Detecting determinism in short time series, with an application to the analysis of a stationary EEG recording

We have developed a new method for detecting determinism in a short time series and used this method to examine whether a stationary EEG is deterministic or stochastic. The method is based on the observation that the trajectory of a time series generated from a differentiable dynamical system behaves smoothly in an embedded phase space. The angles between two successive directional vectors in the trajectory reconstructed from a time series at a minimum embedding dimension were calculated as a function of time. We measured the irregularity of the angle variations obtained from the time series using second-order difference plots and central tendency measures, and compared these values with those from surrogate data. The ability of the proposed method to distinguish between chaotic and stochastic dynamics is demonstrated through a number of simulated time series, including data from Lorenz, R?ssler, and Van der Pol attractors, high-dimensional equations, and 1/f noise. We then applied this method to the analysis of stationary segments of EEG recordings consisting of 750 data points (6-s segments) from five normal subjects. The stationary EEG segments were not found to exhibit deterministic components. This method can be used to analyze determinism in short time series, such as those from physiological recordings, that can be modeled using differentiable dynamical processes.     

Phase plane description of endogenous neuronal oscillators in Aplysia

Phase plane techniques are used to describe graphically the limit cycle behavior of identified endogenous neuronal oscillators in the isolated abdominal ganglion of Aplysia. Intracellularly recorded membrane potential from a bursting neuron and its first derivative with respect to time are used as coordinates (state variables) in phase space. The derivative is either measured electronically or calculated digitally. Each trajectory in phase space represents the entire output of the bursting neuron, i.e., both the rapid action potentials and slow pacemaker potentials. Phase plane portraits are presented for the free run limit cycle before and after a change in a system parameter (applied transmembrane current) and also for phase resetting produced by direct synaptic inhibition from an identified interneuron. The complex topology of the trajectory suggests that the bursting oscillator is a higher order system. Therefore, the second time derivative is used as another state variable. This type of phase plot can help to relate biophysical and mathematical analyses.     

Modeling experimental time series with ordinary differential equations

Recently some methods have been presented to extract ordinary differential equations (ODE) directly from an experimental time series. Here, we introduce a new method to find an ODE which models both the short time and the long time dynamics. The experimental data are represented in a state space and the corresponding flow vectors are approximated by polynomials of the state vector components. We apply these methods both to simulated data and experimental data from human limb movements, which like many other biological systems can exhibit limit cycle dynamics. In systems with only one oscillator there is excellent agreement between the limit cycling displayed by the experimental system and the reconstructed model, even if the data are very noisy. Furthermore, we study systems of two coupled limit cycle oscillators. There, a reconstruction was only successful for data with a sufficiently long transient trajectory and relatively low noise level.     

Face-infringement space: the frame of reference of the ventral intraparietal area

Experimental studies have shown that responses of ventral intraparietal area (VIP) neurons specialize in head movements and the environment near the head. VIP neurons respond to visual, auditory, and tactile stimuli, smooth pursuit eye movements, and passive and active movements of the head. This study demonstrates mathematical structure on a higher organizational level created within VIP by the integration of a complete set of variables covering face-infringement. Rather than positing dynamics in an a priori defined coordinate system such as those of physical space, we assemble neuronal receptive fields to find out what space of variables VIP neurons together cover. Section 1 presents a view of neurons as multidimensional mathematical objects. Each VIP neuron occupies or is responsive to a region in a sensorimotor phase space, thus unifying variables relevant to the disparate sensory modalities and movements. Convergence on one neuron joins variables functionally, as space and time are joined in relativistic physics to form a unified spacetime. The space of position and motion together forms a neuronal phase space, bridging neurophysiology and the physics of face-infringement. After a brief review of the experimental literature, the neuronal phase space natural to VIP is sequentially characterized, based on experimental data. Responses of neurons indicate variables that may serve as axes of neural reference frames, and neuronal responses have been so used in this study. The space of sensory and movement variables covered by VIP receptive fields joins visual and auditory space to body-bound sensory modalities: somatosensation and the inertial senses. This joining of allocentric and egocentric modalities is in keeping with the known relationship of the parietal lobe to the sense of self in space and to hemineglect, in both humans and monkeys. Following this inductive step, variables are formalized in terms of the mathematics of graph theory to deduce which combinations are complete as a multidimensional neural structure that provides the organism with a complete set of options regarding objects impacting the face, such as acceptance, pursuit, and avoidance. We consider four basic variable types: position and motion of the face and of an external object. Formalizing the four types of variables allows us to generalize to any sensory system and to determine the necessary and sufficient conditions for a neural center (for example, a cortical region) to provide a face-infringement space. We demonstrate that VIP includes at least one such face-infringement space.     

The Isochronal Fibration: Characterization and Implication in Biology

Limit cycles, because they are constituted of a periodic succession of states (discrete or continuous) constitute a good manner to store information. From any points of the state space reached after a perturbation or stimulation of the cognitive system storing this information, one can aim to join through a more or less long return trajectory a precise neighbourhood of the asymptotic trajectory at a specific moment (or a specific place) on the limit cycle, i.e. where the information of interest stands. We propose that the isochronal fibration, initially imagined and described by A. T. Winfree may be an excellent way to connect directly those two locations. Each isochron is indeed the set of points in temporal phase with one single point of the attractor. The characterisation of the isochronal fibration of various dynamical systems is not easy and until now has principally only been done numerically but not analytically. By integrating the homogeneous solutions of the dynamical system we can solve this fibration in the case of the well known anharmonic pendulum. Other isochronal fibration on classical examples such as the van der Pol system and the non-symmetrical PFK limit cycle are obtained numerically and we also provide the first numerical study on 3-dimentional systems like the anharmonic pendulum with a linear relaxation on its third variable and the Lorenz attractor. The empirical approach seems us useful for dealing with the isochronal fibration which could constitute a powerful tool for understanding and controlling the dynamics of biological or biological-inspired systems.     

Trajectory analysis of Chinese vegetation types in a multidimensional climatic space

Abstract. The concept of trajectory of a vegetation type is used as a tool to study the position of vegetation types in a multidimensional climatic space. This space was obtained by Principal Component Analysis based on climatic data - based on monthly means. The positions of the main vegetation types distinguished in China were plotted in the climatic space and trajectory lengths and shapes of the types were compared. Three main groups were found, which correspond to: (1) cold and temperate, (2) alpine and mountain, and (3) subtropical and tropical conditions. Within each main category sub-categories were distinguished on the basis of trajectory length and direction. In total, seven trajectory shapes were defined. Based on the correlation between climatic variables and PCA-axes temperature is suggested to be the overriding factor in determining the trajectory shape. But, trajectories can also be affected by the combination of other climatic variables.     

Reduction of a channel-based model for a stomatogastric ganglion LP neuron

Buchholtz et al. constructed a detailed conductance-based model of the LP cell of the stomatogastric ganglion of crustacea based upon the experimental work of Golowasch. Their model incorporated seven ionic currents and had 13 dynamical variables. We have produced a simplification of this model that has a seven-dimensional phase space by using the method of equivalent potentials, suggested by Abbott and Kepler, to combine several dynamical variables with similar time scales. Analysis of the dynamics of the reference and reduced model reveals similar bifurcation diagrams and similar dynamical behavior of the individual ionic currents.     

Dynamical systems analysis of arterial blood pressure signals in relation to heart rate fluctuations in chick embryos

We attempted a new approach based on a modern dynamical system theory to reconstruct the arterial blood pressure signals in relation to heart rate fluctuations of developing chick embryos. The dynamical systems approach in general is to model a phenomenon that is presented by a single time series record and approximate the dynamical property (e.g. heart rate fluctuations) of a system based only on information contained in a single-variable (arterial blood pressure) of the system. The time-series data of the arterial blood pressure was reconstructed in 3-dimensional space to draw characteristic orbits. Since the reconstructed orbits of the blood pressure should retain information contained in the pressure signals, we attempted to derive instantaneous heart rate (IHR) from the reconstructed orbits. The derived IHR presenting HR fluctuations coincided well with the IHR obtained conventionally from the peak-to-peak time intervals of the maximum blood pressure. Movements of the reconstructed orbits of the arterial blood pressure in 3-dimensional space reflected HR fluctuations (i.e. transient decelerations and accelerations).     

Nonlinearities in mating sounds of American crocodiles

We use nonlinear time series analysis methods to analyze the dynamics of the sound-producing apparatus of the American crocodile (Crocodylus acutus). We capture its dynamics by analyzing a recording of the singing activity during mating time. First, we reconstruct the phase space from the sound recording and thereby reveal that the attractor needs no less than five degrees of freedom to fully evolve in the embedding space, which suggests that a rather complex nonlinear dynamics underlies its existence. Prior to investigating the dynamics more precisely, we test whether the reconstructed attractor satisfies the notions of determinism and stationarity, as a lack of either of these properties would preclude a meaningful further analysis. After positively establishing determinism and stationarity, we proceed by showing that the maximal Lyapunov exponent of the recording is positive, which is a strong indicator for the chaotic behavior of the system, confirming that dynamical nonlinearities are an integral part of the examined sound-producing apparatus. At the end, we discuss that methods of nonlinear time series analysis could yield instructive insights and foster the understanding of vocal communication among certain reptile species.     

Automatic identification of discrete substates in proteins: Singular value decomposition analysis of time-averaged crystallographic refinements

The singular value decomposition (SVD) provides a method for decomposing a molecular dynamics trajectory into fundamental modes of atomic motion. The right singular vectors are projections of the protein conformations onto these modes showing the protein motion in a generalized low-dimensional basis. Statistical analysis of the right singular vectors can be used to classify discrete configurational substates in the protein. The configuration space portraits formed from the right singular vectors can also be used to visualize complex high-dimensional motion and to examine the extent of configuration space sampling by the simulation. © 1995 Wiley-Liss, Inc.     

Novel tracking function of moving target using chaotic dynamics in a recurrent neural network model

Chaotic dynamics introduced in a recurrent neural network model is applied to controlling an object to track a moving target in two-dimensional space, which is set as an ill-posed problem. The motion increments of the object are determined by a group of motion functions calculated in real time with firing states of the neurons in the network. Several cyclic memory attractors that correspond to several simple motions of the object in two-dimensional space are embedded. Chaotic dynamics introduced in the network causes corresponding complex motions of the object in two-dimensional space. Adaptively real-time switching of control parameter results in constrained chaos (chaotic itinerancy) in the state space of the network and enables the object to track a moving target along a certain trajectory successfully. The performance of tracking is evaluated by calculating the success rate over 100 trials with respect to nine kinds of trajectories along which the target moves respectively. Computer experiments show that chaotic dynamics is useful to track a moving target. To understand the relations between these cases and chaotic dynamics, dynamical structure of chaotic dynamics is investigated from dynamical viewpoint.     

Symmetry Breaking in a Model of Antigenic Variation with Immune Delay

Effects of immune delay on symmetric dynamics are investigated within a model of antigenic variation in malaria. Using isotypic decomposition of the phase space, stability problem is reduced to the analysis of a cubic transcendental equation for the eigenvalues. This allows one to identify periodic solutions with different symmetries arising at a Hopf bifurcation. In the case of small immune delay, the boundary of the Hopf bifurcation is found in a closed form in terms of system parameters. For arbitrary values of the time delay, general expressions for the critical time delay are found, which indicate bifurcation to an odd or even periodic solution. Numerical simulations of the full system are performed to illustrate different types of dynamical behaviour. The results of this analysis are quite generic and can be used to study within-host dynamics of many infectious diseases.     

Integrative biology: linking levels of organization

Biological systems are composed of different levels of organization. Usually, one considers the atomic, molecular, cellular, individual, population, community and ecosystem levels. These levels of organization also correspond to different levels of observation of the system, from microscopic to macroscopic, i.e., to different time and space scales. The more microscopic the level is, the faster the time scale and the smaller the space scale are. The dynamics of the complete system is the result of the coupled dynamical processes that take place in each of its levels of organization at different time scales. Variables aggregation methods take advantage of these different time scales to reduce the dimension of mathematical models such as a system of ordinary differential equations. We are going to study the dynamics of a system which is hierarchically organized in the sense that it is composed of groups of elements that can be themselves divided into further smaller sub-groups and so on. The hierarchical structure of the system results from the fact that the intra-group interactions are assumed to be larger than inter-group ones. We present aggregation methods that allow one to build a reduced model that governs a few global variables at the slow time scale.     

Conformational states and folding pathways of peptides revealed by principal-independent component analyses

Principal component analysis is a powerful method for projecting multidimensional conformational space of peptides or proteins onto lower dimensional subspaces in which the main conformations are present, making it easier to reveal the structures of molecules from e.g. molecular dynamics simulation trajectories. However, the identification of all conformational states is still difficult if the subspaces consist of more than two dimensions. This is mainly due to the fact that the principal components are not independent with each other, and states in the subspaces cannot be visualized. In this work, we propose a simple and fast scheme that allows one to obtain all conformational states in the subspaces. The basic idea is that instead of directly identifying the states in the subspace spanned by principal components, we first transform this subspace into another subspace formed by components that are independent of one other. These independent components are obtained from the principal components by employing the independent component analysis method. Because of independence between components, all states in this new subspace are defined as all possible combinations of the states obtained from each single independent component. This makes the conformational analysis much simpler. We test the performance of the method by analyzing the conformations of the glycine tripeptide and the alanine hexapeptide. The analyses show that our method is simple and quickly reveal all conformational states in the subspaces. The folding pathways between the identified states of the alanine hexapeptide are analyzed and discussed in some detail.     

Comparing multiple correspondence and principal component analyses with biomechanical signals. Example with turning the steering wheel

The purpose of this article is to compare Principal Component Analysis (PCA) and a much less used method, i.e. MCA (Multiple Correspondence Analysis) with data being first changed into membership values to fuzzy space windows. For such a comparison, data from an experimental study about turning the steering wheel is used. In a didactic perspective, this article only considers one multidimensional signal with 5 components: 3 linked to the steering wheel angle and hand positions and 2 to hand effort variables. A discussion weighs out the pros and the cons of both methods with criteria such as the possibility to show complex relational phenomena, the analysis/computing time or the information loss inherent to the averaging stage (in the perspective to analyze several hundreds of large multidimensional signals).     

Application of the correlation integral to respiratory data of infants during REM sleep

 Non-linear time sequence analysis has been performed on infant sleep measurement data in order to obtain more information about the respiratory processes. As a first step, respiration data during REM sleep were analysed with methods from non-linear dynamics, especially, the correlation integral and the slope of its log-log plot, representing the correlation dimension. Before calculation of the correlation integral, a special kind of filtering has to be applied to the data. This filtering algorithm is a state space and singular value decomposition-based noise reduction method, and it is used to separate the noise and signal subspaces. The dynamics of a signal (in our case data from the respiratory process) and its degrees of freedom can be characterised by the correlation integral and by the correlation dimension, respectively. The main result of this study is that the highly irregular-looking breathing patterns during REM sleep could be described by a deterministic system, and finally the physiological significance of this finding is discussed. Received: 17 June 1994/Accepted in revised form: 18 November 1994     

Tracking a single pigeon using a shadowing filter algorithm

Miniature GPS devices now allow for measurement of the movement of animals in real time and provide high‐ quality and high‐resolution data. While these new data sets are a great improvement, one still encounters some measurement errors as well as device failures. Moreover, these devices only measure position and require further reconstruction techniques to extract the full dynamical state space with the velocity and acceleration. Direct differentiation of position is generally not adequate. We report on the successful implementation of a shadowing filter algorithm that (1) minimizes measurement errors and (2) reconstructs at the same time the full phase‐space from a position recording of a flying pigeon. This filter is based on a very simple assumption that the pigeon's dynamics are Newtonian. We explore not only how to choose the filter's parameters but also demonstrate its improvements over other techniques and give minimum data requirements. In contrast to competing filters, the shadowing filter's approach has not been widely implemented for practical problems. This article addresses these practicalities and provides a prototype for such application.     

Hybrid Monte Carlo simulations theory and initial comparison with molecular dynamics

A formal partially dynamical approach to ergodic sampling, hybrid Monte Carlo, has been adapted for the first time from its proven application in quantum chromodynamics to realistic molecular systems. A series of simulations of pancreatic trypsin inhibitor were run using temperature-rescaled molecular dynamics and hybrid Monte Carlo. It was found that simulations run using hybrid Monte Carlo equilibrated an order of magnitude faster than those run using temperature-rescaled molecular dynamics. Certain aspects of improved performance obtained using hybrid Monte Carlo are probably due to the increased efficiency with which this algorithm explores phase space. To discuss this we introduce the notion of “trajectory stiffness”. © 1993 John Wiley & Sons, Inc.     

Control of computational dynamics of coupled integrate-and-fire neurons

 Generation and control of different dynamical modes of computational processes in a net of interconnected integrate-and-fire neurons are demonstrated. A net architecture resembling a generic cortical structure is formed from pairs of excitatory and inhibitory units with excitatory connections between and inhibitory connections within pairs. Integrate-and-fire model neurons derived from detailed conductance-based models of neocortical pyramidal cells and fast-spiking interneurons are employed for the excitatory and inhibitory units, respectively. Firing-rate adaptation is incorporated into the excitatory units based on the regulation of the slow afterhyperpolarization phase of action potentials by intracellular calcium ions. Saturation of synaptic conductances is implemented for the interconnections between units. It is shown that neuronal adaptation of the excitatory units can generate richer net dynamics than relaxation to fixed-point attractors in a pattern space. At strong adaptivity, i.e. when the neuronal excitability is strongly influenced by the preceding activity, complex dynamics of either aperiodic or limit-cycle character are generated in both the pattern space and the phase space of all dynamical variables. This regime corresponds to an exploratory mode of the system, in which the pattern space can be searched. At weak adaptivity, the dynamics are governed by fixed-point attractors in the pattern space, and this corresponds to a mode for retrieval of a particular pattern. In the brain, neuronal adaptivity can be regulated by various neuromodulators. The results are in accordance with those recently obtained by means of more abstract models formulated in terms of mean firing rates. The increased realism makes the present model reveal more detailed mechanisms and strengthens the relevance of the conclusions to biological systems. The simplicity and realism of the coupled integrate-and-fire neurons make the present model useful for studies of systems in which the temporal aspects of neural coding are important. Received: 8 December 1995 / Accepted in revised form: 23 January 1997     

Viewing tropical forest succession as a three-dimensional dynamical system

As tropical forests are complex systems, they tend to be modelled either roughly via scaling relationships or in a detailed manner as high-dimensional systems with many variables. We propose an approach which lies between the two whereby succession in a tropical forest is viewed as a trajectory in the configuration space of a dynamical system with just three dependent variables, namely, the mean leaf-area index (LAI) and its standard deviation (SD) or coefficient of variation along a transect, and the mean diameter at breast height (DBH) of trees above the 90th percentile of the distribution of tree DBHs near the transect. Four stages in this forest succession are identified: (I) naturally afforesting grassland: the initial stage with scattered trees in grassland; (II) very young forest: mostly covered by trees with a few remaining gaps; (III) young smooth forest: almost complete cover by trees of mostly similar age resulting in a low SD; and (IV) old growth or mature forest: the attracting region in configuration space characterized by fluctuating SD from tree deaths and regrowth. High-resolution LAI measurements and other field data from Khao Yai National Park, Thailand show how the system passes through these stages in configuration space, as do simple considerations and a crude cellular automaton model.