An online sampled-data waveform control system* E. L. Le Page

B.M.

Johnstone

Department of Physiology, University of Western Australia, Nedlands, W. Australia

A b s t r a c t - - T h e study of frequency-discrimination mechanisms in hearing could be facilitated if it were possible to force the basilar membrane in the cochlea to vibrate with specified complex motions. It is difficult to remotely produce forcing functions suitable for the transient analysis of neural excitation mechanisms at points on the basilar membrane. The difficulties have been overcome with the use of a digital minicomputer system incorporating an independent digital-analogue generator. This control system pre-equalises the input signal for the combined transmission characteristics of all frequency-dependent stages (electronic, acoustic and mechanical) up to the basilar membrane or other point whose motion is to be controlled. Sampled data from this output constitutes feedback to the computer which synthesises a corrected input-drive waveform. The specified complex periodic motion is produced iteratively, enabling the control system to function independently of nonlinearities at any stage. An integral feature of the system is the phase-locked detection of the output signal which may be buried in noise. Keywords--Hearing, Data acquisition, Fourier synthesis, Complex waveform control

1 Introduction RECENT research into the mechanisms of hearing has been largely concerned with the area of frequency discrimination. In this regard, two sets of data indicate the existence of two distinct processes giving rise to this discrimination. The first is a mechanical tuning process which arises in the cochlea. As demonstrated by YON BI~KI~SY(1949), the last of the middle ear ossicles, the stapes, vibrates as a hydraulic piston into the fluid of the cochlea and sets up a travelling wave that progresses down the basilar membrane. Each point on the membrane exhibits a maximum amplitude of vibration for a characteristic frequency in the wave, and absorbs energy at that frequency from the wave in a resonant manner as it passes. High audio frequencies are absorbed first, near the stapes, while lower frequency components progress further along the membrane. The first precise measurements of the tuning characteristic of points along the membrane were obtained by JOHNSTONE and BOYLE (1967) using the M6ssbauer technique. Their measurements have since been verified by RHODE (1971), WILSON and JOHNSTONE (1972), K6HLOFFEL (1972) and JOHNSTONE and YATES(I 974). The second set of data mentioned above, the neural-tuning curves, represent stimulus thresholds of single units in the auditory nerve as a function of frequency; e.g., KIANG (1965), EVANS (1970). There is a marked difference between these data, which has led EVANS (1972) to postulate the existence of a second filter process which accounts "First received 30th July and in final form 5th November 1974

Medical and Biological Engineering

for the additional frequency selectivity of the neural tuning curves. The exact nature and site of the second process has yet to be determined. It may involve the hair cells, located in rows along the basilar membrane, which are responsible for the neural excitation as well as for the mechanoelectric transduction. Many properties of the hair cells are already known from electrophysiological studies (JOHNSTONE and SELLICK, 1972; DALLOS, 1973), and also electronmicroscope studies (SPOENDLIN, 1973; ANGELBORG and ENGSTROM, 1973). DALLOS et al. (1972) and ZWlSLOCKI and SOgOLICri (1973) have produced experimental evidence on postulated functional differences between the inner and outer hair cells. By measuring cochlear microphonic potentials in response to approximate square waves, DALLOS et al. (1972) concluded that the outer hair cells responded to a displacement of the basilar membrane while the inner hair cells responded to its time derivative. ZWISLOCKt and SOKOLICH (1973) measured single units in the eighth nerve, arriving at the same conclusion, but also found that the responses of the two sets of hair cells were interdependent. In so far as the experimental methods they used are valid, these findings are of considerable relevance to the data-processing capabilities of the auditory system. However, these experiments suffer from the technical difficulties of obtaining an isolated transfer function for the hair-cell process, particularly for the case of nonsinusoidal test functions. This is because it is virtually impossible to obtain electrical responses from any

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individual hair cell or isolated group of hair cells, other than by neural pathways. Secondly, it has not been possible to know exactly what stimulus is being delivered to the hair cells by the motion of the basilar membrane since it has not been possible to obtain the motion of the basilar membrane directly for nonsinusoidal vibrations at any point. The closest point in the system that DALLOS et aL (1972) and ZWISLOCKIand SOKOLICH(1973) could measure is the perilymph from which spatially summed electric potentials give an approximate indication of the motion of the basilar membrane. It is hence difficult to isolate a transfer function for the neural excitation process when it is not possible to measure the input and output accurately. To obtain more conclusive results from these experiments and to investigate neural tuning further, it would appear desirable to be able to measure the (submicroscopic) displacements of the basilar membrane to complex stimuli, and hence drive it as directly as possible with a suitable forcing function, preferably a repetitive transient of some suitable shape: e.g. a square or triangular waveform. This paper outlines our solution to this measurement and control problem. The usual acoustic input involves phase delays and standing waves in the transducer-ear air path and the complex frequency response of the middle ear. Consequently, it is impossible to preserve the waveshape of a sound stimulus through this chain at low frequencies, where the transmission characteristic is not so dependent on frequency. To avoid this problem, the stapes has been driven directly by piezoelectric or electromechanical drivers (B~K~SV, 1960; DALLOSet aL, 1972). These introduce their own problems, particularly in terms of risetime and ringing. Furthermore, even if a satisfactory drive is given to the stapes, the basilar membrane responds in a complex manner to the stapes motion. Indeed, a frequency/amplitude dependent nonlinearity is reported by RHODE (1971). The solution to this problem inevitably required some form of feedback control that could force the required motion of the basilar membrane. In turn, this required a suitable means of transducing its motion for comparison with the required motion. However, the application of real-time feedback was prevented for two reasons. First, the phase delays in the sound delivery system and external ear plus those in the cochlea due to the travelling wave were far too large for conventional feedback methods. Secondly, it required that the transduced motion of the basilar membrane be immediately available. Unfortunately the amplitude of vibration of the basilar membrane ranges from 0.0005 to 50 nm for physiological sound-pressure levels (JOHNSTONE and TAYLOR, 1970), incurring considerable measurement-noise problems. The improvement of signal/noise ratios by some form of 638

filtering is a time-dependent process, ruling out real-time feedback. Whereas the M6ssbauer technique had been successful in determining sinusoidal amplitudes and phases of the basilar membrane motion (JOHNSTONE and TAYLOR, 1970), it was not readily adaptable for the extraction of nonsinusoidalwaveform information. The remaining alternative was to use a capacitance probe to give the displacement directly, as had been used by yon B~K~sY (1949) and WILSON and JOHNSTONE (1972). A new design was produced which so far has achieved a signal/noise ratio of unity at about 10 nm peak displacement. The capacitance variations between the 100#m-diameter tip of the probe and the vibrating object produce frequency modulations in a 56 MHz carrier. The signal/noise improvement is achieved by averaging, which typically produces a 30 dB improvement in 20 s on a 200 Hz signal, making resolution of displacements of 0.1 nm readily attainable.

2 Solution

The problem of waveform control has been solved by the use of a digital minicomputer plus a small amount of additional hardware. The paper describes the method of forcing the basilar membrane or the middle-ear ossicles to perform arbitrary specified periodic motions precisely and with reasonable speed. In analogue terms, the control system functions as a generator of any arbitrarily specified waveform plus a variable equalisation amplifier, which compensates for the transmission characteristics of the system through which it is driving. For example, it is capable of generating triangular and square waves, not just at the input to the audio power amplifier, but subject to dynamic-range limitations, it can produce these at any stage through the system at the point under waveform control, by synthesising an input waveform to suit. Since the system achieves the final waveform after a few iterations, it functions independently of distortion introduced by any link in the transmission; e.g. it can produce a pure

•

generator~

Fig. 1 Block diagram of computer control system

Medical and Biological Engineering

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sinusoidal output from an amplifier with considerable harmonic distortion. It thereby allows the experimenter to determine the transmission characteristic between two points in a system directly, without the need to refer each point to a third and make compensations. In terms of experiments in hearing, it releases the experimenter from always having to refer mechanical or other transfer ratios firstly to the input sound pressure level. A block diagram of the control system is shown in Fig. I. The computer is used to synthesise waveforms as a time sequence of numbers stored in the computer memory. These numbers are then loaded into a hardwired digital-analogue generator which functions independently of the computer to produce the analogue waveform for stimulating the system under test. The response is sampled synchronously, word by word, with the generation process. This allows digital averaging of the sampled waveform over many cycles to improve its signal/noise ratio. The stimulus and response waveforms stored in the computer memory can now be compared point by point in time, or in the frequency domain to determine the transfer function. This is then used, together with the required response waveform, to calculate a new input waveform, effectively closing the loop. The synchronous method of averaging essentially provides an alternative to the use of phase-locked loops for the extraction of repetitive, not necessarily sinusoidal, signals which are buried in noise. The implementation of the system was inexpensive and quite straightforward.

3 Design details The hardwired digital-analogue generator uses t.t.1.-compatible 256 bit m.o.s, dynamic shift egisters. These are stacked 12 registers high to

computer interface buffered accumulator

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provide 256 twelve-bit words for storing one or more complete cycles of the complex waveform. The contents of these registers are cycled in an endless loop, and the output is connected to a digital-analogue convertor. Switching transients are removed by the use of a sharp cutoff lowpass filter. The 'on-chip' multiplexing of the shift registers enables them to be clocked at 5 MHz, which would allow a maximum cycle frequency of about 20 kHz. However, the particular digital-analogue convertor used in the prototype limited the frequency to less than this, but is still quite adequate for the application. A functional block diagram of the control system is shown in Fig. 2. The generator operates in two modes, LOAD and RUN, and is controlled by a bistable (7474) which is set by a program instruction. In the LOADmode, the desired waveform is strobed, word by word, in parallel into the shift registers from the buffered accumulator by a programmed pulse. Following the loading of all 256 words, the bistable is reset to the RUN mode. This permits the data to be continuously cycled at a rate determined by the generator's internal clock which is set by a front-panel potentiometer and range switch The fundamental frequency can be varied from 4 Hz to 6 kHz, adequate clock stability being obtained from a t.t.1, monostable (74121) connected as an astable multivibrator. (See Fig. 3A.) The multiplexers are controlled by a bistable multivibrator. The quad-multiplexers channel the data to the register inputs from either the computer or register outputs. The clock multiplexer allows either the computer or the internal clock to provide the shift pulses. The generator can be loaded in this manner in less than 3 ms. This is done by a short program written in machine language. Fig. 3B shows the details of the synchronisation-pulse circuitry. This consists of an eight-bit binary

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Medical and Biological Engineering

.

analogue m

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counter from which one can derive pulses for synchronisation of displays and data collection. The generator can produce higher fundamental frequencies if more than one cycle is stored in the generator. Thus, the synchronisation circuit uses the flexibility of the counters to provide pulses more often than 1 in 256 if required. Once loaded, the generator can be used as an auxiliary display device. In this role it has a number of advantages over digital-data display under software control. In addition to providing faster displays, it releases the central processor for other purposes. The latter consideration was vital in our case, since the 1.2/ts cycle time of the computer did not permit the simultaneous generation of waveforms using the computer real-time clock and data collection using interrupts. The usefulness of the generator as a display device could be extended further by duplicating the data circuitry to permit multiple channels or independent x y displays. The relatively small costs of construction are very competitive to the use of other types of memory displays. The analogue-data collection is carried out by the computer in multichannel-analyser fashion. The sampling pulses are derived from the generator clock that also supplies the m.o.s, circuitry. This

maintains the point-by-point synchronism between the generated and sampled waveforms. The 256word generator imposes on the length of the computer data arrays the restriction of being fixed at 2" words, where n is an integer less than or equal to 8. Nevertheless, this provides some advantages as well, particularly in saving time and simplifying the datacollection programs. The speed of operation of the analogue-digital convertor used here is generally lower than that of the generator shift. This depends on the required resolution and the frequency setting of the generator. Consequently, we sample every m generator clock pulses and store the result in every mth bin in the array. The factor m is chosen to correspond to the maximum possible sampling frequency of the a.d.c, but must be odd to cover all the bins in the data array. The divide-by-m circuit is shown in Fig. 3c. The decade counters (7490) count the clock pulses continuously, but are reset immediately prior to sampling and subsequently after every m clock pulses. The ENABLE line is set to HI by the computer when sampling is about to commence, and the first synchronisation pulse following that sets the bistable (7474) to reset the decade counters and allow the monostable to pass sampling pulses. Sampling of a complete waveform thus takes m

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Medical and Biological Engineering

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cycles of the generated waveform, and following this the computer resets the bistable until it is ready to sample the next cycle. The increment m selectable between 1 and 99 by front-panel switches, and the number of cycles to be averaged, are entered into the computer in the instruction to sample. The method of averaging used is determined by speed requirements. A running average with each cycle would be too time-consuming, since it requires arithmetic calculation for each point sampled. Instead, the sample procedure has been considerably shortened by simple additions of the values of the current sweep to the totals of all previous sweeps, followed by a single division per point at the completion of sampling. This method, however, produces very large accumulated sums requiring more than one memory location per bin. Rather than using doubleprecision arithmetic, further time has been saved by arranging that, if any memory bin overflowed, a corresponding bin in a second array would be incremented Following the completion of sampliug, the scaling of the two arrays is handled in double precision.

Consequently, in the computational method adopted we determine, for each element of the input vector, the ratio of the magnitudes and the difference in the phases of the transfer function and the specified vector, respectively. Determination of the transform matrix H requires obtaining n data vectors in response to n input vectors that span the n dimensions of the space. We define matrices El = [ F . Fi2...G.] and Fo = [Fol Fo2...Fo,]. Then H = Fo F~- ~ provided that det F~ r 0. In the linear case, this trivially involves finding the magnitude ratios and the phase differences as before. Depending on the system, however, this method may be useful for nonlinear systems if the nonlinearity is simple (Appendix a). In our case, the non-linearities are of more than one type and we have adopted an iterative method of waveform control. 4 Iteration procedure

3 Method of waveform control To produce a specified waveform at the output of the system being studied, the procedure is simplified by expressing the stimulus and response waveforms f~(t) and .fo(t) as finite sequences of orthogonal functions. The complex f.f.t, offers immediate computational advantages in the use of complex Fourier series for this purpose. We can represent the discrete Fourier transforms of the periodic waveforms as vectors Fdo~) and Fo(cO) in complex linear spaces C m and C", respectively. In the general case, these will be related by a vector-valued function ~,~ : C" ~ C", Fl ~ Fo.

The first step is to determine the transfer function for the required fundamental frequency. The complex fast Fourier transform (Appendix b) is used to determine the discrete spectra of the various waveforms generated and sampled. The particular version was written by ROTHMAN(1968). For linear systems the transfer function is the Fourier transform of the impulse-response function. A good first approximation to the transfer function can be obtained by sampling the mechanical response to repetitive clicks which are readily produced by the generator. Both the click waveform and the impulse response are then fast-Fourier

If the system is linear, superposition applies and there will be no interaction between the harmonics. We assume for simplicity that m = n. Then the linear map can be represented by a diagonal matrix H, so that Fo = HFi. If we specify the desired function at the output by its spectrum to be the vector Fo', then the input function to be synthesised is specified by the vector F / = H - 1 Fo'. If the complex elements of H are a , , i = 1.... , n, then the elements of H -1 = adj H/det H are 1/a,, i = 1, ..., n, provided that det H # 0. The elements of H - 1 and Fo" are complex and can be expressed in polar co-ordinates. So, Fo

' -

Foj' Rj ei*J R, = ---=exp( i(~j-qsj)'} ajj rj e~r rj

for j = 1..... n, where Rx and rj represent the magnitudes of the complex numbers, and ~bj and q/j represent the phases. Medical and Biological Engineering

Fig. 4 Stages in iteration to produce a triangular wavefrom on stapes Displacements are of the order of O" 1 nm. The figures on the left display the loudspeaker drive waveform to achieve the stapes displacements on the right

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transformed, and the transfer function is obtained as for linear systems. The spectrum of the first approximation to the required input waveform is then calculated as above, by scaling the magnitudes and rotating the phases of the harmonics of the required response function by the magnitudes and phases, respectively, of the transfer function. This is then inverse Fourier transformed to produce the initial approximation to the input waveform, which is transferred to the generator, and the system response is again sampled and Fourier transformed. This time, the sampled magnitudes and phases are compared with the spectrum of the required response to produce error differences which are scaled and used to correct the spectrum of the last synthesised waveform, and the iteration proceeds. By this procedure we have produced precisely controlled motions of the tympanic membrane, stapes and basilar membrane in the guinea pig. In our experiments, two to four iterations have typically resulted in convergence of the magnitudes and phases up to the seventh harmonic to within a few percent of specified values. Fig. 4 shows the results of each iteration in the production of a triangular motion of the stapes, together with the stimulus waveforms synthesised at each stage to drive the loudspeaker system. The ultimate precision of waveform control is determined by two limiting factors. First, noise in sampling and secondly by the dynamic range of the generator. In fact, control will not always be possible As a limiting case we consider a test system with a sharp-cutoff lowpass-.filter characteristic. We wish to force its output to perform a reasonable approximation to a discontinuous function such as a square wave. Let us assume that the frequency response of the generator can be regarded as infinite. Since the Fourier series for a square wave converges only slowly, there will be some significant higher harmonics that will be greatly attenuated, particularly if the fundamental frequency is close to that of the filter breakpoint. In practice, one finds that these harmonics are buried in noise and require sampling for very many periods to resolve them, if at all, in order to deduce the transfer function. Assuming, then, that a sufficient number of harmonics have been resolved to deduce the transfer function for a reasonable square wave, one then has the problem that 12 bits per word imposes on resolution in the synthesis of the waveform. On the one hand, the higher harmonics must have large amplitude, introducing time-quantisation errors, and on the other hand, the lower harmonics must be limited to very small numbers, incurring amplitude quantisation errors. These limitations, however, are not very significant in our application. Some optimisation is required to generate approximation to discontinuous functions on the basilar membrane. 642

It should be noted that while certain waveforms are nonrealisable for these reasons, the necessity for real-time prediction is avoided since feedback corrections are not determined in real time. This, however, limits computer waveform control to stationary systems.

5 Conclusion The principle is outlined of a digital system to establish arbitrarily chosen nonsinusoidal motions of vibrating structures within the ear, by appropriate forcing of the tympanic membrane by an acoustic stimulus. In a similar man~aer, and subject to the constraints outlined, inaccessible points in other mechanical systems could be controlled if the waveform at these points could be transduced e.g., by capacitive or optical means. The principle is also outlined for the use of inexpensive dedicated minicomputers to replace phase-locked loops within self-contained systems for extracting repetitive signal waveforms which are buried in noise. The idea could be extended for incorporating in hard-wired Fourier analysers. Since it achieves the control by first determining the transfer function between the electrical stimulus waveform and the vibrating structure, it can equally be applied to the determination of the transfer function between two points by the simple repositioning of the output transducer. In our case, the system is being applied to determining transfer functions within the ear, and controlling mechanisms within the ear which are inaccessible by previous technology. We are also currently seeking nonlinear variations of the mechanical tuning process in terms of controlled variations in the amplitude, frequency and waveforms of the stapes motion. As the system is equally suited to the collection of period histograms, the transfer function describing the neural-excitation process can be obtained with a new degree of precision. The system presented here uses a computer, which, at the time of writing, is obsolescent. No major problems are envisaged, however, in reimplementing the system in conjunction with our recently acquired PDP 11/10 computer. The major limitation of the present system concerns dynamic range and arithmetic precision, but this is largely overcome on the 16-bit machine. The use of the system to establish a controlled triangular waveform on the stapes, of peak amplitude 0.1 nm, is illustrated. Appendix (a) For example, amplitude independent nonlinearities, in which harmonics interacted only significantly for the first frequency differences, could be represented by a transformation matrix in which the nondiagonal terms account for the interactions. (b) An excellent introduction to the f.f.t, is provided by BERGLAND(1969) together with an extensive bibliography.

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Acknowledgments--We wish to thank G. K. Yates for the initial design of the capacitance probe, and Associate Professor Z. L. Budrikis for assistance in the preparation of this paper. E. Le Page is in receipt of assistance from the Arnold Yeldham and Mary Raine Medical Research Foundation. References

ANGELBORG, C. and ENGSTROM, a . (1973) The normal organ of Corti. In: A. MOLLER(ed.) Basic Mechanisms of hearing. Academic Press. B~K~SY, G. von (1949) The vibration of the cochlea partition in anatomic preparation and in models of the inner ear. J. Acoust. Soc. Am. 21,233-245. BI~KI~SY,G. yon (1960) Experiments in hearing. McGrawHill, New York, pp. 203-710. BERGLAND, G. D. (1969) A guided tour of the fast Fourier transform. IEEE Spectrum, July. DALLOS, P., BILLONE,M. C., DURRANT,J. D., WANG, C.-y and RAYNOR,S. (1972) Cochlear inner arid outer hair cells: functional differences. Science 177. DALLOS, P. (1973) The auditory periphery. Academic Press. EVANS, E. F. (1970) Narrow 'tuning' of the responses of cochlear nerve fibres emanating from the exposed basilar membrane. J. Physiol. 208, 75-76. EVANS, E. F. (1972) The frequency response and other properties of single fibres in the guinea pig cochlear nerve. J. Physiol. 226, 263-287. JOHNSTONE,B. M. and BOYLE,A. J. F. (1967) Basilar membrane vibration examined with the M0ssbauer technique. Science 158, 389-390. JOErNSTONE, B. M. and SELLICK, P. M. (1972) The

peripheral auditory apparatus. Quart. Rev. Biophys. 5, 1-57. JOHNSTONE, B. M. and TAYLOR, K. (1970) Mechanical aspects of cochlea function. In: R. PLOMP and G. SMOORENBURG(eds.) Frequency analysis and periodicity detection in hearing. A. W. Sijthoff, Leiden. JOHNSTONE, B. M. and YATES, G. K. (1974) Basilar membrane tuning curves in the guinea pig. J. Acoust. Soe. Am. 55, 584-587. KIANG, N. Y.-S (1965) Discharge patterns of single fibers in the cat's auditory nerve. Research monograph 35, MIT Press, Cambridge, Mass., USA. KOHLLOEEEL, L. U. E. (1972) A study of basilar membrane vibrations--Pts. I, II and IIl. Acustica 27, 49-89. RHODE, W. S. (1971) Observations of the vibration of the basilar membrane in squirrel monkeys using the M6ssbauer technique. J. Acoust. Soe. Am. 49, Pt. 2, 1218-1231. ROTHMAN,J. E. (1968) The fast Fourier transform and its implementation. Decuseope 7. ROTHMAN,J. E. (1971) FFTS-C fast Fourier transform subroutine for complex data. Decus Program Library, Decus No. 8-144. SPOENDLIN, H. (1973) The innervation of the cochlea receptor. In: A. MOLLER (ed.) Basic mechanisms in hearing. Academic Press. WILSON, J. P. and JOHNSTONE, J. R. (1972) Capacitive probe measures of basilar membrane vibration. In: Symposium on hearing theory. IPO, Eindhoven, The Netherlands, pp. 172-181. ZWISLOCKL J. J. and SOKOLICH,W. G. (1973) Velocity and displacement responses in auditory nerve fibres. Science 182, 64-66.

Syst6me de commande de formes d'ondes de donn6es discrimin6es en r6gime continu Sommaire---L'6tude des m6canismes de discrimination de fr6quences dans l'ouie pourrait 6tre facilit6e s'il 6tait possible de forcer la membrane basilaire de la cochl6e ~t vibrer scion des mouvements complexes prescrits. I1 est difficile de produire tt distance des fonctions de contrainte appropri6es pour l'analyse transitoire de m6canismes d'excitation neurale en des points dispos6s sur la membrane basilaire. On a surmont6 ces difficult6s en utilisant un syst6me mini-ordinateur num6rique comportant un g6n6rateur digito-analogique ind6pendant. Ce syst6me de r6gulation 6galise pr6alablement le signal d'entr6e pour les caract6ristiques d'6mission globales de tous les 6tages d6pendant de la fr6quence (61ectroniques, acoustiques et m6caniques) jusqu'~ la membrane basilaire ou autre point dont on veut r6guler le d6placement. Les donn6es discrimin6es ~ partir de cette sortie constituent la r6action destin~e ~t l'ordinateur qui synth6tise une forme d'onde d'attaque entree corrig6e. Le d~placement p6riodique complexe prescrit est produit de faqon it6rative, ce qui permet au syst6me de r~gulation de fonctionner ind6pendamment des non lin6ariti6s en tout point consid6r6. Une caract6ristique inh6rente au syst6me est la d6tection/t accrochage de phase du signal de sortie qui peut ~tre noy6 dans les parasites.

Prozessgekoppeltes Abtastregelsystem Zusammenfassung--Die Untersuchung der Frequenzdiskrimination beim Geh6r kiinnte erleichtert werden, wenn es m6glich ware, die Schadelbasismembrane in der Cochlea mit spezifischen komplizierten Bewegungen zum Schwingen zu bringen. Es ist schwierig, an Stellen auf der Schadelbasismembrane schwingende Funktionen zu erzeugen, die sich fiir die Einschwinganalyse der Nervenerregung eignen. Die Schwierigkeiten wurden durch Verwendung eines digitalen Minirechners gel6st, der einen unabhangigen Digitalanaloggenerator enthielt. Dieses Regelsystem bewirkt die Vorentzerrung des Eingangssignals fiJr die kombinierten Durchlal3charakteristiken aller frequenzabhangigen Stufen (elektronisch, akustisch und mechanisch) bis zur Schadelbasismembrane oder zu einer anderen Stelle, deren Bewegung zu steuern ist. Die von diesem Ausgang abgetasteten Daten werden zum Rechner riickgekoppelt, der eine richtige Eingangssteuerwellenform nachbildet. Die spezifische komplizierte periodische Bewegung wird im Kettenverfahren erzeugt, wodurch das Regelsystem unabhangig yon in gewissen Stadien auftretenden Nichtlinearitaten arbeiten kann. Line Eigenschaft des Systems ist das phasenverriegelte Entdecken des Ausgangssignals, das yon Rauschen iiberlagert sein kann.

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