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At least 163 records · Page 9Linked to original sources
PubMed2002

Color from invisible patterns.

Human pattern resolution is limited by optical blurring as well as neural filtering by a cascade of retinal and cortical sites with progressively lower resolution limits. Curiously, pattern structure can influence perceived color: a high-contrast, monochromatic (single wavelength) pattern appears desaturated (closer to white) relative to a uniform field of the same wavelength. Here we show that this desaturation is evident even when the pattern's frequency is too high for conscious perception, implicating a nonlinear process--namely light adaptation--at the level of single cone photoreceptors. We propose a neural mechanism in which fast, involuntary eye movements serve to shift control over perception between two competing cone populations, each operating at different levels of adaptation.

Color Perception↗
PubMed1991

Recursive parameter identification of constrained systems: an application to electrically stimulated muscle.

In the application of real-time identification methods for diagnosis or adaptive control of biomedical systems, there is often known model information that is ignored. Constraints on the allowable values of parameters, which may be based on physical considerations, are often neglected because the information does "fit" easily into commonly used parameter-identification algorithms. In this paper a method of incorporating constraints on model parameters is developed. This method is applicable to most recursive parameter-identification algorithms. It enforces linear equality constraints on identified parameters. The use of this method for the real-time identification of autoregressive moving-average-type time series models, subject to parameter constraints, is described in detail. These constraints may be time varying. At each time step, the parameter estimate obtained by a recursive least squares estimator is orthogonally projected onto the constraint surface. This simple idea, when appropriately executed, enhances the output prediction accuracy of estimated parameters. Using constraint information in this way is important when we do not wish to destroy a "natural" parameterization of the model (by an initial projection to incorporate equality constraints), or when we cannot use a single initial model simplification (because the constraints are time varying or involve inputs and outputs). Because it improves output prediction at future times, this method is advantageous for use in predictive adaptive controllers. The use of this algorithm is demonstrated in the identification of electrically stimulated quadriceps muscles in paraplegic human subjects, using percutaneous intramuscular electrodes. The nonlinear steady-state force versus pulsewidth recruitment characteristic of the electrode-muscle system is identified simultaneously with the input-output muscle response dynamics, using a Hammerstein-type model. Knowledge of the recruitment curve's shape is translated into constraints on the identified parameters. This information improves the experimental predictive quality of the identified model.

Adult↗
PubMed2003

Improving elbow torque output of stroke patients with assistive torque controlled by EMG signals.

This paper develops an assistive torque system which uses homogeneic surface electromyogram (EMG) signals to improve the elbow torque capability of stroke patients by applying an external time-varying assistive torque. In determining the magnitude of the torque to apply, the incorporated assistive torque algorithm considers the difference between the weighted biceps and triceps EMG signals such that the applied torque is proportional to the effort supplied voluntarily by the user. The overall stability of the assistive system is enhanced by the incorporation of a nonlinear damping element within the control algorithm which mimics the physiological damping of the elbow joint and the co-contraction between the biceps and triceps. Adaptive filtering of the control signal is employed to achieve a balance between the bandwidth and the system adaptability so as to ensure a smooth assistive torque output. The innovative control algorithm enables the provision of an assistive system whose operation is both natural to use and simple to learn. The effectiveness of the proposed assistive system in assisting elbow movement performance is investigated in a series of tests involving five stroke patients and five able-bodied individuals. The results confirm the ability of the system to assist all of the subjects in performing a number of reaching and tracking tasks with reduced effort and with no sacrifice in elbow movement performance.

Aged↗
PubMed2004

Adaptive changes in bacteria: a consequence of nonlinear transitions in chromosome topology?

Adaptive changes in bacteria are generally considered to result from random mutations selected by the environment. This interpretation is challenged by the non-randomness of genomic changes observed following ageing or starvation in bacterial colonies. A theory of adaptive targeting of sequences for enzymes involved in DNA transactions is proposed here. It is assumed that the sudden leakage of cAMP consecutive to starvation induces a rapid drop in the ATP/ADP ratio that inactivates the homeostasis in control of the level of DNA supercoiling. This phase change enables the emergence of local modifications in chromosome topology in relation to the missing metabolites, a first stage in expression of an adaptive status in which DNA transactions are induced. The nonlinear perspective proposed here is homologous to that already suggested for adaptation of pluricellular organisms during their development. In both cases, phases of robustness in regulation networks for genetic expression are interspaced by critical periods of breakdown of the homeostatic regulations during which, through isolation of nodes from a whole network, specific changes with adaptive value may locally occur.

Adaptation, Physiological↗
PubMed1990

A comparison of three different filters for speckle reduction of Doppler spectra.

This paper is concerned with speckle suppression in Discrete Fourier Transform based Doppler signals by means of digital image processing. The Doppler spectrum is treated as a greyscale image, and three different noise smoothing algorithms are applied to it. These are the Double Window Modified Trimmed Mean filter, which is nonlinear, and Lee's and the directional filter, which are adaptive in the sense that the smoothing performed by them at each point of the image is controlled by a local image measure. In order to evaluate the performance of the filters, they were applied to a variety of regular waveforms obtained from a physiological flow phantom. Ensemble averaging of a large number of unfiltered spectra was used as the "gold standard" in the evaluation, i.e., as the output of an ideal filter which reveals the exact nature of the underlying Doppler spectrum after speckle has been eliminated. Comparison of the "gold standard" with the ensemble averaged filtered data allowed the noise reduction, bias and distortion of the maximum frequency envelope introduced by filtering to be examined. Overall, the best performance was offered by the directional filter whose action was controlled by the combination of the local edge content and the slope of the least-squares-fit line passing through the data points along each particular direction.

Algorithms↗
PubMed2000

Local contrast in natural images: normalisation and coding efficiency.

The visual system employs a gain control mechanism in the cortical coding of contrast whereby the response of each cell is normalised by the integrated activity of neighbouring cells. While restricted in space, the normalisation pool is broadly tuned for spatial frequency and orientation, so that a cell's response is adapted by stimuli which fall outside its 'classical' receptive field. Various functions have been attributed to divisive gain control: in this paper we consider whether this output nonlinearity serves to increase the information carrying capacity of the neural code. 46 natural scenes were analysed with the use of oriented, frequency-tuned filters whose bandwidths were chosen to match those of mammalian striate cortical cells. The images were logarithmically transformed so that the filters responded to a luminance ratio or contrast. In the first study, the response of each filter was calibrated relative to its response to a grating stimulus, and local image contrast was expressed in terms of the familiar Michelson metric. We found that the distribution of contrasts in natural images is highly kurtotic, peaking at low values and having a long exponential tail. There is considerable variability in local contrast, both within and between images. In the second study we compared the distribution of response activity before and after implementing contrast normalisation, and noted two major changes. Response variability, both within and between scenes, is reduced by normalisation, and the entropy of the response distribution is increased after normalisation, indicating a more efficient transfer of information.

Contrast Sensitivity↗
PubMed1996

Nonlinear enzyme kinetics can lead to high metabolic flux control coefficients: implications for the evolution of dominance.

In a classic study, Kacser & Burns (1981, Genetics 97, 639-666) demonstrated that given certain plausible assumptions, the flux in a metabolic pathway was more or less indifferent to the activity of any of the enzymes in the pathway taken singly. It was inferred from this that the observed dominance of most wild-type alleles with respect to loss-of-function mutations did not require an adaptive, meaning selectionist, explanation. Cornish-Bowden (1987, J. theor. Biol. 125, 333-338) showed that the Kacser-Burns inference was not valid when substrate concentrations were large relative to the relevant Michaelis constants. We find that in a randomly constructed functional pathway, even when substrate levels are small, one can except high values of control coefficient for metabolic flux in the presence of significant nonlinearities as exemplified by enzymes with Hill coefficients ranging from two to six, or by the existence of oscillatory loops. Under these conditions the flux can be quite sensitive to changes in enzyme activity as might be caused by inactivating one of the two alleles in a diploid. Therefore, the phenomenon of dominance cannot be a trivial "default" consequence of physiology but must be intimately linked to the manner in which metabolic networks have been moulded by natural selection.

Animals↗
PubMed2006

Automatic regulation of hemodynamic variables in acute heart failure by a multiple adaptive predictive controller based on neural networks.

Automated drug-delivery systems that can tolerate various responses to therapeutic agents have been required to control hemodynamic variables with heart failure. This study is intended to evaluate the control performance of a multiple adaptive predictive control based on neural networks (MAPC(NN)) to regulate the unexpected responses to therapeutic agents of cardiac output (CO) and mean arterial pressure (MAP) in cases of heart failure. The NN components in the MAPC(NN) learned nonlinear responses of CO and MAP determined by hemodynamics of dogs with heart failure. The MAPC(NN) performed ideal control against unexpected (1) drug interactions, (2) acute disturbances, and (3) time-variant responses of hemodynamics [average errors between setpoints (+35 ml kg(-1) min(-1) in CO and +/-0 mmHg in MAP) and observed responses; 6.4, 3.7, and 4.2 ml kg(-1) min(-1) in CO and 1.6, 1.4, and 2.7 mmHg (10.5, 20.8, and 15.3 mmHg without a vasodilator) in MAP] during 120-min closed-loop control. The MAPC(NN) could also regulate the hemodynamics in actual heart failure of a dog. Robust regulation of hemodynamics by the MAPC(NN) was attributable to the ability of on-line adaptation to adopt various responses and predictive control using the NN. Results demonstrate the feasibility of applying the MAPC(NN) using a simple NN to clinical situations.

Animals↗
PubMed2005

Dynamical models of happiness.

A sequence of models for the time evolution of one's happiness in response to external events is described. These models with added nonlinearities can produce stable oscillations and chaos even without external events. Potential implications for psychotherapy and a personal approach to life are discussed.

Adaptation, Psychological↗
PubMed1990

Nonlinear mechanisms for gain adaptation in locust photoreceptors.

Intracellular membrane potential responses were recorded from locust photoreceptors under two stimulus conditions: pairs of flashes to dark-adapted receptors, and white-noise modulated light at a range of background intensities from 500 to 15,000 effective photons per second. Nonlinear analysis of the input-output relationships were performed by estimating the Volterra and Wiener kernels of the system. The Volterra kernels obtained from the double-flash experiments were similar to the Wiener kernels obtained from the white-noise experiments, except for a change of time scale. The structure of the second-order kernels obtained with either method gave evidence for a gain control mechanism acting at an early stage of the cascade. Both feedforward and feedback nonlinearities could account for the observed system behavior at any one background level. The differences in amplitude between the kernels obtained at different background levels could be accounted for by an adaptation process which further decreased the gain of the system, acting on a slower time scale, also at some early stage of the cascade.

Animals↗
PubMed1999

A cerebellar model of timing and prediction in the control of reaching.

A simplified model of the cerebellum was developed to explore its potential for adaptive, predictive control based on delayed feedback information. An abstract representation of a single Purkinje cell with multistable properties was interfaced, using a formalized premotor network, with a simulated single degree-of-freedom limb. The limb actuator was a nonlinear spring-mass system based on the nonlinear velocity dependence of the stretch reflex. By including realistic mossy fiber signals, as well as realistic conduction delays in afferent and efferent pathways, the model allowed the investigation of timing and predictive processes relevant to cerebellar involvement in the control of movement. The model regulates movement by learning to react in an anticipatory fashion to sensory feedback. Learning depends on training information generated from corrective movements and uses a temporally asymmetric form of plasticity for the parallel fiber synapses on Purkinje cells.

Adaptation, Physiological↗
PubMed1996

A model of the cerebellum in adaptive control of saccadic gain. I. The model and its biological substrate.

We review data showing that the cerebellum is required for adaptation of saccadic gain to repeated presentations of dual-step visual targets and thus, presumably, for providing adaptive corrections for the brainstem saccade generator in response to any error created by the open-loop saccadic system. We model the adaptability of the system in terms of plasticity of synapses from parallel fibers to Purkinje cells in cerebellar cortex, stressing the integration of cerebellar cortex and nuclei in microzones as the units for correction of motor pattern generators. We propose a model of the inferior olive as an error detector, and use a 'window of eligibility' to insure that error signals that elicit a corrective movement are used to adjust the original movement, not the secondary movement. In a companion paper we simulate this large, realistic network of neural-like units to study the complex spatiotemporal behavior of neuronal subpopulations implicated in the control and adaptation of saccades.

Adaptation, Physiological↗
PubMed2004

Styles of adaptation in autoimmune thyroiditis and bipolar disorder: a pilot study.

BACKGROUND: A growing body of evidence suggests that styles of adaptation, assessed with the Serial Color-Word Test (S-CWT, a 5-trials Stroop task), are able to differentiate several mental and psychosomatic disorders. Recent findings have confirmed a very high rate of cases of autoimmune thyroiditis (so called Hashimoto disease) among bipolar patients, suggesting an etio-pathogenetic relatedness between the two ailments. Based on the latter relatedness, it was hypothesized that the same styles of adaptation, which are known to differentiate bipolar and control subjects are also characteristic of patients with autoimmune thyroiditis. METHODS: Three groups (autoimmune thyroiditis, fully remitted bipolar I, and nonclinical) of 40 women, matched on age and schooling, were administered the S-CWT. The following variables were considered: (a) early discontinuity (i.e. the summed nonlinear change of the first three trials), (b) late discontinuity (i.e. the summed nonlinear change of the last two trials), (c) across-trials discontinuity (i.e. the nonlinear change of the five measures of nonlinear change). RESULTS: The thyroiditis group had (1) higher values of early discontinuity (P=0.006) and of late discontinuity (P=0.004) compared with nonclinical controls, (2) lower values of early discontinuity (P=0.005) and of across-trials discontinuity (P=0.008) compared with the bipolar group. LIMITATIONS: The study did not include men patients and lacked a quantification of affective symptoms among clinical and nonclinical participants. CONCLUSION: A discontinuous style of adaptation is more marked among remitted bipolar than among thyroiditis patients, and more marked among the latter ones than among nonclinical controls, thus delineating a sort of adaptive continuum.

Adaptation, Psychological↗
PubMed1995

Locally activated neural networks and stable neural controller design for nonlinear dynamic systems.

A stable neural control scheme using a locally activated neural network has been proposed for a class of nonlinear dynamic systems. The locally activated neural network, for a given input, essentially selects a small subset of the network hidden nodes for output computation using the CMAC-like content addressing mechanism. This network aims to maintain local representations of the system dynamics. Thus, the global control performance in the concerned state space is achieved by the cooperation of many local control efforts and furthermore, real-time control can be facilitated because only a small sized network is involved to control and learn at any given time. The proposed control scheme is composed of two stages: (1) prediction error based learning in which the network attempts to learn the nonlinear basis functions of the plant inverse dynamics by a modified backpropagation learning rule; and (2) tracking error based learning in which the network weights are further fine-tuned using the basis set obtained in (1). This basis set spans the locally partitioned vector space of the system inverse dynamics when the prediction error based learning is achieved within a prescribed error tolerance. For uniform stability, the sliding mode control is introduced as a safety mechanism when the network has not sufficiently learned the plant dynamics yet. With suitable assumptions on the controlled plant, global stability and tracking error convergence proof has been given. Finally, the proposed control scheme is verified with computer simulation.

Adaptation, Physiological↗
PubMed2002

Fractal dynamics in physiology: alterations with disease and aging.

According to classical concepts of physiologic control, healthy systems are self-regulated to reduce variability and maintain physiologic constancy. Contrary to the predictions of homeostasis, however, the output of a wide variety of systems, such as the normal human heartbeat, fluctuates in a complex manner, even under resting conditions. Scaling techniques adapted from statistical physics reveal the presence of long-range, power-law correlations, as part of multifractal cascades operating over a wide range of time scales. These scaling properties suggest that the nonlinear regulatory systems are operating far from equilibrium, and that maintaining constancy is not the goal of physiologic control. In contrast, for subjects at high risk of sudden death (including those with heart failure), fractal organization, along with certain nonlinear interactions, breaks down. Application of fractal analysis may provide new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as to monitoring the aging process. Similar approaches show promise in assessing other regulatory systems, such as human gait control in health and disease. Elucidating the fractal and nonlinear mechanisms involved in physiologic control and complex signaling networks is emerging as a major challenge in the postgenomic era.

Aging↗
PubMed2004

Isotropic convection scenarios in an anisotropic fluid.

We study a new variant of electroconvection using a homeotropically aligned nematic liquid crystal. The novelty of this system is a direct transition to roll- or square-type patterns controlled by the frequency of the applied voltage with a rich crossover scenario and strong influence of the zigzag instability even at onset. From the weakly nonlinear theory and simulations of an adapted Swift-Hohenberg model one can understand essential features of the phase diagram. In particular we find a quasiperiodic pattern with square symmetry.

Journal Article↗
PubMed1994

Microgenetic styles of regulation in schizophrenia.

Styles of adaptation to the Stroop task were studied with the Serial Color-Word Test among 50 schizophrenic patients and 50 sex- and age-matched clinical controls. Measures of linear (R) and nonlinear change (V) in reading times were derived for each of the five interference trials. Schizophrenia was significantly characterized by (1) a high sum of nonlinear change, (2) the Cumulative-Dissociative pattern (high R and high V), (3) concomitantly high secondary Vs (V of the 5 Rs and V of the 5 Vs), and (4) Cumulative-Dissociative secondary patterns (gradual increase of both linear and nonlinear change on both the 5 Rs and the 5 Vs).

Adolescent↗