Biomedical subjects

Leslie M Collins

Publications and source records attributed to Leslie M Collins.

7 recordsLinked to original sources

PubMed2006

Acoustic model investigation of a multiple carrier frequency algorithm for encoding fine frequency structure: implications for cochlear implants.

Current cochlear implants provide frequency resolution through the number of channels. Improving resolution by increasing channels is limited by factors such as the physiological feasibility of increasing the number of electrodes, the inability to increase the number of channels for those already implanted, and the increased possibility of channel interactions reducing channel efficacy. Recent studies have suggested an alternative method: providing a continuum of pitch percepts for each channel based on the frequency content of that channel. This study seeks to determine the frequency resolution necessary for the highest performance gain, which may give some indication of the feasibility for implementation in implants. A discrete set of carrier frequencies, instead of a continuum, are evaluated using an acoustic model to measure speech recognition. Performance increased as the number of available frequencies increased, and substantive improvement was seen with as few as two frequencies per channel. The effect of variable frequency discrimination was also assessed, and the results suggest that frequency modulation can still provide benefits with poor frequency discrimination on some channels. These results suggest that if two or more discriminable frequencies per channel can be generated for cochlear implant subjects then an improvement in speech recognition may be possible.

Acoustics↗

PubMed2005

Predicting dynamic range and intensity discrimination for electrical pulse-train stimuli using a stochastic auditory nerve model: the effects of stimulus noise.

This work investigates dynamic range and intensity discrimination for electrical pulse-train stimuli that are modulated by noise using a stochastic auditory nerve model. Based on a hypothesized monotonic relationship between loudness and the number of spikes elicited by a stimulus, theoretical prediction of the uncomfortable level has previously been determined by comparing spike counts to a fixed threshold, Nucl. However, no specific rule for determining Nucl has been suggested. Our work determines the uncomfortable level based on the excitation pattern of the neural response in a normal ear. The number of fibers corresponding to the portion of the basilar membrane driven by a stimulus at an uncomfortable level in a normal ear is related to Nucl at an uncomfortable level of the electrical stimulus. Intensity discrimination limens are predicted using signal detection theory via the probability mass function of the neural response and via experimental simulations. The results show that the uncomfortable level for pulse-train stimuli increases slightly as noise level increases. Combining this with our previous threshold predictions, we hypothesize that the dynamic range for noise-modulated pulse-train stimuli should increase with additive noise. However, since our predictions indicate that intensity discrimination under noise degrades, overall intensity coding performance may not improve significantly.

Acoustic Stimulation↗

PubMed2004

Predicting auditory tone-in-noise detection performance: the effects of neural variability.

Collecting and analyzing psychophysical data is a fundamental mechanism for the study of auditory processing. However, because this approach relies on human listening experiments, it can be costly in terms of time and money spent gathering the data. The development of a theoretical, model-based procedure capable of accurately predicting psychophysical behavior could alleviate these issues by enabling researchers to rapidly evaluate hypotheses prior to conducting experiments. This approach may also provide additional insight into auditory processing by establishing a link between psychophysical behavior and physiology. Signal detection theory has previously been combined with an auditory model to generate theoretical predictions of psychophysical behavior. Commonly, the ideal processor outperforms human subjects. In order for this model-based technique to enhance the study of auditory processing, discrepancies must be eliminated or explained. In this paper, we investigate the possibility that neural variability, which results from the randomness inherent in auditory nerve fiber responses, may explain some of the previously observed discrepancies. In addition, we study the impact of combining information across nerve fibers and investigate several models of multiple-fiber signal processing. Our findings suggest that neural variability can account for much, but not all, of the discrepancy between theoretical and experimental data.

Acoustic Stimulation↗

PubMed2004

Predicting the threshold of pulse-train electrical stimuli using a stochastic auditory nerve model: the effects of stimulus noise.

The incorporation of low levels of noise into an electrical stimulus has been shown to improve auditory thresholds in some human subjects (Zeng et al., 2000). In this paper, thresholds for noise-modulated pulse-train stimuli are predicted utilizing a stochastic neural-behavioral model of ensemble fiber responses to bi-phasic stimuli. The neural refractory effect is described using a Markov model for a noise-free pulse-train stimulus and a closed-form solution for the steady-state neural response is provided. For noise-modulated pulse-train stimuli, a recursive method using the conditional probability is utilized to track the neural responses to each successive pulse. A neural spike count rule has been presented for both threshold and intensity discrimination under the assumption that auditory perception occurs via integration over a relatively long time period (Bruce et al., 1999). An alternative approach originates from the hypothesis of the multilook model (Viemeister and Wakefield, 1991), which argues that auditory perception is based on several shorter time integrations and may suggest an NofM model for prediction of pulse-train threshold. This motivates analyzing the neural response to each individual pulse within a pulse train, which is considered to be the brief look. A logarithmic rule is hypothesized for pulse-train threshold. Predictions from the multilook model are shown to match trends in psychophysical data for noise-free stimuli that are not always matched by the long-time integration rule. Theoretical predictions indicate that threshold decreases as noise variance increases. Theoretical models of the neural response to pulse-train stimuli not only reduce calculational overhead but also facilitate utilization of signal detection theory and are easily extended to multichannel psychophysical tasks.

Algorithms↗

PubMed2003

Psychophysical threshold variability in cochlear implant subjects.

The dramatic differences observed when comparing auditory neural responses to electrical and acoustic stimulation may illustrate one of the important mechanisms underlying the sometimes poor speech recognition abilities of individuals with cochlear implants. Recent research has suggested that the absence of a stochastic component in neural responses to electrical activation may be an important potential mechanism for this degradation in speech recognition performance. There are few psychophysical data, however, demonstrating that this stochastic behavior can be measured directly in implant subjects. In this study, variability in psychophysical threshold was investigated as a measure of the stochastic nature of the underlying neural response in human and non-human subjects implanted with intracochlear electrode arrays. Threshold data collected in both monopolar and bipolar stimulation modes at several phase durations from cat and human subjects are presented. The nature of the neural input/output curve suggests that threshold variability should increase as the slope of the input/output curve is decreased, i.e. as phase duration is increased. These predictions are confirmed by the pattern of psychophysical results measured experimentally in cat and human subjects. Furthermore, the data may suggest that subjects with higher threshold variability, i.e. a relatively greater stochastic component, are more likely to have higher speech recognition scores.

Aged↗

PubMed2003

Predicting the threshold of single-pulse electrical stimuli using a stochastic auditory nerve model: the effects of noise.

An important factor that may play a role in speech recognition by individuals with cochlear implants is that electrically stimulated nerves respond with a much higher level of synchrony than is normally observed in acoustically stimulated nerves. Recent work has indicated that the addition of noise to an electrical stimulus may result in neural responses whose statistical characteristics are more similar to those observed in acoustically driven neurons. Psychophysical data have indicated that performance on some tasks might also be enhanced by the addition of noise. However, little theoretical work has been done toward predicting the effect of noise on psychoacoustic measurements. In this paper, theoretical predictions of these effects are developed through the use of a stochastic computational model. The effect of additive noise on the input and output characteristics and aggregate threshold behavior of modeled auditory nerves (ANs) is specifically studied. This paper derives the stochastic properties of the model input and output when using adaptive threshold procedures. A closed form solution for the input, or amplitude, probability distribution is obtained via Markov models for both one-down one-up (1D1U) and two-down one-up (2D1U) experimental paradigms. The output statistics are derived by integrating over the noise-free probability mass function (PMF). All theoretical PMFs are verified by simulations with the model. Theoretical threshold is predicted as a function of noise level based on these PMFs and the predictions match simulated performance. The results indicate that threshold may be adversely affected by the presence of high levels of noise.

Action Potentials↗

PubMed2002

The effect of channel interactions on speech recognition in cochlear implant subjects: predictions from an acoustic model.

Acoustic models that produce speech signals with information content similar to that provided to cochlear implant users provide a mechanism by which to investigate the effect of various implant-specific processing or hardware parameters independent of other complicating factors. This study compares speech recognition of normal-hearing subjects listening through normal and impaired acoustic models of cochlear implant speech processors. The channel interactions that were simulated to impair the model were based on psychophysical data measured from cochlear implant subjects and include pitch reversals, indiscriminable electrodes, and forward masking effects. In general, spectral interactions degraded speech recognition more than temporal interactions. These effects were frequency dependent with spectral interactions that affect lower-frequency information causing the greatest decrease in speech recognition, and interactions that affect higher-frequency information having the least impact. The results of this study indicate that channel interactions, quantified psychophysically, affect speech recognition to different degrees. Investigation of the effects that channel interactions have on speech recognition may guide future research whose goal is compensating for psychophysically measured channel interactions in cochlear implant subjects.

Acoustic Stimulation↗