Quantifying chaos: practical estimation of the correlation dimension

Be it a physical object or a mathematical model, a nonlinear dynamical system can display complicated aperiodic behavior, or "chaos." In many cases, this chaos is associated with motion on a strange attractor in the system's phase space. And the dimension of the strange attractor indicates the effective number of degrees of freedom in the dynamical system.

In this thesis, we investigate numerical issues involved with estimating the dimension of a strange attractor from a finite time series of measurements on the dynamical system.

Of the various definitions of dimension, we argue that the correlation dimension is the most efficiently calculable and we remark further that it is the most commonly calculated. We are concerned with the practical problems that arise in attempting to compute the correlation dimension. We deal with geometrical effects (due to the inexact self-similarity of the attractor), dynamical effects (due to the nonindependence of points generated by the dynamical system that defines the attractor), and statistical effects (due to the finite number of points that sample the attractor). We propose a modification of the standard algorithm, which eliminates a specific effect due to autocorrelation, and a new implementation of the correlation algorithm, which is computationally efficient.

Finally, we apply the algorithm to chaotic data from the Caltech tokamak and the Texas tokamak (TEXT); we conclude that plasma turbulence is not a low- dimensional phenomenon.

Expression of eph-family receptor tyrosine kinases and ephrins in the tadpole of the frog Xenopus laevis, and possible roles in the development of retinotectal topography

Assembling a nervous system requires exquisite specificity in the construction of neuronal connectivity. One method by which such specificity is implemented is the presence of chemical cues within the tissues, differentiating one region from another, and the presence of receptors for those cues on the surface of neurons and their axons that are navigating within this cellular environment.

Connections from one part of the nervous system to another often take the form of a topographic mapping. One widely studied model system that involves such a mapping is the vertebrate retinotectal projection-the set of connections between the eye and the optic tectum of the midbrain, which is the primary visual center in non-mammals and is homologous to the superior colliculus in mammals. In this projection the two-dimensional surface of the retina is mapped smoothly onto the two-dimensional surface of the tectum, such that light from neighboring points in visual space excites neighboring cells in the brain. This mapping is implemented at least in part via differential chemical cues in different regions of the tectum.

The Eph family of receptor tyrosine kinases and their cell-surface ligands, the ephrins, have been implicated in a wide variety of processes, generally involving cellular movement in response to extracellular cues. In particular, they possess expression patterns-i.e., complementary gradients of receptor in retina and ligand in tectum- and in vitro and in vivo activities and phenotypes-i.e., repulsive guidance of axons and defective mapping in mutants, respectively-consistent with the long-sought retinotectal chemical mapping cues.

The tadpole of Xenopus laevis, the South African clawed frog, is advantageous for in vivo retinotectal studies because of its transparency and manipulability. However, neither the expression patterns nor the retinotectal roles of these proteins have been well characterized in this system. We report here comprehensive descriptions in swimming stage tadpoles of the messenger RNA expression patterns of eleven known Xenopus Eph and ephrin genes, including xephrin-A3, which is novel, and xEphB2, whose expression pattern has not previously been published in detail. We also report the results of in vivo protein injection perturbation studies on Xenopus retinotectal topography, which were negative, and of in vitro axonal guidance assays, which suggest a previously unrecognized attractive activity of ephrins at low concentrations on retinal ganglion cell axons. This raises the possibility that these axons find their correct targets in part by seeking out a preferred concentration of ligands appropriate to their individual receptor expression levels, rather than by being repelled to greater or lesser degrees by the ephrins but attracted by some as-yet-unknown cue(s).

A generalized Hausdorff dimension for functions and sets

Let E be a compact subset of the n-dimensional unit cube, 1_n, and let C be a collection of convex bodies, all of positive n-dimensional Lebesgue measure, such that C contains bodies with arbitrarily small measure. The dimension of E with respect to the covering class C is defined to be the number

d_C(E) = sup(β:H_{β, C}(E) > 0),

where H_{β, C} is the outer measure

inf(Ʃm(C_i)^β:UC_i Ↄ E, C_i ϵ C) .

Only the one and two-dimensional cases are studied. Moreover, the covering classes considered are those consisting of intervals and rectangles, parallel to the coordinate axes, and those closed under translations. A covering class is identified with a set of points in the left-open portion, 1’_n, of 1_n, whose closure intersects 1_n - 1’_n. For n = 2, the outer measure H_{β, C} is adopted in place of the usual:

Inf(Ʃ(diam. (C_i))^β: UC_i Ↄ E, C_i ϵ C),

for the purpose of studying the influence of the shape of the covering sets on the dimension d_C(E).

If E is a closed set in 1₁, let M(E) be the class of all non-decreasing functions μ(x), supported on E with μ(x) = 0, x ≤ 0 and μ(x) = 1, x ≥ 1. Define for each μ ϵ M(E),

d_C(μ) = lim/c → inf/0 log ∆μ(c)/log c , (c ϵ C)

where ∆μ(c) = v/x (μ(x+c) – μ(x)). It is shown that

d_C(E) = sup (d_C(μ):μ ϵ M(E)).

This notion of dimension is extended to a certain class Ӻ of sub-additive functions, and the problem of studying the behavior of d_C(E) as a function of the covering class C is reduced to the study of d_C(f) where f ϵ Ӻ. Specifically, the set of points in 1₁,

(*) {d_B(F), d_C(f)): f ϵ Ӻ}

is characterized by a comparison of the relative positions of the points of B and C. A region of the form (*) is always closed and doubly-starred with respect to the points (0, 0) and (1, 1). Conversely, given any closed region in 1₂, doubly-starred with respect to (0, 0) and (1, 1), there are covering classes B and C such that (*) is exactly that region. All of the results are shown to apply to the dimension of closed sets E. Similar results can be obtained when a finite number of covering classes are considered.

In two dimensions, the notion of dimension is extended to the class M, of functions f(x, y), non-decreasing in x and y, supported on 1₂ with f(x, y) = 0 for x · y = 0 and f(1, 1) = 1, by the formula

d_C(f) = lim/s · t → inf/0 log ∆f(s, t)/log s · t , (s, t) ϵ C

where

∆f(s, t) = V/x, y (f(x+s, y+t) – f(x+s, y) – f(x, y+t) + f(x, t)).

A characterization of the equivalence d_C1(f) = d_C2(f) for all f ϵ M, is given by comparison of the gaps in the sets of products s · t and quotients s/t, (s, t) ϵ C_i (I = 1, 2).