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This paper proposes new protocols for two goals: authenticated key agreement and authenticated key agreement with key confirmation in the asymmetric (public-key) setting. A formal model of distributed computing is provided, and a definition of the goals within this model supplied. The protocols proposed are then proven correct within this framework in the random oracle model. We emphasize the relevance of these theoretical results to the security of systems used in practice. Practical implementation of the protocols is discussed. Such implementations are currently under consideration for standardization [2, 3, 18].

Theoretical considerations indicate that discharges from individual neurons in the auditory pathway cannot adequately represent stimulus features. To assess how well a neural ensemble encodes information, we develop a type-based approach that assumes no a priori knowledge of how the component response patterns are inter-related and which naturally accommodates response non-stationarities. From records or simulations of a neural population, we measure neural group activity and determine what aspects of the group response crucially represent sensory variables. The measure thus produced makes optimal use of data, and can assess sensory representation fidelity from both detection and estimation theoretic viewpoints.

Individuals with a prior history of (susceptible to high altitude pulmonary edema (HAPE-S) have high resting pulmonary arterial pressures, but little data are available on their vascular response to exercise. We studied the pulmonary vascular response to exercise in seven HAPE-S and nine control subjects at sea level and at 3,810 m altitude. At each location, both normoxic (inspired PO2 = 148 Torr) and hypoxic (inspired PO2 = 91 Torr) studies were conducted. Pulmonary hemodynamic measurements included pulmonary arterial and pulmonary arterial occlusion pressures. A multiple regression analysis demonstrated that the pulmonary arterial pressure reactivity to exercise was significantly greater in the HAPE-S group. This reactivity was not influenced by altitude or oxygenation, implying that the response was intrinsic to the pulmonary circulation. Pulmonary arterial occlusion pressure reactivity to exercise was also greater in the HAPE-S group, increasing with altitude but independent of oxygenation. These findings suggest an augmented flow-dependent pulmonary vasoconstriction and/or a reduced vascular cross-sectional area in HAPE-S subjects.

Discharge patterns recorded from single auditory‐nerve fibers have demonstrated long‐range dependence, with the count variance‐to‐mean ratio growing as a power of the counting time for times greater than 0.1–1 s. The intent of this study is to provide a large dataset to enable a more detailed investigation of this phenomenon. Based on 108 recordings from a cat, we conclude that the presence of the fractal noise in the discharge rate is independent of characteristic frequency and stimulus level, but does depend on discharge rate. We measured the low‐frequency power of the fractal noise, finding its coefficient of variation to range between 6% and 26% and to decrease as firing rate increases. Such behavior is consistent with multiplicative fractal variations in models of the hair cell membrane permeability to neurotransmitter. Measured standard deviations of spike rate correspond to a sound‐pressure level difference limen of approximately 1 dB.

The initial transient chopping response of LSO neuron discharges to both monaural and binaural tone-burst stimuli in the context of a previously developed point process model of the later sustained response is analyzed and modeled. The analysis reveals the nature of the initial transient response to stimulus onset: The model's stimulus-dependent parameters vary with poststimulus-onset time while the neuron's intrinsic recovery characteristics remain constant throughout the response. By applying maximum-likelihood estimation techniques to determine the time course of the stimulus-dependent parameters, it was found that the initial excitatory and inhibitory effects decay exponentially, with their ratio determining the instantaneous rate of firing and their relative latency determining the extent of the initial chopping pattern. The "absolute" and apparent deadtime also vary exponentially during the transient portion of the response. It is concluded that the recovery characteristics of LSO neurons and, the exponential nature of the transient effects give rise to a tightly distributed latency period and a regular chopping response pattern that could encode azimuthal information.

The behavior of lateral-superior-olive (LSO) auditory neurons over large time scales was investigated. Of particular interest was the determination as to whether LSO neurons exhibit the same type of fractal behavior as that observed in primary VIII-nerve auditory neurons. It has been suggested that this fractal behavior, apparent on long time scales, may play a role in optimally coding natural sounds. We found that a nonfractal model, the nonstationary dead-time-modified Poisson point process (DTMP), describes the LSO firing patterns well for time scales greater than a few tens of milliseconds, a region where the specific details of refractoriness are unimportant. The rate is given by the sum of two decaying exponential functions. The process is completely specified by the initial values and time constants of the two exponentials and by the dead-time relation. Specific measures of the firing patterns investigated were the interspike-interval (ISI) histogram, the Fano-factor time curve (FFC), and the serial count correlation coefficient (SCC) with the number of action potentials in successive counting times serving as the random variable. For all the data sets we examined, the latter portion of the recording was well approximated by a single exponential rate function since the initial exponential portion rapidly decreases to a negligible value. Analytical expressions available for the statistics of a DTMP with a single exponential rate function can therefore be used for this portion of the data. Good agreement was obtained among the analytical results, the computer simulation, and the experimental data on time scales where the details of refractoriness are insignificant. For counting times that are sufficiently large, yet much smaller than the largest time constant in the rate function, the Fano factor is directly proportional to the counting time. The nonstationarity may thus mask fractal fluctuations, for which the Fano factor increases as a fractional power (less than unity) of the counting time.

From theoretical considerations, function-based modeling predicts the input-output characteristics of a neural system intended to perform a signal processing task within a sensory system. The sensory task under study here is the time- and level-based localization of a high-frequency, possibly amplitude-modulated, sound source in the horizontal plane. The stimulus is assumed to be represented by each ear's primary-like discharge pattern. An optimal system that extracts azimuthal angle from these discharge patterns, which represent acoustic time and level localization cues, has been derived. This system can be described as the maximization of a sum of three subsystems' outputs. The stimulus cues employed by these systems are interaural level difference for the level-based subsystem, the interaural onset-time difference for the time-based subsystem, and the interaural envelope-phase difference for the phase-based subsystem. The system encompassing all these cues is shown to trade-off the level, time, and envelope-phase cues depending upon the time since stimulus onset, the observation time, and the incident signal's level. How this system might correspond to known structures in the lower auditory pathway is described.

Fractal intensity point processes--doubly stochastic point processes with a fractal waveform intensity process--are required to describe the discharge patterns recorded from the auditory and visual systems. The Fano factor--the ratio of the variance of the number of events in an interval to the mean of this number--captures the self-similar characteristics of the intensity via two quantities: fractal dimension and fractal time. The fractal dimension is the exponent of the asymptotic power law behavior of the Fano factor with interval duration. The fractal time delineates long-term fractal behavior from short-term characteristics of the data. The average rate and self-similarity parameter of the intensity process, absolute and relative refractory effects, and serial dependence all modify the fractal time. To generate fractal intensity point processes, stochastic fractal processes are derived by applying memoryless, nonlinear transformations to fractional Gaussian noise. The intensity's amplitude distribution in combination with the Fano factor form criteria to choose the transformation that best describes data.

The authors apply the nonparametric kernel predictor to the time-series prediction problem. Because nonparametric prediction makes few assumptions about the underlying time series, it is useful when modeling uncertainties are pervasive, such as when the time series is non-Gaussian. It is shown that the nonparametric kernel predictor is asymptotically optimal for bounded, mixing time series. Numerical experiments were also performed. For the nonlinear autoregressive process, the kernel predictor is shown to outperform greatly the linear predictor; for the Henon time series, the estimated predictor closely resembles the Henon map. >

On the basis of a geometric theory of detection, the authors extend the notion of a signal constellation, a concept deeply rooted in Gaussian problems, to the non-Gaussian case. Significant differences between optimal designs for Gaussian and non-Gaussian situations are shown. In particular, square-wave signals are much more important in heavy-tailed, non-Gaussian noise situations than in Gaussian ones. Furthermore, design guidelines for non-Gaussian problems can vary with the number of signal set members and can depend on SNR. The extent to which suboptimal designs affect performance (using Gaussian-based designs in non-Gaussian situations, for example) can be predicted from calculations of the Kullback information, but only in the sense of determining how the logarithmic error probability rates differ. >

Characterization of the function of single neurons has become essential for developing advanced nodes for massively parallel computational structures. The lateral superior olive (LSO) is a nucleus located early in the ascending auditory pathway. Anatomically, each LSO neuron receives input from both ears; this fact led researchers to hypothesize that the LSO is involved in binaural hearing. Recordings from this nucleus not only confirm that LSO neurons are responsive to sounds presented in either ear, but in a particularly interesting and simple way: sounds presented in one ear excite an LSO neuron (increase its discharge rate) while sounds present at the other ear inhibit the neuron (discharge rate decreases). This mode of operation-the interaction of excitatory an inhibitory inputs to produce neural output-is the fundamental mode of neural processing. Thus, modeling LSO neurons represents a particularly clear-cut opportunity to understand basic neural processing. The approach taken in this research is to create anatomical and biophysical models of single LSO neurons and demand that the discharge patterns thus produced statistically match single-neuron recordings. With this strategy, this project seeks to (1) understand the neural mechanisms that underly basic LSO response patterns (transient chopping response to tone bursts and serial interspike interval correlations), (2) understand the transformation of fractal inputs by the LSO neuron, and (3) characterize excitatory/inhibitory interactions.

We studied lateral superior olivary (LSO) unit responses to binaural tone-bursts using a general point process approach. We show that inhibition of the ipsilaterally elicited response by contralateral stimulation cannot be modeled simply as a reduction of the ipsilateral input. Statistical analyses reveal that inhibition operates by scaling the intensity of the point process describing the ipsilateral response. In some cases the scaling process has secondary effects: Binaurally elicited discharges produce bimodal interspike interval histograms from units that produce unimodal interval histograms under monaural stimulation. We present a specific point process model that describes the scaling process and successfully replicates the observed responses to monaural and binaural stimulation of the three types of LSO units: slow choppers, fast choppers, and bimodal units. We interpret scaling as a shunting inhibitory process in these LSO neurons. By relating scaling magnitude to interaural level difference, we demonstrate the spatial sensitivity of LSO units.

It is noted that the dependence structure and the amplitude distribution of stationary random sequences are linked, with specification of one placing constraints on the other. Time-reversible processes can be Gaussian or non-Gaussian, but all Gaussian processes must be time reversible. The authors examine the thermodynamics of measurements, showing that while information can be extracted from a system without altering system entropy, most measurement techniques irreversibly alter the thermodynamic state with a consequent entropy increase. Because of the second law of thermodynamics, such entropy changes cannot be undone and measurements reflecting thermodynamic state cannot be time reversible. It is concluded that physical measurements are not time reversible, implying that only non-time-reversible processes model physically relevant signals. Consequently, Gaussian processes would seem to be imprecise representations of physical measurements. >

Acoustic telemetry devices and methods that provide directional detection. In one embodiment, a disclosed acoustic telemetry device comprises at least two acoustic sensors and an electronics module. A first of the acoustic sensors detects a communication signal that propagates along a tubing string in a first direction. A second of the acoustic sensors is configured to detect the communication signal before the first acoustic sensor. The electronics module combines the detection signals from the acoustic sensors to obtain a combined signal that substantially excludes signals propagating in a direction opposite to the communication signal. Such signal suppression may significantly enhance the communication signal's signal-to-noise ratio, thereby increasing channel capacity. The acoustic telemetry device may be configured to support logging while drilling and/or full-duplex communication.

The binaural responses of lateral superior olivary (LSO) units, involved in extracting high‐frequency cues for sound localization, are studied using a general point process approach. Previously reported results indicated that the contralateral input does not inhibit by attenuating the excitatory ipsilateral drive [Tsuchitani, J. Neurophysiol. 59, 184–210 (1988)]. Statistical analyses revealed that inhibition operates by scaling the recovery function−the post‐spike instantaneous discharge rate. Thus the inhibitory process can be summarized by a single variable: the scaling factor. The variations of the scaling factor with the interaural level difference (ILD) demonstrate that LSO units are spatially tuned. In contrast, the effect of the excitatory stimulus on LSO unit discharges is more evolved: Excitation operates by both shifting and amplifying the unit recovery function. Thus the excitatory process affects also the unit refractory period. A specific point process model describing the effects of both the...

The capabilities of classical, minimum energy, and linear predictive array processing algorithms to estimate the bearings of two equal-energy sources is examined. Signal coherence is shown to affect adversely the resolution and detection capabilities, as well as the bias characteristics, of all three algorithms. For linear arrays of equally spaced sensors, the superior resolution capability of the linear predictive algorithm is demonstrated. The value of utilizing prediction elements in the center of the array to resolve very closely spaced source bearings is demonstrated. However, the linear predictive algorithm is least capable of detecting highly coherent sources. A tradeoff is established between resolving capability and sensitivity to finite averaging. Conditions are established which indicate which algorithm is best suited to anticipated levels of signal coherence and averaging. The estimates of source bearing produced by each algorithm are shown to be asymptotically biased. The bias produced by the classical beamformer is most severe, while the minimum energy beamformer produces the least bias.

A system for the recognition of spoken phrases is described. The recognizer assumes that the input utterance contains one of a known set of allowable phrases, which may be spoken within a longer carrier sentence. Analysis is performed on a syllable-by-syllable basis with only the strong syllables considered in the recognition process. Each strong syllable is represented in terms of a set of distinguishing acoustic measurements taken at time points in and around the syllable nucleus. Phrases are represented as sequences of strong syllables. All parameters used in recognition are derived from LPC coefficients. Input speech is limited to 3.3 kHz upper frequency. Recognition is completed within 1-3 s after the utterance is spoken. An interactive training facility allows flexible composition of key phrase sets. Testing was performed for a number of phrase sets each containing ten or fewer phrases, and included equal numbers of talkers used in training and talkers not used in training. Average phrase recognition accuracy was 95 percent when parameters were derived from unquantized (i.e., 16-bit) LPC coefficients and 90 percent when the LPC coefficients were transmitted to the recognizer across the ARPA network at 3500 bits/s. The recognizer has been incorporated into a user interface system where the parameters required to set up a point-to-point ARPANET voice connection can be established remotely by voice.

The equivalence between the problem of determining the bearing of a radiating source with an array of sensors and the problem of estimating the spectrum of a signal is demonstrated. Modern spectral estimation algorithms are derived within the context of array processing using an algebraic approach. Emphasis is placed on the problem of determining the bearing of a sound source with an array. Special issues encountered in applying these estimates are discussed.

A general point process theory is developed to describe discharges recorded from lateral superior olivary units of the cat responding to ipsilaterally presented tone bursts and continuous tones. Statistical analysis of the stationary portion of the sequence of interspike intervals demonstrated that, for most units, the duration of each interval is statistically dependent only on the duration of the previous interval. A specific point process model is derived based on this analysis to describe the measured serial dependence as well as the deadtimes observed in interval histograms. The statistical dependence is well described as a shift of the interspike interval distribution, the shift depending on the duration of the previous interval. This model is then used to simulate the initial chopping in the tone burst responses. It is concluded that chopping responses and serial dependence do not result from properties of the unit's inputs; rather, they are consequences of the inherent characteristics of the unit.

Recent developments in the theory of point processes are used to analyze the accuracy by which signals are conveyed by the discharges of single auditory-nerve fibers. It is shown that the only spike trains which can convey a signal without distortion are those well-described by a Poisson process model. Theoretical predictions are made of the distortion present in the transmission of signals by auditory-nerve fiber discharge patterns. Absolute refractory effects are shown to result in more distortion than relative refractory ones. Procedures are developed to compensate for these distortions. The procedures designed to compensate for absolute refractory effects operate on the PST histogram. Computer simulations are used to confirm the theoretical predictions and to assess the accuracy of the compensation techniques.

A signal-processing software system is described which allows the simulation of systems described by block diagrams or signal-flow graphs. A high-level data-flow language describes the interconnection of the components. All configurations of interconnections are allowed, including those containing feedback. Component systems (blocks) are allowed to be multi-input, multi-output, and to be programmed in any language. Blocks are implemented as separate processes running under a UNIX2 operating system. Input and output signals are transferred between blocks via the UNIXpipe facility. Thedata type of a signal is arbitrary in the context of the compiler; within a component or system, signals and internal variables can be either floating point or fixed point. The compiler enforces strong or weak type checking of signals according to the characteristics of the blocks generating and receiving the signals. The invariance of the program to implementation data type is accomplished by the use of abstract data types. Fixed-point simulations having differing number of bits per signal and per internal variable in a block are supported. Special display software is used to allow any signal in the system to be displayed on any graphics device.

A technique for local-area communication among a large number of digital speech terminals is presented. The terminals communicate over a common wide-band broadcast transmission medium using packets. Access to the medium is controlled by a carrier-sense multiple-access protocol with collision detection. Analytic results indicate that many speech terminals having different characteristics can be supported without the necessity of buffering more than one packet. Several distributed access control algorithms are described, and a technique for predicting the performance of these algorithms is presented. Computer simulations were performed to verify the feasibility of these ideas. The simulation results agree with the prediction of the analysis.

In processing continuous-time signals by digitalmeans, it is necessary to represent the signal by a digital sequence. There are many ways other than periodic sampling for obtaining such a sequence. The requirements for such representations and some examples are discussed within the framework of simulating linear time-invariant systems. The representation of digital sequences by other digital sequences is also discussed, with particular emphasis on the use of such representations to implement a nonlinear warping of the digital frequency axis. Some applications and hardware implementation of this digital-frequency warping are described.

The discrete Fourier transform of a sequence, which can be computed using the fast Fourier transform algorithm, represents samples of the z transform equally spaced around the unit circle. In this letter, a technique is discussed and illustrated for transforming a sequence to a new sequence whose discrete Fourier transform is equal to samples of the z transform of the original sequence at unequally spaced angles around the unit circle.

It is shown that non-Gaussian time series require new analysis methods to extract their structure. Spectral analysis does not seem to provide the precise information required to analyze important aspects of a time series. The conditional expected value can, in simple cases, be related to Components of the Barrett-Lampard expansion, which provides a mathematical tool for determining the system which can generate the time series.

Discharges were recorded extracellularly from single units localized in the lateral superior olive (LSO) of barbiturate anesthetized cats. The statistical properties of the unit discharges to monaurally presented tone bursts were determined. Increasing the stimulus level of an ipsilaterally presented tone burst produced an increase in the discharge rate and the emergence and growth of a time‐locked discharge pattern in the initial portion of the response. The initial time‐locked response was transient and was followed by a nontime‐locked sustained response. In the sustained portion, increasing stimulus level produced increases in rate and changes in the interspike interval statistics. Average rate and interval statistics were found to be systematically related. LSO units were differentiated on the basis of rate and pattern of their initial discharges into two main types: the fast and slow chopping units. An analysis of the results indicated that some aspects of response type differences may be related to input characteristics rather than to neuron response mechanisms.

Superposition of point processes has often been suggested as an abstract model for the generation of neural discharge patterns due to its simplicity (input spike trains are simply merged and then retransmitted by the model neuron). The properties of the superposition of two renewal processes are examined in relation to the properties of its components. A satisfactory condition is found so that the superposition of two renewal processes possesses negative serial dependence of interevent intervals. However, the imposition of a dead time of approximately the same duration as that of the components on their superposition process is shown to remove much of this dependence. Thus, the adequacy of the simple superposition of renewal processes as a model for discharge patterns from regions that are typified by negative serial dependence (such as the lateral superior olive) may be limited, but may suffice for the discharges of primary-like units.

Multi-sensor fusion, at the most basic level, can be cast into a concise, elegant model. Reality demands, however, that this model be modified and augmented. These modifications often result in software systems that are confusing in function and difficult to debug. This problem can be ameliorated by adopting an object-oriented, data-flow programming style. For real-time applications, this approach simplifies data communications and storage management. The concept of object-oriented, data-flow programming is conveniently embodied in the black-board style of software architecture. Blackboard systems allow diverse programs access to a central data base. When the blackboard is described as an object, it can be distributed over multiple processors for real-time applications. Choosing the appropriate parallel architecture is the subject of ongoing research. A prototype blackboard has been constructed to fuse optical image regions and Doppler radar events. The system maintains tracks of simulated targets in real time. The results of this simulation have been used to direct further research on real-time blackboard systems.

Discharge patterns of single auditory‐nerve fibers in the cat responding to single tones were recorded and analyzed. Synchronization of the discharges to the stimulus tone was measured for frequencies below 6‐kHz. The synchronized response and the average discharge rate each varied with stimulus level over 20–30‐dB ranges. However, these ranges overlapped only over a 10‐dB range. Under certain conditions the average rate of response varied without accompanying variation in the synchronized response. The physiological implications of these observations are discussed.

Variable digital filters allow the frequency characteristics of a filter to be manipulated. Frequency transformations have been successfully used to obtain a variable filter from a nonrecursive prototype. However, this technique cannot be applied directly to filters having a recursive structure. A variation of this method is presented which allows variable filters to be obtained from both recursive and nonrecursive prototypes. This method has special advantages when the prototype has an equal number of poles and zeros (e.g., digital filters obtained by the bilinear transformation from an analog filter).

When the three of us decided to coedit a special issue on Statistical Signal Processing in Neuroscience, we implicitly knew that the submitted papers would span a wide field, the field measured in coordinate system with disparate axes: engineering-oriented or scientific; analysis of single neuron, populations or aggregate activity recordings; and known signal processing techniques or novel ones “invented” for the neuroscience application. We were not disappointed. The six papers chosen for this special issue represent the best of the submitted papers. The papers span a wide variety of very challenging problems. Some focus on basic estimation theory for reconstructing sensory stimuli from spike trains, while others focus on the prediction of seizure activity from EEG signals in clinical applications. Two papers are inspired by recent striking advances in Brain Machine Interface technology that is aimed at improving our basic understanding of brain function, and possibly restoring, replacing, or augmenting it when it is damaged. The paper by Lazar and Pnevmatikakis, “Reconstruction of sensory stimuli encoded with integrate-and-fire neurons with random thresholds”, employs a fresh idea—time encoding machines—inspired by neural systems that holds promise for new signal processing applications. This paper explores how to optimally decode population responses created within the time-machine encoding framework. Darmanjian and Principe’s paper, “Spatial-temporal clustering of neural data using linked-mixtures of hidden Markov models”, looks at HMM decoding of new populations using unsupervised hierarchical clustering for brainmachine interface (BMI) applications, focusing on motor control. Next, the paper by Filho et al., “Magnitude squared of coherence to detect imaginarymovement”, analyzes ERP data using cross-spectral analysis on a large scale, again for a motor BMI application. In contrast, Krusienski’s paper “A Method for visualizing independent spatio-temporal patterns of brain activity” uses a new spatio-temporal analysis of EEG data to tease out the basic independent patterns from which the measurements were constructed. Continuing along the axis of using modern algorithms, Liu, Pang and Wang explore in their paper “Epileptic seizure prediction by a system of particle filter associated with a neural network” using statistical bayesian filters to analyze EEG data. Finally, Hu et al. propose using a realistic nonlinear model rather than a linearized version to analyze multivoxel behavior in fMRI images in their paper “Nonlinear analysis of the BOLD signal.” We hope you enjoy these articles as much as we did reviewing them.

Superposition of point processes has often been suggested [I. Gath, Math. Biosci. 22, 211–222 (1974); C. E. Molnar and R. R. Pfeiffer, Proc. IEEE 56, 993–1004 (1968)] as a model for neural discharge patterns due to its intuitive appeal (input spike trains are simply merged and then retransmitted), and the tendency for successive interspike intervals in the superposition to be negatively correlated. Discharge patterns in the lateral superior olive (LSO) of the cat often display such negative serial dependence. The properties of the superposition of two identical renewal processes are examined in relation to the properties of its components. Components with increasing intensity functions are proven to yield a superposition process having negative serial dependence. However, the imposition of a dead time on a superposition process is shown to remove much of the serial dependence. For this reason, it is suggested that superposition of renewal processes is not a good model for discharge patterns having both a dead time and significant serial dependence, such as LSO discharge patterns.

Many high‐resolution bearing estimation algorithms involve the computations of the spatial correlation matrix. The utility of applying an eigenanalysis to the spatial correlation matrix has been described by many workers for a variety of algorithms. A cohesive theory for analyzing the properties of these algorithms is presented. The accuracy of bearing estimates is shown to be directly related to the time‐bandwidth product of the computations. However, the various algorithms depend on this quantity in different ways. Adaptive beamforming is shown to yield somewhat better estimates than linear predictive methods when eigenanalysis is used. [Work supported by ONR.]

The spontaneous activity and the responses to single, high‐frequency tones of single auditory‐nerve fibers are often described as a stationary point processes. Using a doubly stochastic Poisson process model as the analysis framework, it is shown that the instantaneous rate of discharge varies more than is consistent with a stationary model. Variability indices (which equal unity for a stationary Poisson process model) were found to be no smaller than 1.5 and at least as large as 6.9 for the data analyzed. Assuming this variability to be due to the presence of a noise process modulating the instantaneous rate, the spectra of the noise processes were measured. These spectra were low pass, having upper frequencies no greater than about 0.1 Hz. Some spectra may contain spectral lines, indicating the presence of nearly periodic components. The presumed noise process, whether containing periodic components or not, could not be readily related to ambient acoustic noise. [Work supported by NINCDS.]

A point process model for the responses of single auditory‐nerve fibers to time‐varying stimuli was extended to describe lateral superior olive (LSO) unit responses to monaural cf tones. In contrast to auditory‐nerve fiber discharges, successive interspike‐intervals of LSO unit discharges exhibit serial dependence. This dependence was found to be well described by a first‐order Markov model of the underlying point process. The characteristics of this model were measured from the conditional interval histogram: the usual interspike interval histogram restricted to those intervals preceded by an interval of a specified duration. From this quantity, the conditional hazard function was derived. This computation revealed that the influence of the pentultimate interval was to shift the hazard function. This detailed description of the serial dependence cannot be derived from measurements of serial correlation coefficients. The cf tone burst responses of LSO units are characterized as “chopper”‐type. Within the ...

The composition and pressure of breathing mixtures used by divers translate frequency components of their speech nonlinearly. Recently, a method of forming a new sampled time function from a given finite‐duration sampled time function in which the spectra of the two are related through a nonlinear warping in frequency has been discussed [A. Oppenheim, D. Johnson, and K. Steiglitz, “Computation of Spectra with Unequal Resolution Using the Fast Fourier Transform,” Proc. IEEE 59, 299 (1971)]. The technique is based on the expansion of the original time function in terms of a set of functions related to Laguerre functions. The coefficients in the expansion, when interpreted as samples of a new time function, have a Fourier transform that is equal to that of the original function with a nonlinear transformation in frequency. These coefficients are obtained by processing the original signal with a second‐order network in cascade with a chain of first‐order all‐pass networks. To apply the technique to translation of divers' speech, individual pitch periods are processed by the above network. These modified pitch periods are than reconvolved with a train of pitch pulses to synthesize the translated speech.

or> Meeting abstracts - A single PDF containing all abstracts in this Supplement is available here . http://www. biomedcentral.co m/content/pdf/14 71-2202 -10-S1-info.pdf