflection of the hair bundle from the axis of the OHC.