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At least 253 records · Page 14Linked to original sources
PubMed2005

Flexible control of mutual inhibition: a neural model of two-interval discrimination.

Networks adapt to environmental demands by switching between distinct dynamical behaviors. The activity of frontal-lobe neurons during two-interval discrimination tasks is an example of these adaptable dynamics. Subjects first perceive a stimulus, then hold it in working memory, and finally make a decision by comparing it with a second stimulus. We present a simple mutual-inhibition network model that captures all three task phases within a single framework. The model integrates both working memory and decision making because its dynamical properties are easily controlled without changing its connectivity. Mutual inhibition between nonlinear units is a useful design motif for networks that must display multiple behaviors.

Algorithms↗
PubMedDate not supplied

[Mitochondrial energy processes of the visual system in the rabbit brain normally and under conditions of light deprivation].

A rate of endogenous respiration in mitochondria of rabbit brain visual system (visual cortex, forebrain) was higher under conditions of light deprivation (from birth up to 2.5 month) as compared with the mitochondria of control animals. The mitochondria of experimental rabbits were characterized by distinct alteration in oxidative phosphorylation of glutamic acid, by an increased rate of electron transport at the step between cytochrome c-cytochrome oxidase-succinate dehydrogenase of the respiratory chain as well as by the peculiar effect of rothenone and DNP on the chain. All the patterns studied approached the control value within the period of restoration of light impulsation. Nonlinear type of the regenerating processes was observed. Role of specific impulsation and compensatory reactions in the age-dependent development of energy processes in brain mitochondria is discussed.

Animals↗
PubMed2004

On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions.

We study networks of spiking neurons that use the timing of pulses to encode information. Nonlinear interactions model the spatial groupings of synapses on the neural dendrites and describe the computations performed at local branches. Within a theoretical framework of learning we analyze the question of how many training examples these networks must receive to be able to generalize well. Bounds for this sample complexity of learning can be obtained in terms of a combinatorial parameter known as the pseudodimension. This dimension characterizes the computational richness of a neural network and is given in terms of the number of network parameters. Two types of feedforward architectures are considered: constant-depth networks and networks of unconstrained depth. We derive asymptotically tight bounds for each of these network types. Constant depth networks are shown to have an almost linear pseudodimension, whereas the pseudodimension of general networks is quadratic. Networks of spiking neurons that use temporal coding are becoming increasingly more important in practical tasks such as computer vision, speech recognition, and motor control. The question of how well these networks generalize from a given set of training examples is a central issue for their successful application as adaptive systems. The results show that, although coding and computation in these networks is quite different and in many cases more powerful, their generalization capabilities are at least as good as those of traditional neural network models.

Action Potentials↗
PubMed1999

Nonlinear model-based predictive control of non-depolarizing muscle relaxants using neural networks.

Neuromuscular blockade can be relatively easily measured in the clinical setting. Consequently, closed-loop control can be exercised by measuring the neuromuscular activity, calculating the dose of drug necessary to achieve a predefined degree of neuromuscular blockade and finally directing an infusion pump. Recently introduced short-acting blocking agents like mivacurium provide benefits for the clinical routine due to a small onset time and half life. In order to provide a stable blockade for different groups of patients a fast and highly adaptable control unit is needed. Furthermore its development should not imply costly investigations for determining a pharmacological model. The fulfilling of these requirements yield a self-adapting model-based predictive control system. The application of artificial neural networks allows an appropriate adjustment of specific parameters without the knowledge of inner pharmacodynamic processes. In a clinical study the EMG module within a Datex AS/3 monitor was used to measure the blockade and a Grasepy 3500 infusion pump for i.v. administration of mivacurium to 35 patients (ASA I-III). The performance of the novel system (mean of the T1 error: -0.32 +/- 1.7) compares favourably with closed-loop controllers demonstrated in the past. These promising results and the easy adaption to other blocking agents encourage to apply this technology even for delivering hypnotic drugs.

Electromyography↗
PubMed2001

Is there chaos in the brain? I. Concepts of nonlinear dynamics and methods of investigation.

In the light of results obtained during the last two decades in a number of laboratories, it appears that some of the tools of nonlinear dynamics, first developed and improved for the physical sciences and engineering, are well-suited for studies of biological phenomena. In particular it has become clear that the different regimes of activities undergone by nerve cells, neural assemblies and behavioural patterns, the linkage between them, and their modifications over time, cannot be fully understood in the context of even integrative physiology, without using these new techniques. This report, which is the first of two related papers, is aimed at introducing the non expert to the fundamental aspects of nonlinear dynamics, the most spectacular aspect of which is chaos theory. After a general history and definition of chaos the principles of analysis of time series in phase space and the general properties of chaotic trajectories will be described as will be the classical measures which allow a process to be classified as chaotic in ideal systems and models. We will then proceed to show how these methods need to be adapted for handling experimental time series; the dangers and pitfalls faced when dealing with non stationary and often noisy data will be stressed, and specific criteria for suspecting determinism in neuronal cells and/or assemblies will be described. We will finally address two fundamental questions, namely i) whether and how can one distinguish, deterministic patterns from stochastic ones, and, ii) what is the advantage of chaos over randomness: we will explain why and how the former can be controlled whereas, notoriously, the latter cannot be tamed. In the second paper of the series, results obtained at the level of single cells and their membrane conductances in real neuronal networks and in the study of higher brain functions, will be critically reviewed. It will be shown that the tools of nonlinear dynamics can be irreplaceable for revealing hidden mechanisms subserving, for example, neuronal synchronization and periodic oscillations. The benefits for the brain of adopting chaotic regimes with their wide range of potential behaviours and their aptitude to quickly react to changing conditions will also be considered.

Animals↗
PubMed2002

A revised model of the inner-hair cell and auditory-nerve complex.

A revised computational model of the inner-hair cell (IHC) and auditory-nerve (AN) complex is presented and evaluated. Building on previous models, the algorithm is intended as a component for use in more comprehensive models of the auditory periphery. It combines smaller components that aim to be faithful to physiology in so far as is practicable and known. Transduction between cochlear mechanical motion and IHC receptor potential (RP) is simulated using a modification of an existing biophysical IHC model. Changes in RP control the opening of calcium ion channels near the synapse, and local calcium levels determine the probability of the release of neurotransmitter. AN adaptation results from transmitter depletion. The exact timing of AN action potentials is determined by the quantal and stochastic release of neurotransmitter into the cleft. The model reproduces a wide range of animal RP and AN observations. When the input to the model is taken from a suitably nonlinear simulation of the motion of the cochlear partition, the new algorithm is able to simulate the rate-intensity functions of low-, medium-, and high-spontaneous rate AN fibers in response to stimulation both at best frequency and at other frequencies. The variation in fiber type arises in large part from the manipulation of a single parameter in the model: maximum calcium conductance. The model also reproduces quantitatively phase-locking characteristics, relative refractory effects, mean-to-variance ratio, and first- and second-order discharge history effects.

Algorithms↗
PubMed2003

Optimal coding through divisive normalization models of V1 neurons.

Current models of the primary visual cortex (V1) include a linear filtering stage followed by a gain control mechanism that explains some of the nonlinear behaviour of neurons. This nonlinear stage consists of a divisive normalization in which each linear response is squared and then divided by a weighted sum of squared linear responses in a certain neighbourhood plus a constant. Simoncelli and Schwartz (1999 Adv. Neural Inform. Process. Syst. 11 153-9) have suggested that divisive normalization reduces the statistical dependence between neuron responses when the weights are adapted to the statistics of natural images, which is consistent with the efficient coding hypothesis. Nevertheless, there are still important open issues, such as, for example, how to obtain the values for the parameters that minimize statistical dependence? Does divisive normalization give a total independence between responses? In this paper, we present the general mathematical formulation of the first of these two questions. We arrive at an expression which permits us to compute, numerically, the parameters of a quasi-optimal solution adapted to an input set of natural images. This quasi-optimal solution is based on a Gaussian model of the conditional statistics of the coefficients resulting from projecting natural images onto an orthogonal linear basis. Our results show, in general, lower values of mutual information, that is, responses are more independent than those provided by previous approximations.

Models, Neurological↗
PubMed2004

A novel model of motor learning capable of developing an optimal movement control law online from scratch.

A computational model of a learning system (LS) is described that acquires knowledge and skill necessary for optimal control of a multisegmental limb dynamics (controlled object or CO), starting from "knowing" only the dimensionality of the object's state space. It is based on an optimal control problem setup different from that of reinforcement learning. The LS solves the optimal control problem online while practicing the manipulation of CO. The system's functional architecture comprises several adaptive components, each of which incorporates a number of mapping functions approximated based on artificial neural nets. Besides the internal model of the CO's dynamics and adaptive controller that computes the control law, the LS includes a new type of internal model, the minimal cost (IM(mc)) of moving the controlled object between a pair of states. That internal model appears critical for the LS's capacity to develop an optimal movement trajectory. The IM(mc) interacts with the adaptive controller in a cooperative manner. The controller provides an initial approximation of an optimal control action, which is further optimized in real time based on the IM(mc). The IM(mc) in turn provides information for updating the controller. The LS's performance was tested on the task of center-out reaching to eight randomly selected targets with a 2DOF limb model. The LS reached an optimal level of performance in a few tens of trials. It also quickly adapted to movement perturbations produced by two different types of external force field. The results suggest that the proposed design of a self-optimized control system can serve as a basis for the modeling of motor learning that includes the formation and adaptive modification of the plan of a goal-directed movement.

Adaptation, Physiological↗
PubMed2004

Analysis and simulation of gain control and precision in crayfish visual interneurons.

Impulse trains in sustaining and dimming fibers of crayfish optic lobe (in situ) were elicited with sinusoidal extrinsic current and sine-wave illumination. Extrinsic currents and currents derived from postsynaptic potentials (PSPs) were used to compute the time course of the spike train with an adaptive integrate-and-fire model. The neurons exhibit variations in gain and spike timing precision related to the frequency of stimulation. These phenomena are influenced by spike-frequency adaptation and nonlinearities in the PSP. Dimming fibers exhibit relatively strong spike-frequency adaptation and an associated increase in gain with the frequency of sinusoidal extrinsic current and sine-wave illumination. The dimming fiber IPSP promotes spike train rectification, and rectification contributes to spike timing precision. Sustaining fibers exhibit weaker spike-frequency adaptation and the gain of the current-elicited response is less sensitive to stimulus frequency. The sustaining fiber excitatory PSP, however, exhibits a strong frequency-dependent nonlinearity that influences the frequency response. Spike timing precision is a function of stimulus frequency in all cells and it is enhanced by rectification of the discharge and/or resonance. In rectified responses the jitter in spike times is closely related to the variance in the times the membrane potential reaches spike threshold. These gain and spike timing results are well approximated by the simulated responses. Because the nonlinearity of the sustaining fiber PSP entails a high rate of depolarization, the PSP can increase the precision of spike timing by 10- to 100-fold compared with the response to pure sine-wave stimuli. This enhanced precision has implications for crayfish oculomotor reflexes that are driven by sustaining fibers and highly sensitive to impulse timing during transient excitation.

Animals↗
PubMed1999

Learning model for coupled neural oscillators.

Neurophysiological experiments have shown that many motor commands in living systems are generated by coupled neural oscillators. To coordinate the oscillators and achieve a desired phase relation with desired frequency, the intrinsic frequencies of component oscillators and coupling strengths between them must be chosen appropriately. In this paper we propose learning models for coupled neural oscillators to acquire the desired intrinsic frequencies and coupling weights based on the instruction of the desired phase pattern or an evaluation function. The abilities of the learning rules were examined by computer simulations including adaptive control of the hopping height of a hopping robot. The proposed learning rule takes a simple form like a Hebbian rule. Studies on such learning models for neural oscillators will aid in the understanding of the learning mechanism of motor commands in living bodies.

Action Potentials↗
PubMed2006

Fractal rigidity by enhanced sympatho-vagal antagonism in heartbeat interval dynamics elicited by central application of corticotropin-releasing factor in mice.

The dynamics of heartbeat interval fluctuations were studied in awake unrestrained mice following intracerebroventricular application of the neuropeptide corticotropin-releasing factor (CRF). The cardiac time series derived from telemetric ECG monitoring were analyzed by non-parametric techniques of nonlinear signal processing: delay-vector variance (DVV) analysis, higher-order variability (HOV) analysis, empirical mode decomposition (EMD), multiscale embedding-space decomposition (MESD), multiexponent multifractal (MEMF) analysis. The analyses support the conjecture that cardiac dynamics of normal control mice has both deterministic and stochastic elements, is nonstationary, nonlinear, and exerts multifractal properties. Central application of CRF results in bradycardia and increased variability of the beat-to-beat fluctuations. The altered dynamical properties elicited by CRF reflect a significant loss of intrinsic structural complexity of cardiac control which is due to central neuroautonomic hyperexcitation, i.e., enhanced sympatho-vagal antagonism. The change in dynamical complexity is characterized by an effect referred to as fractal rigidity, leading to a significant impairment of adaptability to extrinsic challenges in a fluctuating environment. The impact of dynamical neurocardiopathy as a major precipiting factor for the propensity of cardiac arrhythmias or sudden cardiac death by unchecked central CRF release in significant acute life events in man is critically discussed.

Animals↗
PubMed2005

Adapting locomotion to different surface compliances: neuromuscular responses and changes in movement dynamics.

Knowledge of how the nervous system deals with surfaces with different physical properties such as compliance that challenge balance during locomotion is of importance as we are constantly faced with these situations every day. The purpose of this study was to examine the control of center of mass (COM) and lower limb dynamics and recovery response modulation of muscle activity during locomotion across an unexpected compliant surface and in particular, scaling behavior across different levels of compliance. Eight young adults walked along a walkway and stepped on an unexpected compliant surface in the middle of the travel path. There were three different levels of surface compliance, and participants experienced either no compliant surface or one of the three compliant surfaces during each trial that were presented in a blocked or random fashion. Whole body kinematics were collected along with surface electromyography (EMG) of selected bilateral lower limb and trunk muscles. The recovery response to the first compliant-surface trial demonstrated muscle onset latencies between 97 and 175 ms, and activity was modulated while on the compliant surface. Vertical COM trajectory was not preserved after contact with the compliant surface: peak vertical COM, while on the compliant surface was lower than when on stable ground. Perturbed-limb knee flexion after toe-off increased with increased surface compliance, which enabled toe clearance with the ground to be similar to control trials. The results suggest that stepping off of a compliant surface is actively modulated by the CNS and is geared toward maintaining dynamic stability.

Adaptation, Physiological↗
PubMedDate not supplied

Practical real-time computing system for biomedical experiment interface.

Many biomedical experiments require a precisely timed real-time (RT) computer interface. Because commonly used desktop operating systems are inherently non-real-time, real-time laboratory computer systems are often based on outdated DOS software or expensive proprietary real-time operating systems. Here we discuss a real-time computing system, based on the free RT-LINUX operating system, which we have developed for adaptive pacing control in a clinical cardiac electrophysiology laboratory. This powerful, flexible, and inexpensive system demonstrates that RT-LINUX is well suited for real-time biomedical experiment interface.

Algorithms↗
PubMedDate not supplied

Control uncertain continuous-time chaotic dynamical system.

The new chaos control method presented in this paper is useful for taking advantage of chaos. Based on sliding mode control theory, this paper provides a switching manifold controlling strategy of chaotic system, and also gives a kind of adaptive parameters estimated method to estimate the unknown systems' parameters by which chaotic dynamical system can be synchronized. Taking the Lorenz system as example, and with the help of this controlling strategy, we can synchronize chaotic systems with unknown parameters and different initial conditions.

Computer Simulation↗
PubMed2004

Optically injected laser system: characterization of chaos, bifurcation, and control.

A single mode semiconductor laser subjected to optical injection, described by a set of three coupled nonlinear ordinary differential equations, exhibiting chaos is considered. By means of a recurrence analysis, quantification of the strange attractor is made. Analytical studies of the system using asymptotic averaging technique, derive certain conditions describing the prediction of 1-->2 bifurcation, which have subsequently been verified on numerical simulation. Furthermore, the locus of points on the parameter phase space representing Hopf bifurcation has been derived. The problem of control of chaos by a new procedure based on adaptive stabilization is also addressed. The results of such control are shown explicitly. Though this analysis deals with a very specific set of equations, the overall features that come out of the study remains valid for almost all laser systems.

Journal Article↗
PubMed2004

Novel direct and self-regulating approaches to determine optimum growing multi-experts network structure.

This paper presents two novel approaches to determine optimum growing multi-experts network (GMN) structure. The first method called direct method deals with expertise domain and levels in connection with local experts. The growing neural gas (GNG) algorithm is used to cluster the local experts. The concept of error distribution is used to apportion error among the local experts. After reaching the specified size of the network, redundant experts removal algorithm is invoked to prune the size of the network based on the ranking of the experts. However, GMN is not ergonomic due to too many network control parameters. Therefore, a self-regulating GMN (SGMN) algorithm is proposed. SGMN adopts self-adaptive learning rates for gradient-descent learning rules. In addition, SGMN adopts a more rigorous clustering method called fully self-organized simplified adaptive resonance theory in a modified form. Experimental results show SGMN obtains comparative or even better performance than GMN in four benchmark examples, with reduced sensitivity to learning parameters setting. Moreover, both GMN and SGMN outperform the other neural networks and statistical models. The efficacy of SGMN is further justified in three industrial applications and a control problem. It provides consistent results besides holding out a profound potential and promise for building a novel type of nonlinear model consisting of several local linear models.

Algorithms↗
PubMed1996

Time-varying characteristics of visually induced postural sway.

To study potential time-varying dynamics of postural sway as measured via center-of-pressure (COP) under the feet, we applied time-frequency analysis to COP data from ten vestibularly impaired subjects and 13 nonimpaired controls, during quiet stance and in response to visual perturbation. This analysis revealed that 1) the spectral characteristics of COP change over time; 2) there are time-dependent and frequency-dependent differences in COP between impaired and nonimpaired populations during visual perturbation, and 3) there is no difference in COP during quiet stance (eyes opne) between impaired and nonimpaired populations for the parameters investigated. A novel finding of this research is that controls appear to adapt to constant frequency visual perturbation, while vestibularly impaired subjects do not. This difference could not have been observed with conventional Fourier analysis, which is commonly used in COP data analysis, because time is not a parameter of the spectrum and adaptation is, by nature, a time-varying process. These results suggest that time-frequency analysis of COP data is useful for studying temporal dynamics of postural control, and in particular the differences between vestibularly impaired subjects and healthy controls during visual perturbation.

Adult↗
PubMed1989

Three stages and two systems of visual processing.

Three stages of visual processing determine how internal noise appears to an external observer: light adaptation, contrast gain control and a postsensory/decision stage. Dark noise occurs prior to adaptation, determines dark-adapted absolute thresholds and mimics stationary external noise. Sensory noise occurs after dark adaptation, determines contrast thresholds for sine gratings and similar stimuli, and mimics external noise that increases with mean luminance. Postsensory noise incorporates perceptual, decision and mnemonic processes. It occurs after contrast-gain control and mimics external noise that increases with stimulus contrast (i.e., multiplicative noise). Dark noise and sensory noise are frequency specific and primarily affect weak signals. Only postsensory noise significantly affects the discriminability of strong signals masked by stimulus noise; postsensory noise has constant power over a wide spatial frequency range in which sensory noise varies enormously. Two parallel perceptual regimes jointly serve human object recognition and motion perception: a first-order linear (Fourier) regime that computes relations directly from stimulus luminance, and a second-order nonlinear (nonFourier) rectifying regime that uses the absolute value (or power) of stimulus contrast. When objects or movements are defined by high spatial frequencies (i.e., texture carrier frequencies whose wavelengths are small compared to the object size), the responses of high-frequency receptors are demodulated by rectification to facilitate discrimination at the higher processing levels. Rectification sacrifices the statistical efficiency (noise resistance) of the first-order regime for efficiency of neural connectivity and computation.

Adaptation, Ocular↗