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M C Teich

Publications and source records attributed to M C Teich.

At least 19 recordsLinked to original sources
PubMed1992

Auditory-nerve action potentials form a nonrenewal point process over short as well as long time scales.

The firing patterns of auditory-nerve action potentials exhibit long-term fractal fluctuations that do not arise from the distribution of the interevent intervals, but rather from the ordering of these intervals. Using the serial interevent-interval correlation coefficient, the Fano-factor time curve, and shuffling of interevent intervals, it is shown that adjacent intervals for spontaneous firings exhibit significant correlation. The events are therefore nonrenewal over short as well as long time scales.

Evoked Potentials, Auditory↗
PubMed1990

Rate fluctuations and fractional power-law noise recorded from cells in the lower auditory pathway of the cat.

The noise properties of the sequence of action potentials recorded from adult-cat auditory nerve fibers and lateral superior olivary units have been investigated under various stimulus conditions. Large fluctuations exhibited by the spike rate, and spike clusters evident in the pulse-number distribution, both indicate an unusual underlying sequence of neural events. We present results demonstrating that (i) the firing rate calculated with different averaging times can exhibit self-similar behavior; (ii) the pulse-number distribution remains irregular even for large numbers of samples; (iii) the spike-number variance-to-mean ratio increases with the counting time T in fractional power-law fashion for sufficiently large T; and (iv) the exponent in the power law generally depends on the stimulus level. The results obtained in our laboratories support the notion that all auditory-nerve and LSO units exhibit fractal neural firing patterns, as indicated earlier by Teich (IEEE Trans. Biomed. Eng. 36, 150-160, 1989).

Action Potentials↗
PubMed1989

Spectral characteristics of the responses of primary auditory-nerve fibers to amplitude-modulated signals.

The spectral responses of cat single primary auditory nerve fibers to sinusoidal amplitude-modulated (AM) and double-sideband (DSB) acoustic signals applied to the ear were examined. DSB is an amplitude-modulated signal with a suppressed carrier. Period histograms were compiled from the neural spike-train data, and the frequency spectrum was determined by Fourier transforming these histograms. For DSB signals, spectral components were found to be present at the frequencies of the stimulus as well as at certain combination frequencies. For AM signals, several clusters of spectral components were present. The lowest-frequency cluster consisted of components at DC, at the modulation frequency, and at its harmonics. A higher frequency cluster occurs around a component with the frequency of the carrier. The components of cluster are separated from the carrier by the modulation frequency and its harmonics. Yet higher-frequency clusters appear around multiples of the carrier frequency with components at frequencies separated from these multiples by the modulation frequency and its harmonics. The magnitudes of these spectral components were determined for carrier frequencies located below, at, and above the characteristic frequency of the units, and for different stimulus levels, modulation frequencies, and modulation depths. The low-frequency components present in the neural spike train appear to be the result of demodulation taking place in the inner ear. The demodulated components are strong and are present over a wide range of sound levels, carrier frequencies, modulation frequencies, and nerve-fiber characteristics. This demodulation may be significant for speech recognition.

Acoustic Stimulation↗
PubMed1989

Spectral characteristics of the responses of primary auditory-nerve fibers to frequency-modulated signals.

The spectral responses of cat single primary auditory nerve fibers to sinusoidal frequency-modulated acoustic signals applied to the ear are examined. Period histograms were constructed from the neural spike-train data, and the frequency spectrum was determined by Fourier transforming these histograms. Several clusters of spectral components were present. The lowest-frequency cluster consists of components at DC, at the modulation frequency, and at its harmonics. In the next cluster, components surround the carrier frequency and are separated from it by the modulation frequency and its harmonics. Higher-frequency clusters surround frequencies that are twice and three times the carrier frequency. The components in each cluster are separated from the multiples of the carrier frequency by the modulation frequency and its harmonics. The magnitudes of the spectral components were investigated for carrier frequencies located below, at, and above the unit characteristic frequency, and for different signal levels, modulation frequencies, and modulation indices. The components at the modulation frequency and its harmonics were strong and present over a wide range of signal levels, carrier frequencies, modulation frequencies, and nerve-fiber characteristics. The presence of components at the modulation frequency indicates that a demodulation process is occurring. This process may be significant for speech recognition.

Acoustic Stimulation↗
PubMed1989

Fractal character of the auditory neural spike train.

Long-counting-time pulse-number distributions (PND's) were measured from a broad variety of cat primary auditory fibers using different tone and noise stimuli, counting times T, and number of samples NT. Whereas short-counting-time PND's (T approximately 50 ms) manifest the presence of spike pairs (an enhancement of even over odd-count probabilities), the irregular shapes of the long-counting-time PND's (T approximately greater than 0.1 s) reveal that the underlying sequence of action potentials consists of spike clusters when viewed on a longer time scale. For all units measured, the count variance-to-mean ratio (Fano factor) F(T) varied little over some 90 dB change in the stimulus level. On the other hand, F(T) increased substantially as T and/or NT were increased, corresponding to the capture of larger and larger spike clusters in the counting time. A relationship is developed between the Fano-time function F(T) and the normalized coincidence rate function, g(tau) versus delay time tau. A plausible form for g(tau) leads to a Fano-time function in good accord with the data. The observed power-law growth of the Fano factor for large counting times [F(T) approximately T alpha where 0 less than alpha less than 1] is accompanied by a power-law decay of the coincidence rate for large delay times [g(tau) approximately tau alpha -1] and a power-law form for the power spectral density at low frequencies [S(f) approximately f -alpha]. The behavior of the PND's and the scale invariance implicit in these fractional-power-law relationships suggest that the neural events on all primary auditory fibers exhibit fractal behavior for sufficiently large times (sufficiently low frequencies). The spike pairs and spike clusters in the PND's are natural consequences of this behavior. The fractal dimension D identical to alpha is estimated to be in the range of 0.3 approximately less than D approximately less than 0.9 for counting times in the range 0.1-10 s. The fractal dimension provides a measure of the degree of event clustering, or irregularity of a sequence of events, that is preserved over different time scales. PND's from low-skew vestibular units, in contrast, do not exhibit fractal behavior. It is suggested that auditory neural-firing patterns may serve to efficiently sample natural fractal noises.

Action Potentials↗
PubMed1988

Multinomial pulse-number distributions for neural spikes in primary auditory fibers: theory.

We previously reported experimental short- and long-counting-time pulse-number distributions (PND's) for the neural spike train in cat primary auditory nerve fibers. Data were obtained for spontaneous activity, pure-tone stimuli with a wide range of frequencies and intensity levels, and Gaussian noise. The irregular shapes of the PND's are an indication of the presence of spike clusters of various sizes in the neural impulse train. We develop a family of theoretical cluster counting distributions and examine their suitability for describing the experimental PND's. The reduced-quintinomial distribution provides theoretical results that describe the characteristics of the PND's quite well, accounting for the smooth or scalloped behavior of short-counting-time data, the jagged nature of long-counting-time data, and the Poisson-like character of very-short-counting-time data. This family of distributions admits values for the spike-number mean-to-variance ratio that are independent of stimulus level, in agreement with experimental observation. A number of procedures for fitting the theoretical distributions to the experimental PND's are studied. These include the use of a minimum mean-square error criterion, the factorial moments of the data, and the discrete Fourier transform of the PND. The first of these techniques appears to be the most useful.

Animals↗
PubMed1985

Multiplication and refractoriness in the cat's retinal-ganglion-cell discharge at low light levels.

Measurements of the pulse-interval distribution and pulse-number distribution for cat retinal ganglion cells in darkness and light have been carried out by Barlow, Levick, and Yoon. The experimental results for an on-center brisk-sustained cell are in accord with a mathematical model incorporating four features: Poisson quantum fluctuations, additive dark noise, multiplication noise (random multiple neural spikes per absorbed quantum), and refractoriness. The data cannot be properly explained by a model lacking any one of these features. Parameters extracted from the model are in good agreement with physiological values.

Animals↗
PubMed1985

Pulse-number distribution for the neural spike train in the cat's auditory nerve.

Pulse-number distributions (PNDs) were recorded from primary afferent fibers in the auditory nerve of the cat, using standard extracellular microelectrode recording techniques. Pure-tone and broadband-noise stimuli were used. The number of neural spikes (pulses) n was measured in a set of contiguous intervals, each of duration T seconds. The quantity n varies from one interval to another. These data were then used to determine the PND, which is the probability p(n,T) of occurrence of n spikes in the time T, versus the number n. The estimated mean and variance of p(n,T) were obtained. Two different values of T were used. An unexpected observation was that the count mean-to-variance ratio R is relatively constant and independent of the stimulus intensity. Use of the PND as a statistical measure of the underlying neural point process has a number of virtues. For example, the PND readily exhibits the existence of spike clusters (e.g., pairs) for some units. The PND is essentially unaffected by time jitter and time quantization and provides a statistically significant measure for units firing at low rates. A study of the scaled and unscaled pulse-interval distributions (PIDs), under conditions of spontaneous firing, demonstrates that the occurrences of neural events are generally not describable by a renewal process. Our investigation shows that none of the point processes customarily used to model the auditory neural spike train is consistent with all of the data. It appears that the encoding of acoustic information into nerve spikes in the peripheral auditory system takes the form of a cluster point process similar to the Neyman-Scott type. For pure-tone excitation, the PND will be well represented as a multinomial distribution in this case.

Animals↗
PubMed1982

Multiplication noise in the human visual system at threshold: 2. Probit estimation of parameters.

A mathematical technique is described that relates detection model parameters to stimulus magnitude and experimental probability of detection. The normalizing transform is used to make the response statistics approximately Gaussian. Conventional probit analysis is then applied. From measurements at M stimulus levels, a system of M equations is solved and estimates of M unknown parameters of the detection model are obtained. The technique is applied to a threshold vision model based on additive and multiplicative Poisson noise. Results are obtained for the parameter estimates for individual subjects, and for the standard deviation of the estimates, for various values of the stimulus energy and number of trials. A frequency-of-seeing experiment is performed using a point-source stimulus that randomly assumes 3 energy levels with 200 trials per level. With a central efficiency of 50%, the estimated ocular quantum efficiency for our four subjects lies between 12% and 23%, the average dark count at the retina lies between 8 and 36 counts, and the threshold count for our (low false-report rate) data lies between 11 and 32. The theoretical results reduce to those obtained by Barlow (J. Physiol. London 160, 155-168, 1962), in the absence of dark light and multiplication noise.

Humans↗
PubMed1982

Multiplication noise in the human visual system at threshold. 3. The role of non-Poisson quantum fluctuations.

Several kinds of light used in vision experiments produce photon statistics that are distinctly non-Poisson. Representative examples are light from a cathode-ray tube and an image-intensifier device. For the class of vision experiments in which the photon statistics play an important role, excess fluctuations produced by such light sources can alter the observed results and obscure the visual mechanisms being studied. They must therefore be accounted for in a proper way. We use the results of a Hecht-Shlaer-Pirenne type experiment, carried out with modulated Poisson light, to illustrate the point. Sensitivity and modulation depth, as well as sensitivity and reliability, are shown to be traded against each other. Finally, we demonstrate that number-state light, which is comprised of photons of an ideal kind, provides the ultimate tool for extracting information about the intrinsic noise distribution in the visual system at threshold. The state of the art in producing such light is discussed.

Humans↗
PubMed1982

Multiplication noise in the human visual system at threshold: 1. Quantum fluctuations and minimum detectable energy.

We have carried out a series of frequency-of-seeing experiments similar to those performed by Hecht, Shlaer, and Pirenne [J. Gen. Physiol. 25, 819-840 (1942)], using an Ar+ laser operated at 514.5 nm as the source of light. In certain blocks of trials, our subjects were encouraged to report as seen those trials in which the stimulus might have been present. It was determined that sensitivity and reliability were traded against each other over a broad range: for our subjects, the detection of 147 photons at the cornea with 60% frequency of seeing entailed, on the average, a 1% false-positive rate (FPR), whereas the detection of 34 photons at the cornea with 60% frequency of seeing was accompanied by a 33% FPR. A new neural-counting model has been developed in the framework of signal-detection theory. It combines Poisson stimulus fluctuations with additive and multiplicative neural noise, both of which are known to be present in the visual system at threshold. The resulting probability-of-detection curves, derived from the Neyman Type-A counting distribution, are in good accord with our experimental frequency-of-seeing data for sensible values of the model parameters. We deduce that, on the average, our four subjects are able to detect a single photon at the retina with 60% frequency of seeing, at the expense of a 55% FPR. In Part 2 of this set of papers [P.R. Prucnal and M.C. Teich, Biol. Cybern. 43, 87-96 (1982)], we use the normalizing transform, together with probit analysis, to provide improved estimates of threshold parameters, whereas in Part 3 [M.C. Teich, P.R. Prucnal, G. Vannucci, M.E. Breton, and W.J. McGill, submitted to Biol. Cybern.], we consider the effects of non-Poisson quantum fluctuations.

Adult↗
PubMed1981

A neural-counting model incorporating refractoriness and spread of excitation. II. Application to loudness estimation.

In previous paper [Teich and Lachs, J. Acoust. Soc. Am 66, 1738--1749 (1979)] we demonstrated that an energy-based neural counting model incorporating refractoriness and spread of excitation satisfactorily described the results of pure-tone intensity discrimination experiments. In this paper, we show that the identical linear filter refractoriness model (LFRM) also provides proper results for pure-tone loudness estimation experiments at all stimulus levels. In particular, as the stimulus intensity increases from very low to moderate values, the model predicts that the slope of the intensity discrimination curve will climb from 1/2 toward 1, whereas the slope of the loudness function will gradually decline below 1 in this same region. For sufficiently high values of the stimulus intensity, the slopes calculated from a simplified (crude saturation) version of the model are found to be 1--1/4N for the intensity discrimination curve and 1/2N for the loudness function. The quantity N is the number of poles associated with the tuned-filter characteristic of the individual neural channels; it is the only important free parameter in the model. Appropriate values for N appear to lie between 2 and 4, providing an asymptotic slope for the intensity discrimination curve bounded by 7/8 and 15/16 (the near miss to Weber's Law), and an asymptotic slope for the loudness function bounded by 1/4 and 1/8. The results follow from the assumption that the neural concomitant of loudness is the number of impulses observed on a collection of parallel neural channels during a fixed observation time. Our calculations are supported by Hellman and Zwislocki's [J. Acoust. Soc. Am. 33, 687--694 (1961)] observation of unit slope for the loudness function at low intensities and provide a theoretical foundation, based on spread of excitation, for Stevens' power law at high intensities.

Differential Threshold↗
PubMed1979

A neural-counting model incorporating refractoriness and spread of excitation. I. Application to intensity discrimination.

We consider in detail a new mathematical neural-counting model that is remarkably successful in predicting the correct detection law for pure-tone intensity discrimination, while leaving Weber's law intact for other commonly encountered stimuli. It incorporates, in rather simple form, two well-known effects that become more marked in the peripheral auditory system as stimulus intensity is increased: (1) the spread of excitation along the basilar membrane arising from the tuned-filter characteristics of individual primary afferent fibers and (2) the saturation of neural counts due to refractoriness. For sufficiently high values of intensity, the slope of the intensity-discrimination curve is calculated from a simplified (crude saturation) model to be 1-1/4N, where N is the number of poles associated with the tuned-filter characteristic of the individual neural channels. Since 1 less than or equal to N less than infinity, the slope of this curve is bounded by 3/4 and 1 and provides a theoretical basis for the "near miss" to Weber's law.

Auditory Perception↗