Biomedical subjects

Rick L Jenison

Publications and source records attributed to Rick L Jenison.

5 recordsLinked to original sources

PubMed2004

The temporal growth and decay of the auditory motion aftereffect.

The present work investigated the temporal tuning of the auditory motion aftereffect (aMAE) by measuring the time course of adaptation and recovery to auditory motion exposure. On every trial, listeners were first exposed to a broadband, horizontally moving sound source for either 1 or 5 seconds, then presented moving test stimuli after delays of 0, 2/3, or 1 2/3 seconds. All stimuli were synthesized from head related transfer functions recorded for each participant. One second of motion exposure (i.e., a single pass of the moving source) produced clearly measurable aMAEs which generally decayed monotonically after adaptation ended, while five seconds exposure produced stronger aftereffects that remained largely unattenuated across test delays. These differences may imply two components to the aMAE: a short time-constant motion illusion and a longer time-constant response bias. Finally, aftereffects were produced only by adaptor movement toward but not away from listener midline. This aftereffect asymmetry may also be a consequence of brief adaptation times and reflect initial neural response to auditory motion in primate auditory cortex.

Acoustic Stimulation↗

PubMed2004

The shape of neural dependence.

The product-moment correlation coefficient is often viewed as a natural measure of dependence. However, this equivalence applies only in the context of elliptical distributions, most commonly the multivariate gaussian, where linear correlation indeed sufficiently describes the underlying dependence structure. Should the true probability distributions deviate from those with elliptical contours, linear correlation may convey misleading information on the actual underlying dependencies. It is often the case that probability distributions other than the gaussian distribution are necessary to properly capture the stochastic nature of single neurons, which as a consequence greatly complicates the construction of a flexible model of covariance. We show how arbitrary probability densities can be coupled to allow greater flexibility in the construction of multivariate neural population models.

Action Potentials↗

PubMed2003

The effect of trajectory on the auditory motion aftereffect.

The auditory motion aftereffect (aMAE) can be induced in listeners after repeated presentation of a horizontally moving sound source. Aftereffects have also been found for the individual acoustic consequences of source motion such as amplitude or frequency modulations (AM, FM). No study, however, has investigated whether combining these changes would enhance the magnitude of the aMAE, which has appeared otherwise weak relative to its visual counterpart. AM, FM and binaural changes can occur simultaneously when sources move along common translational trajectories rather than the restricted rotational paths used in previous adaptation studies. This raises the question whether the observed weakness of the aMAE is due to the improper stimulation of units responsive to the entire macrostructure induced by translational motion. The hypothesis is tested here that if integrated motion detectors exist, then including lawful amplitude and frequency changes in adapting stimuli may enhance aftereffects. Though results indicate that interaurally moving stimuli in general induce an aMAE, the acoustic macrostructure of translational motion does not appear to increase the aftereffect. A simple cross-correlation model is used to illustrate that such acoustic modulations may allow brainstem auditory centers time to recover from adaptation to translational motion.

Acoustic Stimulation↗

PubMed2003

Directional sensitivity of neurons in the primary auditory (AI) cortex: effects of sound-source intensity level.

Transient sounds were delivered from different directions in virtual acoustic space while recording from single neurons in primary auditory cortex (AI) of cats under general anesthesia. The intensity level of the sound source was varied parametrically to determine the operating characteristics of the spatial receptive field. The spatial receptive field was constructed from the onset latency of the response to a sound at each sampled direction. Spatial gradients of response latency composing a receptive field are due partially to a systematic co-dependence on sound-source direction and intensity level. Typically, at any given intensity level, the distribution of response latency within the receptive field was unimodal with a range of approximately 3-4 ms, although for some cells and some levels, the spread could be as much as 20 or as little as 2 ms. Response latency, averaged across directions, differed among neurons for the same intensity level, and also differed among intensity levels for the same neuron. Generally, increases in intensity level resulted in decreases in the mean and variance, which follows an inverse Gaussian distribution. Receptive field models, based on response latency, are developed using multiple parameters (azimuth, elevation, intensity), validated with Monte Carlo simulation, and their spatial filtering described using spherical harmonic analysis. Observations from an ensemble of modeled receptive fields are obtained by linking the inverse Gaussian density to the probabilistic inverse problem of estimating sound-source direction and intensity. Upper bounds on acuity is derived from the ensemble using Fisher information, and the predicted patterns of estimation errors are related to psychophysical performance.

Acoustic Stimulation↗

PubMed2003

Likelihood approaches to sensory coding in auditory cortex.

Likelihood methods began their evolution in the early 1920s with R A Fisher, and have developed into a rich framework for inferential statistics. This framework offers tools for the analysis of the differential geometry of the full likelihood function based on observed data. We examine likelihood functions derived from inverse Gaussian (IG) probability density models of cortical ensemble responses of single units. Specifically, we investigate the problem of sound localization from the observation of an ensemble of neural responses recorded from the primary (Al) field of the auditory cortex. The problem is framed as a probabilistic inverse problem with multiple sources of ambiguity. Observed and expected Fisher information are defined for the IG cortical ensemble likelihood functions. Receptive field functions of multiple acoustic parameters are constructed and linked to the IG density. The impact of estimating multiple acoustic parameters related to the direction of a sound is discussed, and the implications of eliminating nuisance parameters are considered. We examine the degree of acuity afforded by a small ensemble of cortical neurons for locating sounds in space, and show the predicted patterns of estimation errors, which tend to follow psychophysical performance.

Acoustic Stimulation↗