@article{heffer_sly_fallon_white_shepherd_o'leary_2010, title={Examining the auditory nerve fiber response to high rate cochlear implant stimulation: Chronic sensorineural hearing loss and facilitation}, volume={104}, number={6}, journal={Journal of Neurophysiology}, author={Heffer, L. F. and Sly, D. J. and Fallon, J. B. and White, M. W. and Shepherd, R. K. and O'Leary, S. J.}, year={2010}, pages={3124–3135} }
 @article{sly_heffer_white_shepherd_birch_minter_nelson_wise_stephen j. o'leary_2007, title={Deafness alters auditory nerve fibre responses to cochlear implant stimulation}, volume={26}, ISSN={["1460-9568"]}, DOI={10.1111/j.1460-9568.2007.05678.x}, abstractNote={Abstract}, number={2}, journal={EUROPEAN JOURNAL OF NEUROSCIENCE}, author={Sly, David J. and Heffer, Leon F. and White, Mark W. and Shepherd, Robert K. and Birch, Michael G. J. and Minter, Ricki L. and Nelson, Niles E. and Wise, Andrew K. and Stephen J. O'Leary}, year={2007}, month={Jul}, pages={510–522} }
 @article{bruce_irlicht_white_sj o'leary_clark_2000, title={Renewal-process approximation of a stochastic threshold model for electrical neural stimulation}, volume={9}, ISSN={["0929-5313"]}, DOI={10.1023/A:1008942623671}, abstractNote={In a recent set of modeling studies we have developed a stochastic threshold model of auditory nerve response to single biphasic electrical pulses (Bruce et al., 1999c) and moderate rate (less than 800 pulses per second) pulse trains (Bruce et al., 1999a). In this article we derive an analytical approximation for the single-pulse model, which is then extended to describe the pulse-train model in the case of evenly timed, uniform pulses. This renewal-process description provides an accurate and computationally efficient model of electrical stimulation of single auditory nerve fibers by a cochlear implant that may be extended to other forms of electrical neural stimulation.}, number={2}, journal={JOURNAL OF COMPUTATIONAL NEUROSCIENCE}, author={Bruce, IC and Irlicht, LS and White, MW and SJ O'Leary and Clark, GM}, year={2000}, pages={119–132} }
 @article{young_blanchard_white_johnson_smith_ideker_2000, title={Using an artificial neural network to detect activations during ventricular fibrillation}, volume={33}, ISSN={["0010-4809"]}, DOI={10.1006/cbmr.1999.1530}, abstractNote={Ventricular fibrillation is a cardiac arrhythmia that can result in sudden death. Understanding and treatment of this disorder would be improved if patterns of electrical activation could be accurately identified and studied during fibrillation. A feedforward artificial neural network using backpropagation was trained with the Rule-Based Method and the Current Source Density Method to identify cardiac tissue activation during fibrillation. Another feedforward artificial neural network that used backpropagation was trained with data preprocessed by those methods and the Transmembrane Current Method. Staged training, a new method that uses different sets of training examples in different stages, was used to improve the ability of the artificial neural networks to detect activation. Both artificial neural networks were able to correctly classify more than 92% of new test examples. The performance of both artificial neural networks improved when staged training was used. Thus, artificial neural networks may beuseful for identifying activation during ventricular fibrillation.}, number={1}, journal={COMPUTERS AND BIOMEDICAL RESEARCH}, author={Young, MT and Blanchard, SM and White, MW and Johnson, EE and Smith, WM and Ideker, RE}, year={2000}, month={Feb}, pages={43–58} }
 @article{bruce_irlicht_white_sj o'leary_dynes_javel_clark_1999, title={A stochastic model of the electrically stimulated auditory nerve: Pulse-train response}, volume={46}, ISSN={["0018-9294"]}, DOI={10.1109/10.764939}, abstractNote={The single-pulse model of the companion paper [see ibid., vol. 46, no. 6, p. 617-29, 1999] is extended to describe responses to pulse trains by introducing a phenomenological refractory mechanism. Comparisons with physiological data from cat auditory nerve fibers are made for pulse rates between 100 and 800 pulses/s. First, it is shown that both the shape and slope of mean discharge rate curves are better predicted by the stochastic model than by the deterministic model. Second, while interpulse effects such as refractory effects do indeed increase the dynamic range at higher pulse rates, both the physiological data and the model indicate that much of the dynamic range for pulse-train stimuli is due to stochastic activity. Third, it is shown that the stochastic model is able to predict the general magnitude and behavior of variance in discharge rate as a function of pulse rate, while the deterministic model predicts no variance at all.}, number={6}, journal={IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING}, author={Bruce, IC and Irlicht, LS and White, MW and SJ O'Leary and Dynes, S and Javel, E and Clark, GM}, year={1999}, month={Jun}, pages={630–637} }
 @article{bruce_white_irlicht_sj o'leary_dynes_javel_clark_1999, title={A stochastic model of the electrically stimulated auditory nerve: Single-pulse response}, volume={46}, ISSN={["0018-9294"]}, DOI={10.1109/10.764938}, abstractNote={Most models of neural response to electrical stimulation, such as the Hodgkin-Huxley equations, are deterministic, despite significant physiological evidence for the existence of stochastic activity. For instance, the range of discharge probabilities measured in response to single electrical pulses cannot be explained at all by deterministic models. Furthermore, there is growing evidence that the stochastic component of auditory nerve response to electrical stimulation may be fundamental to functionally significant physiological and psychophysical phenomena. In this paper authors present a simple and computationally efficient stochastic model of single-fiber response to single biphasic electrical pulses, based on a deterministic threshold model of action potential generation. Comparisons with physiological data from cat auditory nerve fibers are made, and it is shown that the stochastic model predicts discharge probabilities measured in response to single biphasic pulses more accurately than does the equivalent deterministic model. In addition, physiological data show an increase in stochastic activity with increasing pulse width of anodic/cathodic biphasic pulses, a phenomenon not present for monophasic stimuli. Those and other data from the auditory nerve are then used to develop a population model of the total auditory nerve, where each fiber is described by the single-fiber model.}, number={6}, journal={IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING}, author={Bruce, IC and White, MW and Irlicht, LS and SJ O'Leary and Dynes, S and Javel, E and Clark, GM}, year={1999}, month={Jun}, pages={617–629} }
 @article{bruce_white_irlicht_sj o'leary_clark_1999, title={The effects of stochastic neural activity in a model predicting intensity perception with cochlear implants: Low-rate stimulation}, volume={46}, ISSN={["0018-9294"]}, DOI={10.1109/10.804567}, abstractNote={Most models of auditory nerve response to electrical stimulation are deterministic, despite significant physiological evidence for stochastic activity. Furthermore, psychophysical models and analyses of physiological data using deterministic descriptions do not accurately predict many psychophysical phenomena. Here, the authors investigate whether inclusion of stochastic activity in neural models improves such predictions. To avoid the complication of interpulse interactions and to enable the use of a simpler and faster auditory nerve model the authors restrict their investigation to single pulses and low-rate (<200 pulses/s) pulse trains. They apply signal detection theory to produce direct predictions of behavioral threshold, dynamic range and intensity difference limen. Specifically, the authors investigate threshold versus pulse duration (the strength-duration characteristics), threshold and uncomfortable loudness (and the corresponding dynamic range) versus phase duration, the effects of electrode configuration on dynamic range and on strength-duration, threshold versus number of pulses (the temporal-integration characteristics), intensity difference limen as a function of loudness, and the effects of neural survival on these measures. For all psychophysical measures investigated, the inclusion of stochastic activity in the auditory nerve model was found to produce more accurate predictions.}, number={12}, journal={IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING}, author={Bruce, IC and White, MW and Irlicht, LS and SJ O'Leary and Clark, GM}, year={1999}, month={Dec}, pages={1393–1404} }
 @article{janet_gutierrez_chase_white_sutton_1997, title={Autonomous mobile robot global self-localization using Kohonen and region-feature neural networks}, volume={14}, ISSN={["0741-2223"]}, DOI={10.1002/(SICI)1097-4563(199704)14:4<263::AID-ROB4>3.0.CO;2-O}, abstractNote={This article presents and compares two neural network-based approaches to global self-localization (GSL) for autonomous mobile robots using: (1) a Kohonen neural network, and (2) a region-feature neural network (RFNN). Both approaches categorize discrete regions of space (topographical nodes) in a manner similar to optical character recognition (OCR). That is, the mapped sonar data assumes the form of a character unique to that region. Hence, it is believed that an autonomous vehicle can determine which room it is in from sensory data gathered from exploration. With a robust exploration routine, the GSL solution can be time-, translation-, and rotation-invariant. The GSL solution can also become independent of the mobile robot used to collect the sensor data. This suggests that a single robot can transfer its knowledge of various learned regions to other mobile robots. The classification rate of both approaches are comparable and, thus, worthy of presentation. The observed pros and cons of both approaches are also discussed. © 1997 John Wiley & Sons, Inc.}, number={4}, journal={JOURNAL OF ROBOTIC SYSTEMS}, author={Janet, JA and Gutierrez, R and Chase, TA and White, MW and Sutton, JC}, year={1997}, month={Apr}, pages={263–282} }