Coordination dynamics of trajectory formation

 The present study aims to understand the neurally based coordination dynamics (multistability, loss of stability, transitions, etc.) of trajectory formation in a simple task. Six subjects produced two spatial patterns of coordination in the xy plane by alternating the abduction-adduction and flexion-extension motions of their right index finger. Each pattern was characterized by a unique temporal ratio between the x and y directions of motion: (1) a figure zero, a 1:1 temporal pattern; and (2) a figure eight, a 2:1 temporal pattern. The patterns were produced rhythmically and movement frequency was scaled across ten frequency plateaus, with ten cycles of motion per step. As movement frequency increased, switching from a figure eight to a figure zero was observed at critical cycling frequencies. The switch from pattern (2) to pattern (1) was identified in the spatial trajectory and power spectra of x(t) and y(t). En route to the transition, enhancement of fluctuations was observed in the Fourier amplitudes of x(t) and y(t), specifically at f ₀ (the metronome frequency) and 2f ₀ (the first harmonic of f ₀). Interestingly, there was no difference in the spatial variability of the two patterns. Overall, the data demonstrate that spatial patterns of coordination can be characterized in terms of the temporal relationship between the spatial components of the trajectory itself. We discuss the experimental findings in relation to other end-point planning and multijoint control strategies, as well as the much more general problem of temporal synchronization in many interlimb and intralimb coordination tasks. Received: 24 February 1995/Accepted in revised form: 8 August 1995     

Coordination dynamics of trajectory formation

The present study aims to understand the neurally based coordination dynamics (multistability, loss of stability, transitions, etc.) of trajectory formation in a simple task. Six subjects produced two spatial patterns of coordination in the xy plane by alternating the abduction-adduction and flexion-extension motions of their right index finger. Each pattern was characterized by a unique temporal ratio between the x and y directions of motion: (1) a figure zero, a 1∶1 temporal pattern; and (2) a figure eight, a 2∶1 temporal pattern. The patterns were produced rhythmically and movement frequency was scaled across ten frequency plateaus, with ten cycles of motion per step. As movement frequency increased, switching from a figure eight to a figure zero was observed at critical cycling frequencies. The switch from pattern (2) to pattern (1) was identified in the spatial trajectory and power spectra of x(t) and y(t). En route to the transition, enhancement of fluctuations was observed in the Fourier amplitudes of x(t) and y(t), specifically at f ₀ (the metronome frequency) and 2f ₀ (the first harmonic off ₀). Interestingly, there was no difference in the spatial variability of the two patterns. Overall, the data demonstrate that spatial patterns of coordination can be characterized in terms of the temporal relationship between the spatial components of the trajectory itself. We discuss the experimental findings in relation to other end-point planning and multijoint control strategies, as well as the much more general problem of temporal synchronization in many interlimb and intralimb coordination tasks.     

A neural network model for limb trajectory formation

This paper deals with the problem of representing and generating unconstrained aiming movements of a limb by means of a neural network architecture. The network produced time trajectories of a limb from a starting posture toward targets specified by sensory stimuli. Thus the network performed a sensory-motor transformation. The experimenters trained the network using a bell-shaped velocity profile on the trajectories. This type of profile is characteristic of most movements performed by biological systems. We investigated the generalization capabilities of the network as well as its internal organization. Experiments performed during learning and on the trained network showed that: (i) the task could be learned by a three-layer sequential network; (ii) the network successfully generalized in trajectory space and adjusted the velocity profiles properly; (iii) the same task could not be learned by a linear network; (iv) after learning, the internal connections became organized into inhibitory and excitatory zones and encoded the main features of the training set; (v) the model was robust to noise on the input signals; (vi) the network exhibited attractor-dynamics properties; (vii) the network was able to solve the motorequivalence problem. A key feature of this work is the fact that the neural network was coupled to a mechanical model of a limb in which muscles are represented as springs. With this representation the model solved the problem of motor redundancy.     

A biologically inspired neural net for trajectory formation and obstacle avoidance

In this paper we present a biologically inspired two-layered neural network for trajectory formation and obstacle avoidance. The two topographically ordered neural maps consist of analog neurons having continuous dynamics. The first layer, the sensory map, receives sensory information and builds up an activity pattern which contains the optimal solution (i.e. shortest path without collisions) for any given set of current position, target positions and obstacle positions. Targets and obstacles are allowed to move, in which case the activity pattern in the sensory map will change accordingly. The time evolution of the neural activity in the second layer, the motor map, results in a moving cluster of activity, which can be interpreted as a population vector. Through the feedforward connections between the two layers, input of the sensory map directs the movement of the cluster along the optimal path from the current position of the cluster to the target position. The smooth trajectory is the result of the intrinsic dynamics of the network only. No supervisor is required. The output of the motor map can be used for direct control of an autonomous system in a cluttered environment or for control of the actuators of a biological limb or robot manipulator. The system is able to reach a target even in the presence of an external perturbation. Computer simulations of a point robot and a multi-joint manipulator illustrate the theory.     

Self-organization at the origin of life

The concept of an (M,R) system with organizational invariance allows one to understand how a system may be able to maintain itself indefinitely if it is coupled to an external source of energy and materials. However, although this constitutes an important step towards understanding the difference between a living and a non-living system, it is not clear that an (M,R) system with organizational invariance is sufficient to define a living system. To take a further step towards defining what it means to be alive it is necessary to add to a simple (M,R) system some property that represents its identity, and which can be maintained and modified in subsequent generations.     

Self-organization of an oscillatory neural system

Hebbian dynamics is used to derive the differential equations for the synaptic strengths in the neural circuitry of the locomotive oscillator. Initially, neural connection are random. Under a specified arborization hypothesis relating to the density of neural connections, the differential equations are shown to model the self-organization and the stability of the oscillator.     

Self-organization phenomena resulting from cell-cell contact

Self-organization phenomena in an ensemble of cells in contact is considered. By a simple and general argument it is shown that in order to have pattern formation or spatio-temporal organization in these systems it is not necessary that chemicals move throughout the field. The influence of one cell on its neighbour can be transmitted by surface contact interactions in which molecules in the surface of one cell influence the rates of reactions in neighbouring cells without the actual passage of molecules from one cell to the other. It is shown that these systems present all the self-organizing properties seen in reaction diffusion systems. The possible biological applications and the role of first bifurcating solution out of the homogeneous state to provide “positional information” in morphogenesis is discussed.     

Self-organization of muscle cell structure and function

The organization of muscle is the product of functional adaptation over several length scales spanning from the sarcomere to the muscle bundle. One possible strategy for solving this multiscale coupling problem is to physically constrain the muscle cells in microenvironments that potentiate the organization of their intracellular space. We hypothesized that boundary conditions in the extracellular space potentiate the organization of cytoskeletal scaffolds for directed sarcomeregenesis. We developed a quantitative model of how the cytoskeleton of neonatal rat ventricular myocytes organizes with respect to geometric cues in the extracellular matrix. Numerical results and in vitro assays to control myocyte shape indicated that distinct cytoskeletal architectures arise from two temporally-ordered, organizational processes: the interaction between actin fibers, premyofibrils and focal adhesions, as well as cooperative alignment and parallel bundling of nascent myofibrils. Our results suggest that a hierarchy of mechanisms regulate the self-organization of the contractile cytoskeleton and that a positive feedback loop is responsible for initiating the break in symmetry, potentiated by extracellular boundary conditions, is required to polarize the contractile cytoskeleton.     

Self-organization of mobile populations in cyclic competition

The formation of out-of-equilibrium patterns is a characteristic feature of spatially extended, biodiverse, ecological systems. Intriguing examples are provided by cyclic competition of species, as metaphorically described by the ‘rock-paper-scissors’ game. Both experimentally and theoretically, such non-transitive interactions have been found to induce self-organization of static individuals into noisy, irregular clusters. However, a profound understanding and characterization of such patterns is still lacking. Here, we theoretically investigate the influence of individuals’ mobility on the spatial structures emerging in rock-paper-scissors games. We devise a quantitative approach to analyze the spatial patterns self-forming in the course of the stochastic time evolution. For a paradigmatic model originally introduced by May and Leonard, within an interacting particle approach, we demonstrate that the system's behavior—in the proper continuum limit—is aptly captured by a set of stochastic partial differential equations. The system's stochastic dynamics is shown to lead to the emergence of entangled rotating spiral waves. While the spirals’ wavelength and spreading velocity is demonstrated to be accurately predicted by a (deterministic) complex Ginzburg-Landau equation, their entanglement results from the inherent stochastic nature of the system. These findings and our methods have important applications for understanding the formation of noisy patterns, e.g. in ecological and evolutionary contexts, and are also of relevance for the kinetics of (bio)-chemical reactions.     

Self-organization in and on biological spheres

Three biological settings involving self-organization performed by the Turing-Child field inside a sphere and on its surface are considered. In the first setting the interior of a sphere made up of cells communicating via gap junctions is considered. It is suggested that the Turing-Child self-organization is the cause of radial polarization, the first differentiation of an early mammalian embryo. In the second setting, the Turing example of gastrulation of a hollow cellular sphere is considered. It is shown that Child's experimental patterns are predicted and explained by the Turing-Child theory. The third setting is the interior of a biological cell, and it is suggested that it is the self-organization of the Turing-Child field that causes the formation of the mitotic spindle.     

Self-organization in and on biological spheres

Three biological settings involving self-organization performed by the Turing-Child field inside a sphere and on its surface are considered. In the first setting the interior of a sphere made up of cells communicating via gap junctions is considered. It is suggested that the Turing-Child self-organization is the cause of radial polarization, the first differentiation of an early mammalian embryo. In the second setting, the Turing example of gastrulation of a hollow cellular sphere is considered. It is shown that Child's experimental patterns are predicted and explained by the Turing-Child theory. The third setting is the interior of a biological cell, and it is suggested that it is the self-organization of the Turing-Child field that causes the formation of the mitotic spindle.     

Self-organization of neurons described by the maximum-entropy principle

In the article the maximum-entropy principle and Parzen windows are applied to derive an optimal mapping of a continuous into a descrete random variable. The mapping can be performed by a network of self-organizing information processing units similar to biological neurons. Each neuron is selectively sensitized to one prototype from the sample space of the discrete random variable. The continuous random variable is applied as the input signal exciting the neurons. The response of the network is described by the excitation vector which represents the encoded input signal. Due to the interaction between neurons adaptive changes of prototypes are caused by the excitations. The derived mathematical model explains this interaction in detail; a simplified self-organization rule derived from it corresponds to that of Kohonen. One and two-dimensional examples of self-organization simulated on a computer are shown in the article.     

Self-organization of a chromatin fibril into topologically-associated domains

A number of recent experimental approaches demonstrated that a three-dimensional organization of the eukaryotic genome play an important role in the regulation of its activity. One of the most important results was a discovery of the genome separation into relatively independent topologically-associated domains (TADs). They restricted the action area of regulatory elements, i.e., they simultaneously were regulatory domains of the genome. In this connection, an understanding of the molecular mechanism of the TAD formation has become a very topical problem. Here, we review and discuss our recent data which demonstrated that the TAD formation was directed by simple physical laws and was based on establishing multiple internucleosomal contacts.     

Self-organization of dust grains in proton beam plasma

The results of investigations of dust grain behavior in plasma formed by a proton beam in inert gases (He, Ar, Kr) are demonstrated. Stable ordered dust structures, namely “a plasma-dust crystal” formed of dust grains 1.0, 3.0, and 4.8 μm in diameter are obtained in the proton beam range for the first time. The mathematical model which allows for numerical simulation of crystal formation from dust grains formed by proton beam plasma is developed.     

Self-organization of polymer-motor systems in the cytoskeleton.

Microtubules and actin filaments are organized into dynamic arrays inside cells. In this paper I discuss in conceptual form the assembly mechanisms of three specific arrays: asters, spindles and leading edge structures. The role of energy transducing processes, particularly motor protein activity, in assembly is explored. I conclude that dynamic interaction between motor proteins and cytoskeletal polymers play a very general role in spatial organization of the cytoplasm.