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PubMed2004

Adaptive control of unknown unstable steady states of dynamical systems.

A simple adaptive controller based on a low-pass filter to stabilize unstable steady states of dynamical systems is considered. The controller is reference-free; it does not require knowledge of the location of the fixed point in the phase space. A topological limitation similar to that of the delayed feedback controller is discussed. We show that the saddle-type steady states cannot be stabilized by using the conventional low-pass filter. The limitation can be overcome by using an unstable low-pass filter. The use of the controller is demonstrated for several physical models, including the pendulum driven by a constant torque, the Lorenz system, and an electrochemical oscillator. Linear and nonlinear analyses of the models are performed and the problem of the basins of attraction of the stabilized steady states is discussed. The robustness of the controller is demonstrated in experiments and numerical simulations with an electrochemical oscillator, the dissolution of nickel in sulfuric acid; a comparison of the effect of using direct and indirect variables in the control is made. With the use of the controller, all unstable phase-space objects are successfully reconstructed experimentally.

Journal Article↗
PubMed2004

Intelligent motion control for linear piezoelectric ceramic motor drive.

Since the dynamic characteristics of a linear piezoelectric ceramic motor (LPCM) are highly nonlinear and time varying, it is difficult to design a suitable motor drive and position controller that realizes accurate position control at all time. This study investigates a double-inductance double-capacitance (LLCC) resonant driving circuit and a sliding-mode fuzzy-neural-network control (SMFNNC) system for the motion control of an LPCM. First, the motor structure and LLCC driving circuit of an LPCM are introduced. The LLCC resonant inverter is designed to operate at an optimal switching frequency such that the output voltage will not be influenced by the variation of quality factor. Moreover, a SMFNNC system is designed to achieve favorable tracking performance without precise dynamic models being controlled. All adaptive learning algorithms in the SMFNNC system are derived in the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed driving circuit and control system is verified by experimental results.

Artificial Intelligence↗
PubMed2004

Condition for alternans and its control in a two-dimensional mapping model of paced cardiac dynamics.

We investigate a two-dimensional mapping model of a paced, isolated cardiac cell that relates the duration of the action potential to the two preceding diastolic intervals as well as the preceding action potential duration. The model displays rate-dependent restitution and hence memory. We derive a criterion for the stability of the 1:1 response pattern displayed by the model. This criterion can be written in terms of experimentally measured quantities-the slopes of restitution curves obtained via different pacing protocols. In addition, we analyze the two-dimensional mapping model in the presence of closed-loop feedback control. The control is initiated by making small adjustments to the pacing interval in order to suppress alternans and stabilize the 1:1 pattern. We find that the domain of control does not depend on the functional form of the map, and, in the general case, is characterized by a combination of the slopes. We show that the gain gamma necessary to establish control may vary significantly depending on the value of the slope of the so-called standard restitution curve (herein denoted as S12), but that the product gammaS12 stays approximately in the same range.

Action Potentials↗
PubMed2005

The role of spiking nonlinearity in contrast gain control and information transmission.

Threshold and saturation are two nonlinear features common to almost all spiking neurons. How these nonlinearities affect the performance gain of the transfer function and coding properties of the neurons has attracted much attention. Here, we deduce basic analytical relationships among these nonlinearities (threshold and saturation), performance gain and information transmission in neurons. We found that performance gain and information transmission can be maximized by input signals with optimal variance. The threshold and saturation inside the model determines the gain tuning property and maximum coding capacity. This framework provides an understanding of some basic design principles underlying information processing systems that can be adjusted to match the statistics of signals in the environment. This study also isolates the exact contributions of the nonlinearities on the contrast adaptation phenomena observed in real visual neurons.

Contrast Sensitivity↗
PubMed1983

Seasonal variations of carbon monoxide-induced polycythemia in rats.

Fluctuations of the extent of polycythemia in rats due to repeated exposure to carbon monoxide prompted us to study whether the differences were caused by seasonal variations. Data from 18 independent experiments involving the hemoglobin values of 206 CO-exposed and 208 control rats over a period of nearly 3 years were available. Because the experiments were not designed for chronobiological studies, several methods of quasilinear and nonlinear regression analysis were applied to fit periodic models to the data. The best fit was obtained by a single cosine function with a period of nearly 12 months and with maximum values in winter and minimum values in summer. Experiments to elucidate mechanism(s) of seasonal variations showed that following a single CO exposure to the nocturnally active rodents, the rate of CO release from the body was higher in the dark span, that reduced food supply produced a potentiation of the CO-induced hemoglobin increase, and that the adaptive CO response was not affected by testosterone or thyroidectomy, but inhibited by L-thyroxine. Thus it seems that influences of light, CO elimination rate, food intake, metabolic rate and related hormones play a role for seasonal variations.

Animals↗
PubMed2006

Individual SNP allele reconstruction from informative markers selected by a non-linear Gauss-type algorithm.

OBJECTIVES: In view of the linkage disequilibrium structure of the genome, the selection of maximally informative SNP markers is a fundamental issue in the design of association studies. Currently used selection methods rely on pairwise marker correlation or informativity measures for subsets of markers. Nevertheless, the selected markers do not provide a completely satisfactory description of the individual remaining markers. The number of tag markers can be further reduced by using haplotypic information, but then the results of association analysis are difficult to interpret. METHODS AND RESULTS: We propose a non-linear Gauss-type algorithm selecting a subset of markers which is optimal with respect to the informativity measures and allows an explicit reconstruction of all other known markers, thus permitting direct inference of allelic association. The selection is based on the haplotype distribution in the population, but can be adapted to work with unphased genotypes directly. CONCLUSIONS: The proposed algorithm provides a rational methodology of informative marker selection, allowing for control and optimisation of information content and full marker reconstruction. Moreover, the reconstruction step can also be applied to tag markers selected using a different method at the stage of study design, identifying those markers which cannot be uniquely recovered from the chosen tags.

Algorithms↗
PubMedDate not supplied

Chaos control and synchronization, with input saturation, via recurrent neural networks.

This paper deals with the adaptive tracking problem of non-linear systems in presence of unknown parameters, unmodelled dynamics and input saturation. A high order recurrent neural network is used in order to identify the unknown system and a learning law is obtained using the Lyapunov methodology. Then a stabilizing control law for the reference tracking error dynamics is developed using the Lyapunov methodology and the Sontag control law. Tracking error boundedness is established as a function of a design parameter. The new approach is illustrated by examples of complex dynamical systems: chaos control and synchronization.

Neural Networks, Computer↗
PubMed1983

Response of pulmonary rapidly adapting receptors during lung inflation.

Studies were conducted to establish the factors that determine the response of canine pulmonary rapidly adapting receptors (RAR) during lung inflation. Inflations of the lung were performed at several constant rates during which the activity of individual RAR was counted. At each rate of inflation tested multiple identical tests were performed. The volume of each test inflation was controlled. Data obtained in all tests at each flow rate were averaged to give the mean response of the receptor at that rate of inflation. These studies indicate the major response characteristics of RAR during lung inflation in conditions of relatively constant lung mechanics. First, at a constant rate of inflation, the activity of RAR augments increasingly as the lung is expanded. Second, their activity is influenced markedly by the rate of inflation. However, this sensitivity is nonlinear. Specifically, at low rates of inflation increases in flow rate produce more marked augmentation of RAR firing than do identical increases in flow at higher rates of inflation. The major difference between receptors is in their threshold; however, this too is a function of flow rate. With increasing flow rate the threshold, whether measured as the inflation volume or transpulmonary pressure at which receptors begin to fire, declines. The response of receptors, however, with thresholds over the entire range show the major features discussed above. The present results provide quantitative information which are necessary to begin to eludicate the transduction properties of this receptor type.

Animals↗
PubMed2003

Biomechanical properties of knee articular cartilage.

Structure and properties of knee articular cartilage are adapted to stresses exposed on it during physiological activities. In this study, we describe site- and depth-dependence of the biomechanical properties of bovine knee articular cartilage. We also investigate the effects of tissue structure and composition on the biomechanical parameters as well as characterize experimentally and numerically the compression-tension nonlinearity of the cartilage matrix. In vitro mechano-optical measurements of articular cartilage in unconfined compression geometry are conducted to obtain material parameters, such as thickness, Young's and aggregate modulus or Poisson's ratio of the tissue. The experimental results revealed significant site- and depth-dependent variations in recorded parameters. After enzymatic modification of matrix collagen or proteoglycans our results show that collagen primarily controls the dynamic tissue response while proteoglycans affect more the static properties. Experimental measurements in compression and tension suggest a nonlinear compression-tension behavior of articular cartilage in the direction perpendicular to articular surface. Fibril reinforced poroelastic finite element model was used to capture the experimentally found compression-tension nonlinearity of articular cartilage.

Animals↗
PubMed1995

Nonlinear dynamical patterns as personality theory for neurobiology and psychiatry.

ADVANCES in the theory of nonlinear differential equations and their statistical representations have yielded a powerful, qualitatively descriptive yet quantitative language that captures characteristic patterns of behavior (what the psychoanalyst Roy Schafer calls "continuity, coherence, and consistency of action") that has begun to influence studies of complex systems in motion as diverse in specifics as signatory patterns of discharge of neurochemically defined single neurons and the dynamical structures characteristic of a particular composer's music. What might be called personality theories of neurobiological dynamics have arisen to replace neurobiological theories of personality. It is in this way that rigorously proven and powerful general mathematical insights have changed the face of determinism in research in brain and behavior. Two examples: (1) Very complicated looking behavior of neurobiological forced-dissipative (expanding and contracting) systems over time take place on low dimensional abstract surfaces on which only a few underlying abstract parameters control the action. (2) Independent of specific details (chemical, electrical, and/or behavioral), there exist a relatively few fundamental categories of behavior in time and transitions, among them a property called universality. Results from this new theoretical, in contrast with experimental, reductionism yield analogies with and new approaches to historically important dynamic ideas about personality and character patterns that are equally relevant to micro and macrocomplex systems such as neural membrane receptor proteins and individual personality styles. Research findings achieved over the past decade and a half in our laboratory and others in neurochemistry, neurophysiology, and animal and human behavior, as well as the results of a new demonstration experiment involving the prediction of dynamical category membership from abstract expressive motion in humans, are used to exemplify this use of a quantitative dynamic category theory across disciplinary levels in brain and behavior. Multiple measures of complexity adapted from current research in the statistical properties of chaos on unobtrusively observed and reconstructed orbits on the computer screen made by non-premorbid subjects executing content-free, computer-game-like tasks with a mouse, were used to reliably differentiate the "signatures" of two Axis II diagnoses as established using SCID-II criteria. Whereas the techniques of nonlinear systems have achieved some success in quantifying and stimulating the dynamical styles of relatively local phenomena such as the spontaneous behavior of neuronal membrane conductances, single neurons, neural networks, and field electrical events, we think that the real power of these techniques lies in their quantitative description and statistical prediction of global patterns of behavior of entire systems. For example, since the late 1970s our work has shown that these measures could be used to discriminate categories of drug action and dose when applied to patterns of rat exploratory behavior in space and time. The combination of abstract generality and quantitative precision of these methods suggests their usefulness as a cross-disciplinary language for fields like psychiatry that deal with complicated behavior of both neurobiological elements and "the whole person."

Behavior↗
PubMed2001

Learning the dynamics of reaching movements results in the modification of arm impedance and long-latency perturbation responses.

Some characteristics of arm movements that humans exhibit during learning the dynamics of reaching are consistent with a theoretical framework where training results in motor commands that are gradually modified to predict and compensate for novel forces that may act on the hand. As a first approximation, the motor control system behaves as an adapting controller that learns an internal model of the dynamics of the task. It approximates inverse dynamics and predicts motor commands that are appropriate for a desired limb trajectory. However, we had previously noted that subtle motion characteristics observed during changes in task dynamics challenged this simple model and raised the possibility that adaptation also involved sensory-motor feedback pathways. These pathways reacted to sensory feedback during the course of the movement. Here we hypothesize that adaptation to dynamics might also involve a modification of how the CNS responds to sensory feedback. We tested this through experiments that quantified how the motor system's response to errors during voluntary movements changed as it adapted to dynamics of a force field. We describe a nonlinear approach that approximates the impedance of the arm, i.e., force response as a function of arm displacement trajectory. We observe that after adaptation, the impedance function changes in a way that closely matches and counters the effect of the force field. This is particularly prominent in the long-latency (> 100 ms) component of response to perturbations. Therefore, it appears that practice not only modifies the internal model with which the brain generates motor commands that initiate a movement, but also the internal model with which sensory feedback is integrated with the ongoing descending commands in order to respond to error during the movement.

Adult↗
PubMed2002

An approach for assessing estrogen receptor-mediated interactions in mixtures of three chemicals: a pilot study.

Most studies investigating interactions among endocrine-active chemicals have been limited to binary mixtures. This study reports on the preliminary evaluation an in vitro MCF-7 cell ER-alpha reporter gene system, coupled with a statistical methodology adapted for assessing interactions within ternary (3-chemical) mixtures. Two mixtures were initially chosen for assessment of the in vitro system's ability to detect additivity (mixture A) as well as greater-than-additive (mixture B) responses. Mixture A was composed of 17beta-estradiol (E2), ethinyl estradiol, and diethylstilbestrol and served as a control for additivity, whereas mixture B (E2, epidermal growth factor, insulin-like growth factor-I) was selected to model greater-than-additive interactions based on previous in vitro studies. After generating complete dose-response curves for each chemical, ternary mixtures were then tested in a full factorial design (4 concentrations per chemical, 64 treatment groups). A response surface was estimated using a nonlinear mixed model, and the observed responses were statistically analyzed for departures from the responses expected under the assumption of additivity. Mixture A exhibited additivity in vitro when the chemicals were present at concentrations in the linear range of their individual dose-response curves. For mixture B, in vitro analysis resulted in the additivity hypothesis being rejected (p < 0.001) because of a greater-than-additive interaction, as expected. A limited in vivo evaluation of mixture A was performed in the immature mouse uterotrophic assay (27 treatment groups), which agreed with the in vitro assessment of no significant departure from additivity ( p = 0.903). These findings demonstrate the ability of this in vitro methodology to detect additive, greater-than-additive, and less-than-additive interactions within ternary mixtures, which now allows for the assessment of environmentally relevant mixtures.

Animals↗
PubMed1998

Multiple synchronization strategies in rhythmic sensorimotor tasks: phase vs period correction.

To characterize synchronisation strategies in the tracking of auditory rhythm with rhythmic finger tapping, the adaptation process after unexpected step changes of an interstimulus interval (ISI) of 500 ms was investigated. Step changes of 2% (10 ms), 4% (20 ms), and 10% (50 ms) of ISI were applied to the stimulus sequence. Synchronisation patterns of 5 subjects were analyzed based on synchronisation error (SE) and interresponse intervals (IRI). A strategy shift contigent upon the size of the introduced step change was detected. After small ISI changes, rapid IRI matching to the new ISI was accompanied by temporarily enlarged SE values, which slowly returned to preferred SE values before the step change. Large ISI changes showed quick SE adaptations accompanied by a temporary overcorrection of IRI. Response asymmetry between ISI decreases and increases emerged, showing a stronger adaptation during ISI increases. A two-dimensional difference equation was formulated to simulate the time series of intertap intervals and explain the control process during IRI and SE adjustments. The system constants were optimized to minimalize the deviations between the computed and the observed response trajectories, consisting of the time series of SE and IRI. It was shown that a successful model fit using a linear two-dimensional difference equation was based on the size and direction of the ISI changes. MANOVA procedures showed that differences in equation parameters during small and large step changes were statistically significant (P < 0.05). It is therefore suggested that a uniform model accounting for synchronization responses to all step changes would require the introduction of nonlinear system properties.

Acoustic Stimulation↗
PubMed1990

Synaptic and intrinsic control of membrane excitability of neostriatal neurons. I. An in vivo analysis.

1. The relationship between membrane properties of neostriatal neurons and spontaneous and evoked synaptic potentials was studied with the use of intracellular recordings from anesthetized rats. Most of these neurons showed regular or irregular spontaneous depolarizing potentials that only in a few cases triggered action potentials at resting level. 2. The stimulation of the ipsilateral substantia nigra or of the sensorimotor cortex produced a relatively fast depolarizing post-synaptic potential (EPSP). In some cells this potential was followed by an inhibitory period that appeared as an hyperpolarization when the cell was depolarized from the resting level (inhibitory postsynaptic potential, IPSP). A late and long-lasting depolarization (LD) followed the EPSP or the EPSP-IPSP sequence. 3. Repetitive discharge with little adaptation was observed during direct depolarization. Most of the neurons tested for current-voltage (I-V) relationship showed nonlinearity of the input resistance in the hyperpolarizing direction. Spontaneous and evoked EPSPs were decreased in their amplitude and duration when the membrane potential was held at levels more hyperpolarized than -85 mV because of the strong rectification at these levels of hyperpolarization. 4. Local microiontophoretic application of bicuculline (BIC) or systemic administration of BIC and pentylenetetrazole (PTZ) produced a reduction of the IPSPs. The reduction of the inhibitory transmission caused a strong increase of the LD. The current-evoked firing pattern was not greatly altered. 5. The intracellular application of cesium increased the amplitude and the duration of the spontaneous depolarizations that triggered bursts of action potentials under this condition. Spikes were broadened and the rectification in the hyperpolarization direction was reduced. 6. Iontophoretically applied cadmium strongly depressed the amplitude of the spontaneous and evoked postsynaptic potentials. During cadmium application, nigral stimulation produced constant latency, all-or-none spikes in the absence of any synaptic potential. 7. Repetitive stimulation of the ipsilateral substantia nigra by electrical shocks (5 Hz, 25 s) produced a progressive and reversible decrease of the spontaneous depolarizing potentials (SDPs) and a decrease of the firing rate. In the same cells, when the train of stimulation was delivered in the ipsilateral cortex, a membrane depolarization coupled with an increase of the firing rate was observed. 8. We conclude that although synaptic circuits mediate a phasic inhibition in neostriatum, the low level of spontaneous firing of most neostriatal neurons is mainly because of the effects that membrane properties exert on the spontaneous and the evoked synaptic depolarizations in the striatum.(ABSTRACT TRUNCATED AT 400 WORDS)

Action Potentials↗
PubMed1995

Efficient sampling strategies for forecasting pharmacokinetic parameters of irinotecan (CPT-11): implication for area under the concentration-time curve monitoring.

A linear two-compartment Bayesian pharmacokinetic model was developed using a standard two-stage population method for the novel anti-cancer agent CPT-11 from 11 adult patients with refractory cancer. The accuracy and efficiency of this Bayesian model for estimating pharmacokinetic parameters including the area under the concentration-time curve (AUC) was then evaluated using two different sampling strategies in a new study cohort of 13 patients with cancer. Sampling strategies included either one, two, or three nonsteady-state feedback levels determined empirically and from optimal sampling theory (D-optimality). All 24 patients in this study received CPT-11 (60 mg/m2) as a 90-min infusion. Pharmacokinetic parameters derived from the Bayesian model combined with these limited sampling strategies were compared with those parameters obtained from the full sample data sets (n = 10) analyzed by weighted nonlinear least squares regression (reference method). The least-bias and most precise sampling times for estimating AUC were 3.5; 3.5 and 9.5; and 0.5, 3.5, and 9.5 h, respectively. At these times, only marginal improvement in precision of the AUC estimate was observed using two versus three samples. However, the precision of the estimate of clearance was not improved using two versus three samples. The sampling times derived from optimal sampling theory were 0.25, 3.5, 8.5, and 24 h and correlated closely to the actual and best empirical sampling times of 0.5, 3.5, 9.5, and 24 h. These results strongly suggest that Bayesian estimation combined with only two optimally timed samples accurately predicts the AUC of CPT-11 and should be useful for implementing adaptive control dosing for monitoring CPT-11 systemic exposure in patients with cancer.

Adult↗
PubMed2001

Impact of comprehensive day treatment on societal participation for persons with acquired brain injury.

OBJECTIVES: To evaluate comprehensive day treatment (CDT) for survivors of brain injury by time since injury and to identify outcome predictors. DESIGN: Before and after. SETTING: Rehabilitation center. PARTICIPANTS: Ninety-six program graduates; 17 dropouts with acquired brain injury. INTERVENTIONS: Comprehensive Day Treatment Program: daily group sessions to build cognitive and behavioral skills through a transdisciplinary approach, supportive feedback, and a variety of therapeutic modalities. Obtained outcome measures before and after the program, and at 1-year follow-up. MAIN OUTCOME MEASURES OUTCOME: Independent living status, vocational independence scale at program end and 1-year follow-up; and Rasch-analyzed Mayo-Portland Adaptability Inventory (MPAI-22) and goal attainment scaling (GAS) at program end. PREDICTORS: age, education, severity of initial injury, time since injury, and preadmission MPAI-22. RESULTS: Significant goal achievement on GAS and improvement on MPAI-22; increased societal participation at 1-year follow-up for those treated postacutely and many years after injury: 72% of graduates living independently; 39% working independently, 10% in transitional placements, and 18% in supported or volunteer work. Long-term outcomes were modestly related linearly to preadmission MPAI-22 and nonlinearly to time since injury. CONCLUSIONS: CDT improves societal participation even among persons with a long history of limited participation after brain injury. This de facto extended baseline analysis indicates the effectiveness of CDT and paves the way for randomized control trials of active treatment components. Relationships of predictors to outcomes are not sufficiently strong for patient selection. More effective interventions for vocational reintegration are needed for those most severely disabled after brain injury.

Activities of Daily Living↗
PubMed2005

Temporal nonlinearity during recovery from sequential inhibition by neurons in the cat primary auditory cortex.

Auditory stimuli occur most often in sequences rather than in isolation. It is therefore necessary to understand how responses to sounds occurring in sequences differ from responses to isolated sounds. Cells in primary auditory cortex (AI) respond to a large set of binaural stimuli when presented in isolation. The set of responses to such stimuli presented at one frequency comprises a level response area. A preceding binaural stimulus can reduce the size and magnitude of level response areas of AI cells. The present study focuses on the effects of the time interval between a preceding stimulus and the stimuli of a level response area in pentobarbital-anesthetized cats. After the offset of a preceding stimulus, the ability of AI cells to respond to succeeding stimuli varies dynamically in time. At short interstimulus intervals (ISI), a preceding stimulus can completely inhibit responses to succeeding stimuli. With increasing ISIs, AI cells respond first to binaural stimuli that evoke the largest responses in the control condition, i.e., not preceded by a stimulus. Recovery rate is nonlinear across the level response area; responses to these most-effective stimuli recover to 70% of control on average 187 ms before responses to other stimuli recover to 70% of their control sizes. During the tens to hundreds of milliseconds that a level response area is reduced in size and magnitude, the selectivity of AI cells is increased for stimuli that evoke the largest responses. This increased selectivity results from a temporal nonlinearity in the recovery of the level response area which protects responses to the most effective binaural stimuli. Thus in a sequence of effective stimuli, a given cell will respond selectively to only those stimuli that evoke a strong response when presented alone.

Acoustic Stimulation↗
PubMed2004

The effect of strain rate on the viscoelastic response of aortic valve tissue: a direct-fit approach.

Knowledge of strain-rate sensitivity of soft tissue viscoelastic and nonlinear elastic properties is important for accurate predictions of biomechanical behavior and for quantitative assessment of the effects of disease or surgical/pharmaceutical intervention. Soft tissues are known to exhibit mild rate sensitivity, but experimental artifacts related to testing system control can confound estimation of these effects. "Perfect" ramp-and-hold stress-relaxation tests become difficult at high strain rates because of problems related to undershoot/overshoot error and vibrations. These errors can introduce unwanted bias into parameter estimation methods that rely on idealizations of the applied ramp-and-hold displacement. To address these problems, we describe a new method for estimating quasilinear viscoelastic (QLV) parameters that directly fits the QLV constitutive model to the actual point-wise stress-time history of the test, using an adaptive grid refinement (AGR) global optimization algorithm. This new method significantly improves the accuracy and predictivity of QLV parameter estimates for heart valve tissues, compared to traditional methods that use idealized displacement data. We estimated QLV parameters for aortic valve tissue over a range of physiologic displacement rates, finding that the viscoelastic content parameter (C) increased slightly with increasing strain rate, but the fast (tau1) and slow (tau2) time constants were strain rate insensitive.

Algorithms↗