Initial Component Control In Disparity Vergence: A ... - IEEE Xplore

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 45, NO. 2, FEBRUARY 1998

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Initial Component Control in Disparity Vergence: A Model-Based Study Jia-Long Horng, John L. Semmlow,* Fellow, IEEE, George K. Hung, Member, IEEE, and Kenneth J. Ciuffreda

Abstract—The dual-mode theory for the control of disparityvergence eye movements states that two components control the response to a step change in disparity. The initial component uses a motor preprogram to drive the eyes to an approximate final position. This initial component is followed by activation of a late component operating under visual feedback control that reduces residual disparity to within fusional limits. A quantitative model based on a pulse-step controller, similar to that postulated for saccadic eye movements, has been developed to represent the initial component. This model, an adaptation of one developed by Zee et al. [1], provides accurate simulations of isolated initial component movements and is compatible with the known underlying neurophysiology and existing neurophysiological data. The model has been employed to investigate the difference in dynamics between convergent and divergent movements. Results indicate that the pulse-control component active in convergence is reduced or absent from the control signals of divergence movements. This suggests somewhat different control structures of convergence versus divergence, and is consistent with other directional asymmetries seen in horizontal vergence. Index Terms— Eye movements, initial control component, uniocular and binocular models, vergence.

I. INTRODUCTION

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HEORIES for the generation and control of eye movements are well supported with quantitative models, as they can be rigorously tested by the process of computer simulation. Through simulation, such a mathematical model often provides insight into neural strategies for eye-movement control. Specifically, over the past three decades, several model structures have been proposed to explain dynamic-vergence oculomotor control [2]–[6]. Of these models, a structure based on classical continuous feedback control has been favored. For example, Rashbass and Westheimer [2] developed the first vergence oculomotor model which stated that vergence eye movements are guided throughout the movement by visual Manuscript received June 17, 1996; revised May 1, 1997. This work was supported in part by the National Eye Institute under Grant EY07519 and by the National Academy of Sciences under a Grant-in-Aid for Research through Sigma Xi. Asterisk indicates corresponding author. J.-L. Horng is with the Department of Neurology, Olive View–UCLA Medical Center, Sylmar, CA 91342 USA. *J. L. Semmlow is with the Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08855 USA (e-mail: [email protected]). He is also with the Department of Surgery (Bioengineering), UMDNJ-Robert Wood Johnson Medical School, New Brunswick, NJ 08903 USA. G. K. Hung is with the Department of Surgery (Bioengineering), UMDNJRobert Wood Johnson Medical School, New Brunswick, NJ 08903 USA. K. J. Ciuffreda is with the Department of Vision Sciences, State College of Optometry, State University of New York, New York, NY 10010 USA. Publisher Item Identifier S 0018-9294(98)00914-8.

feedback. In addition to linear dynamic processes representing the plant and controller, they introduced a delay element of 160 ms. between the vergence error (i.e., retinal disparity) and the control process. Although simulations of the step responses fit the experimental data reasonably well, those of sinusoidal responses did not. Krishnan and Stark [4] modified the Rashbass and Westheimer model by adding a derivative element (implemented as a lead-lag element) in the forward path. The derivative component contributed only to the initial response, whereas a parallel “leaky-integrator”1 element was also incorporated to ensure that steady-state errors would be small in the simulated vergence responses. The time constant of the leaky integral component reported by Krishnan and Stark [4] was approximately 15 s, which was derived from the drift dynamics following the disappearance of a highly convergent (6 MA) stimulus. Despite the fact that their model was developed primarily on the basis of step responses, sinusoidal responses were also well simulated. However, their model required small values for the feed-forward gain, so that during maintained disparity vergence large steady-state errors could be expected. Yet, numerous experiments have shown that steady-state disparity error, termed fixation disparity, is quite small, of the order of a few minutes of arc [7], [8]. Moreover, this model was not capable of simulating the faster dynamics observed in most subjects [9]. Recently, a different feedback control model was constructed by Pobuda and Erkelens [10], [11]. Their “disparity channel” model featured a controller having a number of parallel channels, each of which featured a gain term and a leaky integrator (also implemented as a lowpass filter). A given channel was selectively activated based on the magnitude of the instantaneous retinal disparity. To simulate the extremely small disparity error (ie., several minutes of arc) during prolonged, steady-state binocular fixation, an additional leaky integrator was inserted in series with the vergence loop. Finally, their model incorporated a second-order oculomotor plant. While sinusoidal simulations from their disparitychannel model appeared to match experimental data reasonably well, simulations of step responses were less accurate. The failure of oculomotor models based on classical feedback control to produce realistic simulations suggests that a different strategy may be used. Based on considerable experimental evidence, Semmlow et al. [9] and Hung et al. 1 The integrator is described as “leaky” because it fails to hold its output value over a significant period of time after the signal has been removed (i.e., in total darkness). It can be simulated by a first-order lowpass filter.

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[6] proposed a “dual-mode” control strategy consisting of: 1) an initial component, which produces an open-loop, preprogrammed movement that dominates the initial 200–300-ms rapid portion of the vergence response, and 2) a late component which provides visual feedback control to guide the eyes to their highly accurate final position.2 One recent experimental demonstration of preprogrammed (i.e., open-loop) behavior was shown by Horng et al. [12] and Semmlow et al. [13] using disappearing step stimuli with short durations (as brief as 50 ms). Since the 160-ms latent period [2], [14] was longer than the stimulus presentation time, the resulting movements occurred in total darkness and, thus, were effectively openloop. Nonetheless, the amplitude and dynamics of the resulting movements were quite similar to those found in normal step responses, at least during the initial portion of the movement. These preprogrammed movements also had a consistent dynamic behavior which was independent of stimulus duration or target size. In addition to demonstrating preprogramming, the “disappearing step” protocol provided a simple experimental technique for isolating the initial component. In the Section IV, we show that disparity vergence dynamics are clearly direction dependent. That is, convergent movements are consistently faster than divergent movements. In addition to presenting data on vergence asymmetries, a primary objective of this paper is to present a system model representation of the initial vergence component. This model is consistent with known neurophysiology and demonstrates highly accurate simulations of isolated initial component motor behavior (as evoked by the disappearing step stimulus). The internal signals predicted by the model are also in agreement with existing neurophysiological evidence. The initial component model will be used to simulate direction-dependent nonlinearities found in the initial component responses produced by disappearing disparity step stimuli. Simulations will show that these differences in dynamics can be attributed to a difference in the underlying control signal: specifically, the lack of a significant pulse component during divergence. II. METHODOLOGY Experiments were designed to acquire vergence responses from human subjects, which were then used in developing the mathematical model. As described below, two stimulus patterns were presented and responses were monitored with a head-mounted infrared reflection eye movement system. Stimulus generation and data acquisition were under computer control. Data analysis was performed off-line. A. Experimental Apparatus and Calibration A specially designed stimulus device (Semmlow and Venkiteswaran, 1976) was used to generate a target consisting of stereoscopically-paired vertical lines 0.2 in width and 2 . in height. Only this target was visible to the subject: no other 2 We use the terms “initial” and “late” since that is their order of activation during a normal step response. However, under certain stimulus conditions only one component may appear or the two may appear alternately [9].

Fig. 1. Temporal pattern of a standard step and disappearing step stimulus. In the latter case, the step stimulus disappears after 50 or 100 ms, well before the vergence eye movement begins. The resulting eye movement itself takes place in total darkness. Note differences in time scale of each schematic response.

objects in the apparatus or room were visible. The optics of the stimulus device were carefully adjusted to ensure that the vergence stimulus was symmetrical; that is, each eye viewed equal and opposite displacements in target position. The potential influence of accommodation (which could be driven through convergence accommodation) was eliminated by presenting the target through a pinhole ( 1 mm) optically conjugate to the plane of the pupil [15], and the isolated stimulus environment did not provide any change in proximal cues [16]. B. Stimulus Generation and Data Acquisition The disappearing step protocol shown schematically in Fig. 1 was used to evoke the isolated initial component responses. This specially developed disappearing step protocol [13] takes advantage of the 160–200-ms disparity vergence response latency [2], [14]. Since the target disappeared before the motor response began, the resulting movements occurred in the absence of visual information and, therefore, were executed without visual feedback.3 The disappearing step stimuli had amplitudes of 2 , 4 , and 8 with presentation times of either 50 or 100 ms. Step changes in target vergence were generated by a computer which controlled stimulus presentation and data acquisition. To discourage prediction, the amplitude, direction,4 and time of presentation (after the subject indicated readiness by pressing a button) were randomized for each trial. Immediately following each stimulus presentation, three seconds of data were recorded. Between 16–20 responses were recorded for each stimulus pattern, of which about 80% were generally artifact-free and suitable for analysis. Common artifacts included large saccades, small saccades within the transient vergence response, and blinks. Saccades during the transient vergence response could generally be avoided when the stimulus was carefully adjusted to be symmetrical. 3 Implicit in this statement is the assumption that the eye is not moving when the target change occurs. If the eye moves while the target is visible, some visual feedback will be present. 4 Disappearing step stimuli were presented in both divergent and convergent directions, although only the convergent disappearing step responses were analyzed here.

HORNG et al.: INITIAL COMPONENT CONTROL IN DISPARITY VERGENCE

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Fig. 2. System model of the initial component of disparity vergence.

Experimental runs were repeated at least three times on each subject on separate days. Binocular eye position was recorded by means of a Skalar infrared eye movement monitor (Model 6500). This device has a linear range (within 3%) of 25 , a resolution of 1.5 min. arc, and a bandwidth of 200 Hz. A two-point calibration was performed before and after each response. The baseline position (prior to stimulus onset) was taken as the first calibration point and the final position (after 3 s) was taken as the second point. Calibrations were stored in the computer and used to construct a separate calibration curve for each eye. All data acquisition was done at a sampling rate of 120 Hz., which is well above the Nyquist frequency for vergence eye movements. C. Subjects Four subjects, between 26–50 years of age, participated in the experiment. Each subject had normal vision (20/20), although some subjects required optical correction during the experiment. Among these subjects, JS and GH were experienced and were aware of the goals of this study; JH and RC were inexperienced subjects, but had knowledge of the study’s objectives. III. INITIAL COMPONENT MODEL A. Model Development Based on the tenets of the dual-mode theory [6], [9], [13], [17], the initial component model is open-loop, i.e., it does not utilize external feedback from the visual system. This model is an adaptation of the model developed by Zee et al. [1] for use in studying saccade/vergence interactions. As shown in Fig. 2, the model consists of three major components: a pulse generator representing the motor program, a step producing integrator, and a first-order process representing the neuro-

muscular plant(s). The motivation for each of these sections is based on the known underlying oculomotor neurophysiology as described below. 1) Controller: It is well known that the saccadic controller signal has a pulse-step profile [18]–[20]. This signal is generated by “burst” neurons located in the paramedian pontine reticular formation (PPRF) of the brainstem which correlate with motoneuron activity when monkeys perform saccadic eye movements [21]–[25]. Neurophysiological evidence regarding the vergence controller less developed. Several experiments offering quantitative descriptions of the neurons relating to vergence have been undertaken [26]–[29]. Mays et al. [27] reported a linear relationship between instantaneous vergence velocity and the firing rate of burst cells located in the mesencephalic reticular formation in monkeys. They suggested that the vergence burst cells may be a primary mechanism in the vergence controller and may provide the vergence velocity signal to the motorneurons. The identification of vergence burst cells indicates that a pulse generator is a major component in the vergence control system. In the model, Fig. 2, the pulse generator is represented by a “local” feedback loop as first hypothesized by Robinson [17] for the saccadic pulse generator. This controller produces a pulse by taking the difference between the input step stimulus and a slightly delayed feedback signal [1], [17]. The shape of this pulse is influenced primarily by the rate of rise of the upslope (controlled by the “Rate Limiter” element in Fig. 2) and the gain of the feedback signal (“Internal Feedback” element in Fig. 2). In addition, the “Lag” element and the “Local Delay” element (fixed at 3 ms) have a modest influence on pulse shape. The “Nonlinear Gain” is a soft saturation element associated with neuronal processes. It was found to have negligible influence on pulse shape, at least for the range of pulse amplitudes simulated, but was retained to be compatible with the original Zee model.

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2) Integrator: Most previous models have postulated an integrator as the primary control process in a visual feedback control system [2]–[4], [14]. Although anatomical evidence is sketchy, behavioral evidence suggests the existence of a separate integrator for the vergence system [14], [30]. Certainly the late component has an integrator to achieve its high level of positional accuracy, and behavioral and neurophysiological evidence suggest that the initial component controller also uses an integrator. Mays et al. [27] found burst-tonic cells intermixed with convergence burst cells in the mesencephalic reticular formation. The tonic firing rate recorded in these cells was proportional to vergence angle and could be the result of integration applied to the velocity signals of the burst cells [27], [29]. A simple first-order process, the “Step Integrator” in Fig. 2, is placed after the pulse controller to represent the leaky integrator. As in the model of Zee et al. [1], the integrator is driven by a pulse signal, a concept adapted from the saccadic system. In addition to providing the input to the leaky integrator, the pulse signal directly drives the vergence plant. The amplitude of the pulse and step signal components are adjusted through the “Pulse Gain” and “Step Gain” elements, respectively. The summation of pulse and step signals drives the muscle “Plant” as described below. 3) Oculomotor Plant: The oculomotor plant represents the mechanical properties of the muscles, eye, and orbit. One of the earliest and most straightforward representations of this plant was introduced by Robinson [31]. Based on mechanical studies, he found that the oculomotor plant could be approximately represented as a second-order, overdamped system with time constants of 224 and 13 ms [31], [32]. This secondorder oculomotor plant has been implemented in a number of computer simulation models of saccadic eye movements [11], [33]–[35]. More complicated plant models of higher order and containing nonlinearities have been proposed [1], [36]–[39]; however, Van Opstal et al. [40] determined that a model order higher than two was not necessary to obtain realistic saccadic velocity profiles. Analogous to saccadic models, second-, fourth-, and fifthorder plant representations have been used in models of disparity vergence [1], [6], [14], [30], [31]. Except for the model of Zee et al. [1], the plant models described above were derived based on neurological evidence from the saccadic system. Recently, Gamlin and Mays [29] measured the firing rate of cells located at the putative medial rectus motorneuron of rhesus monkeys in response to symmetrical disparity vergence stimuli. These signals are thought to be the direct vergence input into the oculomotor plant. Combining measurements of these input signals with simultaneous binocular eye-movement recordings, they were able to identify the effective dynamics of the oculomotor plant. Their results showed that both static and dynamic vergence eye movements, as well as velocity, could be accounted for by a first-order oculomotor plant with time constant of 265 114 ms ( ).5 5 Note that the time constant of a typical vergence movement is usually less than the plant time constant. As with the saccade, this is achieved by the pulse component of the combined pulse/step drive signal.

Based on this neurophysiological evidence, we used a simple first-order model to represent the vergence oculomotor plant. To limit the number of model variables (and the resultant degrees of freedom), the plant time constant was fixed at 265 ms. This was the average value found by Gamlin and Mays [29] in monkey, and is also close to the dominant time constant found by Robinson [31] in man. B. Model Simulations The model shown in Fig. 2 contains six parameters which are adjusted to simulate disparity vergence movements. Five parameters are associated to the common central controller: 1) response latency; 2) pulse gain; 3) step gain; 4) internal feedback gain of the pulse generator, which controls pulse duration; 5) rate limiter, which adjusts the rise time of the pulse. In addition, a differential gain element proportions the common control signal to the two eyes. As mentioned above, the two oculomotor plants are represented by a simple firstorder process with a fixed time constant. Simulations were implemented using the MATLAB Simulink software package (Math Works, Inc., Natick, MA). With each simulation trial, both the eye movement response and the model simulation were displayed on the computer screen, and the root-mean-square (rms) error between the two was calculated to aid in the parameter adjustment. Parameters were first adjusted by an optimization program (MATLAB Optimization Toolbox), then fine-tuned by an operator to obtain the best fit between model simulations and experimental data. While it is difficult to guarantee the uniqueness of any solution to the inverse problem, model parameters did relate to specific behavioral features, which provided some constraints on the solution space. For example, the response latency parameter was set so that the onset of the simulated and actual response were equal. Pulse gain was found to influence the peak velocity of the simulated response, while the pulse internal feedback gain modified the duration of the velocity curve; hence, these parameters were adjusted to provide the closest possible match between model and experimental peak velocities and durations. Similarly, the rate limiter influenced primarily the rising slopes of the velocity curves, while the step gain influenced the late, descending portion of the velocity profiles and the analogous portions of the velocity curves were used to adjust these parameters. In sum, each parameter was linked to a specific feature of the dynamic response, although some parameters had secondary interactions that required simultaneous adjustment by the operator. IV. RESULTS A. Convergence Responses from all subjects to convergent and divergent disappearing stimuli (2 , 4 , and 8 with presentation times of 50 and 100 ms) were simulated. Model parameters were adjusted as described previously to obtain a close fit between simulated and experimental data. Fig. 3(a) shows a typical left and right movement response to a 4 disappearing step stimulus and the model’s simulation of that response. Model simulations closely matched the experimental data over the first 0.7 s, where the


(a)

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(b)

Fig. 3. Initial component vergence response (solid line) and associated model simulation (dashed line) evoked by a disappearing step stimulus of 4 amplitude and 100-ms duration. The upper traces show the left eye position and correlated velocity, while the lower traces are the same for the right eye. Both traces plot convergence as upward to aid in comparison of movement dynamics: (a) convergent response and (b) divergent response. Subject GH.

initial component response, the component of interest here, is dominant. (Note particularly the close match in velocity traces.) Since the stimulus disappeared before the movement actually began, these responses took place in total darkness, and some drift during the latter portion of the response can be expected. In Fig. 4(a), records from another subject containing substantial saccades (not a “typical” response) were simulated to illustrate that the model still accurately represents the vergence portion of these records. B. Divergence Experimental responses to both disappearing and normal step stimuli show substantial direction dependent differences in dynamics; that is, divergence movements achieve lower maximum velocities than convergence movements of comparable amplitude. Typical divergence responses and the associated simulations are shown for two subjects in Figs. 3(b) and 4(b). As with convergent responses, there is a very close match between experimental and simulated data, despite differences in dynamics. Analysis of a large number of simulations of both convergent and divergent responses showed that the dynamic differences could be represented primarily by differences in the ratio of pulse to step gain. As seen in Table I, the ratio of pulse

to step gains was quite small for simulations of divergence movements. In fact, the pulse component may be completely absent from the divergence control signal as discussed in the Section V. V. DISCUSSION A. Neurophysiological Correlates Since the model is structured around known and accepted oculomotor neurophysiology, its internal variables should reflect the internal neural signals of the vergence control system. One internal signal has been quantified in a study by Mays et al. [27], and it is possible to compare these results with the corresponding model signal. Mays et al. [27] found a linear relationship between firing rate of convergence burst cells in monkey midbrain and the instantaneous vergence velocity. Averaging this relationship over 50 cells, he found this relationship could be summarized by the linear equation (1) where is the neural firing response in spikes/s and the vergence velocity in /s.

is

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(a)

(b)

Fig. 4. Initial component response and associated monocular model simulations plotted as in Fig. 3. The response is to a 4 stimulus with 50-ms duration. (a) and (b) Subject JS. A response containing substantial saccades was specifically chosen to illustrate that the model still accurately simulates the initial component of the vergence portion of the response.

PULSE

AND

TABLE I STEP COMPONENTS OF CONVERGENCE CONTROL SIGNALS

An analogous relationship for the model can be calculated from the pulse control signal (which represents the activity of the burst cells). To calculate the effective model pulse control , the model’s pulse signal was integrated on either signal, side of the peak value ( 10 ms on either side of the pulse peak) to provide an averaged value. This procedure is similar to the technique used to derive the neurophysiological data. These values were then scaled such that the maximum model are the same. The relationship and neurological values of ) obtained from model simulations of a large number ( of actual movements from all four subjects is shown in Fig. 5

and is defined by a linear relationship similar to (1) (2) In Fig. 5, the solid circles represent the peak pulse amplitude values calculated from the model simulations of actual data, the solid line indicates the regression line of these model data, and the dashed line shows the neurological relationship given in (1). The close correspondence between the internal control signal predicted by the model and that found in neurophysiological experiments on monkeys indicates the model is a plausible representation of the underlying neurophysiology.


Fig. 5. The relationship between peak pulse amplitudes and peak response velocity obtained from model simulations (solid squares and solid regression line) and neurophysiological data (dashed regression line) (Mays et al. [27]). Model simulation data includes responses from all subjects and for both 272). The neurophysiological regression line summarizes the eyes (n relationship between convergence burst cell firing rate and vergence velocity, averaged over 50 cells. These data were obtained from primates.

=

B. Direction Dependent Dynamic Asymmetries Model simulations indicate that convergence is driven by the combination of a pulse and step control signal. This is similar in concept to the control signal that drives saccadic eye movements, except that the ratio of pulse to step is considerably less for convergence. Based on quantitative model from Hsu et al. [42], the pulse/step ratio for saccades varies with amplitude, but can be estimated to be approximately 9.5 for a 5 movement. This compares to a value of approximately 0.3 for a 4 convergence movement. In the simulations described above, the pulse/step ratio was determined by the dynamics of a given response. However, the specific value of the pulse/step also depends on the specific value chosen for the vergence plant time constants. In the simulations described above, the plant time constants were the same for both eyes and were set based on neurophysiological evidence. Specifically, a fixed value of 265 ms was chosen based on the average found by Gamlin and Mays [29] from monkey data. This value agrees well with the dominant time constant of 224 ms found by Robinson [31] during external force experiments on humans. For the plant time constant selected, a small pulse was required in addition to the step to match the divergence data. However, it is possible to eliminate the requirement for this pulse if the plant time constants are slightly modified. The simulations that made up the data of Table I were rerun based on the assumption that divergence is a true step response. It was found that equally accurate simulations (based on the rms error between simulated and experimental data) could be obtained provided the plant time constants were set to between 150 and 200 ms. These values are still plausible based on current neurophysiological evidence; hence, we speculate that the pulse signal could be, in fact, absent from the divergence control signal. Under this assumption, the divergence response represents the true step response of the eye movement plant.

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The increase in plant time constants required that the pulsestep ratio for convergence movements be reduced by about 20% from the values in Table I in order to accurately represent these movements with the new plant time constants. The model-based analysis used here ascribes the directiondependent dynamic asymmetry to a difference in control signal properties and suggests somewhat separate control process. The separation between convergence and divergence control processes has been supported by neurophysiological findings. Studying primates, Mays et al. [27] reported burst cell activity in the mesencephalic reticular formation dorsal and lateral to the oculomotor nucleus during divergence, but not convergence. Moreover, these divergent burst cells (i.e., pulse-producing cells) required more activity to achieve the same movement velocity as a convergent burst cell. This finding implies that divergence burst cells are less effective in producing activity at the motor neuron level. A reduced level of pulse-like activity measured at the medial rectus nucleus during divergent responses [29] suggests that some process between the divergent burst cells and the medial rectus nuclei interferes with the pulse signal. Model simulations suggest that the divergence pulse signal is completely integrated so that only the step component reaches the muscle. This would account for the apparent decreased effectiveness of divergence burst cells and slower divergence dynamics.

VI. CONCLUSION A model of the control processes which mediate the initial component of disparity vergence eye movements was constructed. This model is a binocular extension of that originally proposed by Zee et al. [1]. Based on the dual-mode theory, which states that the initial component is preprogrammed, the model featured an open-loop controller composed of a pulse generator and step-producing integrator along with a simple first-order neuromuscular plant. Model simulations of the initial component closely matched the dynamics of disparity vergence eye movements supporting both the dualmode theory and the specific model configuration. In addition, the value of internal model parameters were shown to be consistent with known neurophysiological data. A modelbased analysis of direction-dependent dynamics demonstrated that these difference could be attributed to a difference in the amount of pulse component present in the control signal.

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Jia-Long Horng was born in Taipei, Taiwan, R.O.C., in 1960. He received the B.S. degree in biomedical engineering from Chung Yuan University, Chung Li, Taiwan, in 1987 and the M.S. degree in 1990 in bioengineering from Texas A&M University, College Station. In 1994 he received the Ph.D. degree from the Department of Biomedical Engineering at Rutgers University, New Brunswick, NJ. His dissertation involved experimental and mathmatical modeling of the vergence oculomotor control system. From 1993 to 1995, he was a Postdoctoral Fellow in the Division of Pulmonary and Critical Care Medicine at Robert Wood Johnson Medical School, New Brunswick, NJ. He is currently a Research Associate at the Department of Neurology, Olive View–UCLA Medical Center, Sylmar, CA, where he continues to study oculomotor behavior in patients with brain disease. His major research interests are visual and oculomotor physiology and pathology, mathmatical modeling in eye-movement control systems, the application of biomedical signal processing, and bioinstrumentation. Dr Horng is a member of the Association for Research in Vision and Ophthalmology and the Society for Neuroscience.

John Semmlow (M’79–SM’89–F’94) was born in Chicago, IL, in 1942, and received the B.S.E.E. degree from the University of Illinois, Champaign, in 1964. Following several years as a Design Engineer for Motorola, Inc., Chicago, he entered the Bioengineering Program at the University of Illinois, Medical Center in Chicago, receiving the Ph.D. degree in 1970. He has held faculty positions at the University of Illinois, Chicago, and currently holds a joint position as Professor of Surgery, UMDNJ Robert Wood Johnson Medical School, New Brunswick, NJ, and Professor of Biomedical Engineering at Rutgers University, Piscataway, NJ. In 1985, he was a NSF/CNRS Fellow in the Sensorimotor Control Laboratory of the University of Provence, Marseille, France. His active research areas include physiological motor control and medical instrumentation with an emphasis on non invasive instrumentation. Dr. Semmlow was appointed a Fellow in IEEE in 1994 in recognition of his work in acoustic detection of coronary artery disease.


George K. Hung (M’82–SM’90) was born in Shanghai, China, in 1947. He received the B.S. degree in mechanical engineering, the M.S. degree in bioengineering, and the Ph.D. degree in physiological optics from the University of California, Berkeley, in 1970, 1971, and 1977, respectively. He joined the faculty at Rutgers University, Piscataway, NJ in 1978, where he is currently Professor of Biomedical Engineering. He has published extensively in the areas of experimentation and modeling of the human accommodation and vergence systems. His current research interest include dynamic interactions between saccade and vergence under the free- and instrument-space condition, clinical applications of the accommodation and vergence model, psychophysics of early visual processing in the young adult and the elderly using briefly-presented stationary and moving target, and application of control of human eye movements to machine vision. Dr. Hung is a member of Sigma Xi and the Association for Research in Vision and Ophthalmology.

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Kenneth J. Ciuffreda received the B.A. degree in biology from Seton Hall University, South Orange, NJ, in 1969, the O.D. degree from the Massachusetts College of Optometry in 1973, and the Ph.D. degree in physiological optics from the University of California, Berkeley, in the School of Optometry, in 1977. He has been a Faculty Member at SUNY/State College of Optometry, New York, since 1979, where he is currently the Chairman of the Department of Vision Sciences and Distinguished Teaching Professor. He is also an Associate Member of the Graduate Faculty of Rutgers University, Piscataway, NJ, in Biomedical Engineering. His research interests include eye movements, accommodation, myopia, and bioengineering applications to clinical optometry. He has more than 175 publications, including five books.