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PubMed2004

Quantification of dark adaptation dynamics in retinitis pigmentosa using non-linear regression analysis.

PURPOSE: Non-linear regression analysis was used to determine dark adaptation indices in people with retinitis pigmentosa and in control subjects. METHODS: Dark adaptation data were collected for 13 people with retinitis pigmentosa and 21 controls using the Goldmann-Weekers Dark Adaptometer. Data were analysed using an exponential non-linear regression model and dark adaptation indices derived. The results were compared to age-related values. RESULTS: The mean cone threshold of the group with RP (4.73 +/- 0.19 log units) was significantly greater than that found in the control group (3.69 +/- 0.12 log units). The rate of cone dark adaptation in the RP group was not significantly different from that of the control group. The a break in the RP group (6.46 +/- 0.70 minutes) was delayed when compared to the control group (4.29 +/- 0.21 minutes) and the rate of rod dark adaptation in the RP group was slower (10 +/- 2 per cent per minute) than that of the control group (15 +/- 1 per cent per minute). CONCLUSIONS: This study has shown that a relatively simple data analysis can provide a more quantitative and intuitive description of dark adaptation rates in people with retinal disease. This technique will enable more effective use of dark adaptometry as a supplement to objective electrophysiology, when monitoring people with retinitis pigmentosa.

Adult↗
PubMed2000

Stabilizing nonlinear dynamical systems by an adaptive adjustment mechanism

An adaptive adjustment mechanism is applied to stabilize multidimensional dynamical systems. Without utilizing any prior knowledge of the system itself, nor extra external control signals, the mechanism can ensure a large class of chaotic systems to converge to their "generic" stable periodic orbits.

Journal Article↗
PubMed1997

Deficits of gaze stability in multiple axes following unilateral vestibular lesions.

Abnormalities in the vestibulo-ocular reflex (VOR) after unilateral vestibular injury may cause symptomatic gaze instability. We compared five subjects who had unilateral vestibular lesions with normal control subjects. Gaze stability and VOR gain were measured in three axes using scleral magnetic search coils, in light and darkness, testing different planes of rotation (yaw and pitch), types of stimulus (sinusoids from 0.8 to 2.4 Hz, and transient accelerations) and methods of rotation (active and passive). Eye velocity during horizontal tests reached saturation during high-velocity/acceleration ipsilesional rotation. Rapid vertical head movements triggered anomalous torsional rotation of the eyes. Gaze instability was present even during active rotation in the light, resulting in oscillopsia. These abnormal VOR responses are a consequence of saturating nonlinearities, which limit the usefulness of frequency-domain analysis of rotational test data in describing these lesions.

Adaptation, Physiological↗
PubMed1998

Neuromuscular control model of the arm including feedback and feedforward components.

A neuromusculoskeletal model of the human arm was developed which contains both feedforward and feedback control, and thereby accounts for motor control of fast movements as well as interaction with external forces. The feedforward control component forms an approximate representation of the inverse dynamics of the arm and its interaction with the environment. The feedback control component compensates for errors in the representation of the inverse dynamics and for unexpected forces acting on the arm. Moreover, the control system provides a solution for the redundancy of the muscles. The system performance is adapted in a learning procedure according to a specified goal function. It is shown in the paper that good control of the nonlinear musculoskeletal model and neural control signals which are similar to electromyographic (EMG) data, are attained. The response of the arm to external forces is analysed and compared with experimental data on arm impedance.

Arm↗
PubMed2005

Nonlinear PI controllers for continuous bioreactors using population balance models.

Continuous bioreactors are critical unit operations in many biological systems, but the unique modeling is very complicated due to the underlying biochemical reactions and the distributed properties of cell population. The scope of this paper considers a popular modeling method for microbial cell cultures by population balance equation models, and the control objective aims to attenuate undesired oscillations appeared in the nonlinear distributed parameter system. In view of pursuing the popular/practical control configuration and the lack of on-line sensors, an approximate technique by exploiting the "pseudo-steady-state" approach constructs a simple nonlinear control model. Through an off-line estimation mechanism for the system having self-oscillating behavior, two kinds of nonlinear PI configurations are developed. Closed-loop simulation results have confirmed that the regulatory and tracking performances of the control system proposed are good.

Adaptation, Physiological↗
PubMed2000

Adaptive neural network control of cyclic movements using functional neuromuscular stimulation.

In this study, we evaluated the performance of an adaptive feedforward controller and its ability to automatically develop and customize stimulation patterns for use in functional neuromuscular stimulation (FNS) systems. Results from previous experiments using the pattern generator/pattern shaper (PG/PS) controller to generate isometric contractions demonstrated its ability to adjust stimulation patterns to account for recruitment nonlinearities and muscle dynamics. In this study, the PG/PS controller was tested under isotonic conditions. This evaluation required the PG/PS controller to account for muscle length-tension and force-velocity properties as well as limb dynamics. The performance of the adaptive controller was also compared with that of a proportional-derivative (PD) feedback controller. The PG/PS controller is composed of a neural network system that adaptively filters a periodic signal to produce a muscle stimulation pattern for generating cyclic movements. We used computer-simulated models to determine controller parameters for the PG/PS and PD controller that perform well across a variety of musculoskeletal systems. The controllers were then experimentally evaluated on both legs of two subjects with spinal cord injury. Results indicated that the PG/PS controller was able to achieve and maintain better tracking performance than the PD controller. This study indicates that the PG/PS control system may provide an effective mechanism for automatically customizing stimulation patterns for individuals using FNS systems.

Algorithms↗
PubMed1994

Stochastic versus deterministic variability in simple neuronal circuits: II. Hippocampal slice.

Long time series of Schaffer collateral to CA1 pyramidal cell presynaptic volleys (stratum radiatum) and population spikes (stratum pyramidale) were evoked (driven) in rat hippocampal slices. From the driven CA1 region in normal [K+] perfusate, both population spike amplitude and an input-output function consisting of population spike amplitude divided by the presynaptic volley amplitude were analyzed. Raising [K+] in the perfusion medium to 8.5 mM, slices were induced to spontaneously burst fire in CA3 and long time series of inter-burst intervals were recorded. Three tests for determinism were applied to these series: a discrete adaptation of a local flow approach, a local dispersion approach, and nonlinear prediction. Surrogate data were generated to serve as mathematical and statistical controls. All of the population spike (6/6) and input-output (6/6) time series from the normal [K+] driven circuitry were stochastic by all three methods. Although most of the time series (5/6) from the autonomously bursting high [K+] state failed to demonstrate evidence of determinism, one (1/6) of these time series did demonstrate significant determinism. This single instance of predictability could not be accounted for by the linear correlation in these data.

Algorithms↗
PubMed1995

Age-related changes in the "complexity" of cardiovascular dynamics: A potential marker of vulnerability to disease.

Healthy physiologic control of cardiovascular function is a result of complex interactions between multiple regulatory processes that operate over different time scales. These include the sympathetic and parasympathetic nervous systems which regulate beat-to-beat heart rate (HR) and blood pressure (BP), as well as extravascular volume, body temperature, and sleep which influence HR and BP over the longer term. Interactions between these control systems generate highly variable fluctuations in continuous HR and BP signals. Techniques derived from nonlinear dynamics and chaos theory are now being adapted to quantify the dynamic behavior of physiologic time series and study their changes with age or disease. We have shown significant age-related changes in the 1/f(x) relationship between the log amplitude and log frequency of the heart rate power spectrum, as well as declines in approximate dimension and approximate entropy of both heart rate and blood pressure time series. These changes in the "complexity" of cardiovascular dynamics reflect the breakdown and decoupling of integrated physiologic regulatory systems with aging, and may signal an impairment in cardiovascular ability to adapt to external and internal perturbations. Studies are currently underway to determine whether the complexity of HR or BP time series can distinguish patients with fainting spells due to benign vasovagal reactions from those due to life-threatening cardiac arrhythmias. Thus, measures of the complexity of physiologic variability may provide novel methods to monitor cardiovascular aging and test the efficacy of specific interventions to improve adaptive capacity in old age. (c) 1995 American Institute of Physics.

Journal Article↗
PubMed2006

FITSK: online local learning with generic fuzzy input Takagi-Sugeno-Kang fuzzy framework for nonlinear system estimation.

Existing Takagi-Sugeno-Kang (TSK) fuzzy models proposed in the literature attempt to optimize the global learning accuracy as well as to maintain the interpretability of the local models. Most of the proposed methods suffer from the use of offline learning algorithms to globally optimize this multi-criteria problem. Despite the ability to reach an optimal solution in terms of accuracy and interpretability, these offline methods are not suitably applicable to learning in adaptive or incremental systems. Furthermore, most of the learning methods in TSK-model are susceptible to the limitation of the curse-of-dimensionality. This paper attempts to study the criteria in the design of TSK-models. They are: 1) the interpretability of the local model; 2) the global accuracy; and 3) the system dimensionality issues. A generic framework is proposed to handle the different scenarios in this design problem. The framework is termed the generic fuzzy input Takagi-Sugeno-Kang fuzzy framework (FITSK). The FITSK framework is extensible to both the zero-order and the first-order FITSK models. A zero-order FITSK model is suitable for the learning of adaptive system, and the bias-variance of the system can be easily controlled through the degree of localization. On the other hand, a first-order FITSK model is able to achieve higher learning accuracy for nonlinear system estimation. A localized version of recursive least-squares algorithm is proposed for the parameter tuning of the first-order FITSK model. The local recursive least-squares is able to achieve a balance between interpretability and learning accuracy of a system, and possesses greater immunity to the curse-of-dimensionality. The learning algorithms for the FITSK models are online, and are readily applicable to adaptive system with fast convergence speed. Finally, a proposed guideline is discussed to handle the model selection of different FITSK models to tackle the multi-criteria design problem of applying the TSK-model. Extensive simulations were conducted using the proposed FITSK models and their learning algorithms; their performances are encouraging when benchmarked against other popular fuzzy systems.

Algorithms↗
PubMed2006

Complex spike-event pattern of transient ON-OFF retinal ganglion cells.

ON-OFF transient ganglion cells of the turtle retina show distinct spike-event patterns in response to abrupt intensity changes, such as during saccadic eye movements. These patterns consist of two main spike events, with the latency of each event showing a systematic dependency on stimulus contrast. Whereas the latency of the first event decreases monotonically with increasing contrast, as expected, the second event shows the shortest latency for intermediate contrasts and a longer latency for high and low contrasts. These spike-event patterns improve the discrimination of different light-intensity transitions based on ensemble responses of the ON-OFF transient ganglion cell subpopulation. Although the discrimination results are far better than chance using either spike counts or latencies of the first spikes, they are further improved by using properties of the second spike event. The best classification results are obtained when spike rates and latencies of both events are considered in combination. Thus spike counts and temporal structure of retinal ganglion cells carry complementary information about the stimulus condition, and thus spike-event patterns could be an important aspect of retinal coding. To investigate the origin of the spike-event patterns in retinal ganglion cells, two computational models of retinal processing are compared. A linear-nonlinear model consisting of separate filters for on and off response components fails to reproduce the spike-event patterns. A more complex cascade filter model, however, accurately predicts the timing of the spike events by using a combination of gain control loop and spike rate adaptation.

Algorithms↗
PubMed2005

Modeling a multivariable reactor and on-line model predictive control.

A nonlinear first principle model is developed for a laboratory-scaled multivariable chemical reactor rig in this paper and the on-line model predictive control (MPC) is implemented to the rig. The reactor has three variables-temperature, pH, and dissolved oxygen with nonlinear dynamics-and is therefore used as a pilot system for the biochemical industry. A nonlinear discrete-time model is derived for each of the three output variables and their model parameters are estimated from the real data using an adaptive optimization method. The developed model is used in a nonlinear MPC scheme. An accurate multistep-ahead prediction is obtained for MPC, where the extended Kalman filter is used to estimate system unknown states. The on-line control is implemented and a satisfactory tracking performance is achieved. The MPC is compared with three decentralized PID controllers and the advantage of the nonlinear MPC over the PID is clearly shown.

Algorithms↗
PubMed2005

A cellular and molecular model of response kinetics and adaptation in primate cones and horizontal cells.

A model for the sensitivity regulation in the primate outer retina is developed and validated using horizontal cell measurements from the literature. The main conclusion is that the phototransduction of the cones is the key factor regulating sensitivity. The model consists of a nonlinearity cascaded with three feedback control loops. The nonlinearity is caused by the hydrolysis of cGMP by activated phosphodiesterase. The first feedback loop is divisive, with calcium regulating the photocurrent in the cone outer segment. The second feedback loop is also divisive, with voltage-sensitive channels regulating the membrane voltage of the cone inner segment. The final feedback loop is subtractive, where the membrane voltage of the horizontal cell is subtracted from that of the cone before the cone drives the horizontal and bipolar cells. The model describes adequately the major characteristics of the horizontal cell responses to wide field, spectrally white stimuli. In particular, it shows (1) sensitivity and bandwidth control as a function of background intensity; (2) the major nonlinearities observed in the horizontal cells; and (3) the transition from linear responses toward contrast constancy (Weber's law) for background illuminances ranging from 1-1000 td.

Adaptation, Ocular↗
PubMed1981

Optimal control evaluation of left ventricular systolic dynamics.

A model of the contracting left ventricle was developed, in which the left ventricle was represented as a time-varying compliance. The vascular load included the nonlinear (Bernoulli) resistance of the aortic valve, blood inertance, and a Windkessel model of the arterial tree. Owing to the obligatory aerobic nature of the heart, oxygen consumption can be used to characterize the energy utilized by the myocardium. An adaptive control law was developed for determining the systolic time course of ventricular pressure and volume that minimizes cardiac oxygen consumption. Three main determinants of myocardial oxygen consumption were included in the integral criterion function: developed wall tension, inotropic state, and external (mechanical) work. The optimal control problem was solved using the Pontryagin maximum principle. The model could predict, in good agreement with experimentally obtained data, systolic time course of ventricular pressure and volume, as well as directional changes in the duration of isovolumic contraction and ejection phase under various conditions of end-diastolic volume, mean aortic pressure, and inotropic state.

Animals↗
PubMed1994

A nonlinear model of step responses in the cockroach tactile spine neuron.

Rapid sensory adaptation in the cockroach tactile spine neuron has previously been associated with a labile threshold for action potentials, which changes with the membrane potential by a process involving two time constants. A feed-forward, variable-threshold model has previously been used to account for the frequency response function of the neuron when stimulated with small-signal, white-noise currents. Here, we used a range of accurately controlled steps of extracellular current to stimulate the neuron. The same model was able to predict the individual step responses and could also fit the entire set of step responses from a single neuron if an initial, saturating, static nonlinearity was included. These results indicate that the two-time-constant, variable-threshold model can account for most of the rapidly adapting behavior of the tactile spine neuron.

Animals↗
PubMed2005

Acute adaptations of the coronary circulation to exercise.

Coronary blood flow is tightly coupled to myocardial oxygen consumption to maintain a consistently high level of myocardial oxygen extraction. This tight coupling has been proposed to depend on periarteriolar oxygen tension, signals released from cardiomyocytes (adenosine acting on K+(ATP) channels), and/or the endothelium (prostanoids, nitric oxide, endothelin [ET]) and autonomic influences (catecholamines), but the contribution of each of these regulatory pathways and their interactions are still incompletely understood. Until recently, experimental studies into the regulation of coronary blood flow during exercise were principally performed in the dog. We have performed several studies on the regulation of vasomotor tone in coronary resistance vessels in chronically instrumented exercising swine. These studies have shown that the coronary resistance vessels in swine lack significant alpha-adrenergic control, but that these vessels are subject to beta-adrenergic feed-forward control during exercise, which is aided by a parasympathetic withdrawal. In addition, withdrawal of an ET-mediated vasoconstrictor influence also contributes to exercise-induced coronary vasodilation. Coronary blood flow regulation by endothelial and metabolic vasodilator pathways contributes to resting vasomotor tone regulation but does not appear to contribute to the exercise-induced coronary vasodilation. Furthermore, blockade of one vasodilator pathway is not compensated by an increased contribution of the other vasodilator mechanisms, suggesting that porcine coronary vasomotor control by endothelial and metabolic factors occurs in a linear additive rather than a nonlinear synergistic fashion.

Adaptation, Physiological↗
PubMed2004

Bifurcating neuron: computation and learning.

The ability of bifurcating processing units and their networks to rapidly switch between different dynamic modes has been used in recent research efforts to model new computational properties of neural systems. In this spirit, we devise a bifurcating neuron based on control of chaos collapsing to a period-3 orbit in the dynamics of a quadratic logistic map (QLM). Proposed QLM3 neuron is constructed with the third iterate of QLM and uses an external input, which governs its dynamics. The input shifts the neuron's dynamics from chaos to one of the stable fixed points. This way the inputs from certain ranges (clusters) are mapped to stable fixed points, while the rest of the inputs is mapped to chaotic or periodic output dynamics. It has been shown that QLM3 neuron is able to learn a specific mapping by adaptively adjusting its bifurcation parameter, the idea of which is based on the principles of parametric control of logistic maps [Proceedings of the International Symposium on Nonlinear Theory and its Applications (NOLTA'97), Honolulu, HI, 1997; Proceedings of SPIE, 2000]. Learning algorithm for the bifurcation parameter is proposed, which employs the error gradient descent method.

Learning↗
PubMed1998

An analysis of learning control by backpropagation through time.

The main objective of this paper is to indicate how the learning process of a neural control system by backpropagation through time (BTT) is influenced by the structure of the control system, the dynamics of the plant and the parameters of the learning process. Since the analysis of a linear system is far more tractable than the analysis of a nonlinear system, learning control of linear systems is pursued as a step towards the understanding of learning control of nonlinear systems. Very good control performance can be attained for linear control systems by BTT learning. Learning of a feedforward controller is enhanced by inclusion of a feedback controller. The attained control performance is not affected by the sensitivity model of the plant. Application of BTT to the control of a nonlinear musculoskeletal model of the human arm shows that both feedback and feedforward control are necessary for a good performance and that both control modes are effectively adapted in the learning process. A satisfactory control performance can still be achieved with drastically simplified sensitivity models during this learning process.

Journal Article↗
PubMedDate not supplied

A temporal model for early vision that explains detection thresholds for light pulses on flickering backgrounds.

A model is presented for the early (retinal) stages of temporal processing of light inputs in the visual system. The model consists of a sequence of three adaptation processes, with two instantaneous nonlinearities in between. The three adaptation processes are, in order of processing of the light input: a divisive light adaptation, a subtractive light adaptation, and a contrast gain control. Divisive light adaptation is modeled by two gain controls. The first of these is a fast feedback loop with square-root behavior, the second a slow feedback loop with logarithm-like behavior. This can explain several aspects of the temporal behavior of photoreceptor outputs. Subtractive light adaptation is modeled by a high-pass filter equivalent to a fractional differentiation, and it can explain the attenuation of low frequencies observed in ganglion cell responses. Contrast gain control in the model is fast (Victor, 1987), and can explain the decreased detectability of test signals that are superimposed on dynamic backgrounds. We determine psychophysical detection thresholds for brief test pulses that are presented on flickering backgrounds, for a wide range of temporal modulation frequencies of these backgrounds. The model can explain the psychophysical data for the full range of modulation frequencies tested, as well as detection thresholds obtained for test pulses on backgrounds with increment and decrement steps in intensity.

Adaptation, Ocular↗