DOCSLIB.ORG

Optimal Prediction with Resource Constraints Using the Information Bottleneck

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Optimal Prediction with Resource Constraints Using the Information Bottleneck bioRxiv preprint doi: https://doi.org/10.1101/2020.04.29.069179; this version posted May 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Optimal prediction with resource constraints using the information bottleneck Vedant Sachdeva1, Thierry Mora2, Aleksandra M. Walczak3, Stephanie Palmer4 1 Graduate Progam in Biophysical Sciences, University of Chicago, Chicago, IL, 2Laboratoire de physique statistique, CNRS, Sorbonne Universit´e,Universit´eParis-Diderot, and Ecole´ Normale Sup´erieure (PSL University), 24, rue Lhomond, 75005 Paris, France, 3 Laboratoire de physique th´eorique,CNRS, Sorbonne Universit´e, and Ecole´ Normale Sup´erieure (PSL University), 24, rue Lhomond, 75005 Paris, France, 4 Deparment of Organismal Biology and Anatomy, University of Chicago, Chicago, IL∗ (Dated: April 29, 2020) Responding to stimuli requires that organisms encode information about the external world. Not all parts of the signal are important for behavior, and resource limitations demand that signals be compressed. Prediction of the future input is widely beneficial in many biological systems. We compute the trade-offs between representing the past faithfully and predicting the future for input dynamics with different levels of complexity. For motion prediction, we show that, depending on the parameters in the input dynamics, velocity or position coordinates prove more predictive. We identify the properties of global, transferrable strategies for time-varying stimuli. For non-Markovian dynamics we explore the role of long-term memory of the internal representation. Lastly, we show that prediction in evolutionary population dynamics is linked to clustering allele frequencies into non-overlapping memories, revealing a very different prediction strategy from motion prediction. I. INTRODUCTION predictions might be encoded by neural and molecular systems using a variety of dynamical inputs that explore a range of temporal correlation structures. We solve the How biological systems represent external stimuli is `information bottleneck' problem in each of these scenar- critical to their behavior. The efficient coding hypoth- ios and describe the optimal encoding structure in each esis, which states that neural systems extract as much case. information as possible from the external world, given ba- sic capacity constraints, has been successful in explaining The information bottleneck framework allows us to de- some early sensory representations in neuroscience. Bar- fine a `relevance' variable in the encoded sensory stream, low suggested sensory circuits may reduce redundancy in which we take to be the future behavior of that input. the neural code and minimize metabolic costs for signal Solving the bottleneck problem allows us to optimally es- transmission [1{4]. However, not all external stimuli are timate the future state of the external stimulus, given a as important to an organism, and behavioral and environ- certain amount of information retained about the past. mental constraints need to be integrated into this picture In general, prediction of the future coordinates of a sys- to more broadly characterize biological encoding. Delays tem, Xt+∆t reduces to knowing the precise historical co- in signal transduction in biological systems mean that ordinates of the stimulus Xt and an exact knowledge of predicting external stimuli efficiently can confer benefits the temporal correlations in the system. These rules and to biological systems [5{7], making prediction a general temporal correlations can be thought of as arising from goal in biological sensing. two parts: a deterministic portion, described by a func- tion of the previous coordinates, H(Xt), and the noise Evidence that representations constructed by sensory internal to the system, ξ(t). Knowing the actual real- systems efficiently encode predictive information has ization of the noise ξ(t) reduces the prediction problem been found in the visual and olfactory systems [8{10]. to simply integrating the stochastic equations of motion Molecular networks have also been shown to be predictive forward in time. If the exact realization of the noise if of future states, suggesting prediction may be one of the not known, we can still perform a stochastic prediction underlying principles of biological computation [11, 12]. by calculating the future form of the probability distri- However, the coding capacity of biological systems is lim- bution of the variable X or its moments [15, 16]. The ited because they cannot provide arbitrarily high preci- t higher-order moments yield an estimate of Xt and the sion about their inputs: limited metabolic resources and uncertainty in the our estimate. However, biological sys- other sources of internal noise impose finite precision sig- tems cannot precisely know Xt due to inherently limited nal encoding. Given these trade-offs, one way to effi- readout precision [17, 18] and limited availability of re- ciently encode the history of an external stimulus is to sources tasked with remembering the measured statistics. keep only the information relevant for the prediction of the future input [12{14]. Here, we explore how optimal Constructing internal representations of sensory stim- uli illustrates a tension between predicting the future, for which the past must be known with higher certainty, and compression of knowledge of the past, due to finite ∗Correspondence should be addressed to [email protected] resources. We explore this intrinsic trade-off using the in- bioRxiv preprint doi: https://doi.org/10.1101/2020.04.29.069179; this version posted May 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 2 formation bottleneck (IB) approach proposed by Tishby Information et. al. [13]. This method assumes that the input variable, Bottleneck in our case the past signal Xt, can be used to make infer- ences about the relevance variable, in our case the future Build compressed representation signal Xt+∆t. By introducing a representation variable, X~, we can construct the conditional distribution of the representation variable on the input variable P(X~jXt) to be maximally informative of the output variable (Fig. 1). Formally, the representation is constructed by optimiz- FIG. 1: A schematic representation our predictive informa- ing the objective function, tion bottleneck. On the left hand side, we have coordinates Xt evolving in time, subject to noise to give Xt+∆t. We construct a representation, X~, that compresses the past input (mini- mizes I(Xt; X~)) while retaining as much information about ~ L = min I(X ; X~) − βI(X~; X ): (1) the future (maximizes I(X; Xt+∆t)) up to the weighting of t t+∆t the prediction compared to the compression set by β. P(X~ jXt) Each term is the mutual information between two vari- II. RESULTS ables: the first between the past input and estimate of the past given our representation model, X~, and the sec- A. The Stochastically Driven Damped Harmonic ond between X~ and future input. The tradeoff param- Oscillator eter, β, controls how much future information we want X~ to retain as it is maximally compressed. For large β, the representation variable must be maximally informa- tive about Xt+∆t, and will have, in general, the lowest Previous work explored the ability of the retina to con- compression. Small β means less information is retained struct an optimally predictive internal representation of about the future and high, lossy compression is allowed. a dynamic stimulus. Palmer et al [9] recorded the re- sponse of a salamander retina to a moving bar stimulus The causal relationship between the past and the fu- with SDDHO dynamics. In this case, the spike trains ture results in a data processing inequality, I(Xt; X~) ≥ in the retina encode information about the past stimuli in a near-optimally predictive way [9]. In order for op- I(Xt+∆t; X~), meaning that the information generated about the future cannot exceed the amount encoded timal prediction to be possible, the retina should encode about the past [19]. Additionally, the information the position and velocity as dictated by the information about the past that the representation can extract is bottleneck solution to the problem, for the retina's given bounded by the amount of information the uncompressed level of compression of the visual input. Inspired by this experiment, we explore the optimal predictive encoding past, itself, contains about the future, I(X~; Xt+∆t) ≤ schemes as a function of the parameters in the dynamics, I(Xt; Xt+∆t). and we describe the optimal solution across the entire pa- We use this framework to study prediction in two rameter space of the model, over a wide range of desired well-studied dynamical systems with ties to biological prediction timescales. data: the stochastically driven damped harmonic oscil- We consider the dynamics of a mass m in a viscous lator (SDDHO) and the Wright-Fisher model. We look medium attached to a spring receiving noisy velocity simultaneously at these two different systems to gain in- kicks generated by a temporally uncorrelated Gaussian tuition about how different types of dynamics influence process, as depicted in Figure 2a. Equations of motion the ability of a finite and noisy system to make accurate are introduced in terms of physical variablesx ¯,v ¯, and predictions. We further consider two types of SDDHO t¯ (bars will be dropped later when referring to rescaled processes with different noise profiles to study the effect variables), which evolve according to of noise correlations on prediction. Our exploration of the SDDHO system has a two-fold motivation: it is the dv¯ simplest possible continuous stochastic system whose full m = −kx¯ − Γ¯v + (2k T Γ)1=2ξ(t¯); (2) ¯ B dynamics can be solved exactly. Additionally, a visual dt dx¯ stimulus in the form of a moving bar that was driven by =v; ¯ an SDDHO process was used in retinal response stud- dt¯ ies [9, 20, 21].
Load more

Recommended publications
  • WHAT IS a CHAOTIC ATTRACTOR? 1. Introduction J. Yorke Coined the Word 'Chaos' As Applied to Deterministic Systems. R. Devane
    WHAT IS A CHAOTIC ATTRACTOR? CLARK ROBINSON Abstract. Devaney gave a mathematical definition of the term chaos, which had earlier been introduced by Yorke. We discuss issues involved in choosing the properties that characterize chaos. We also discuss how this term can be combined with the definition of an attractor. 1. Introduction J. Yorke coined the word `chaos' as applied to deterministic systems. R. Devaney gave the first mathematical definition for a map to be chaotic on the whole space where a map is defined. Since that time, there have been several different definitions of chaos which emphasize different aspects of the map. Some of these are more computable and others are more mathematical. See [9] a comparison of many of these definitions. There is probably no one best or correct definition of chaos. In this paper, we discuss what we feel is one of better mathematical definition. (It may not be as computable as some of the other definitions, e.g., the one by Alligood, Sauer, and Yorke.) Our definition is very similar to the one given by Martelli in [8] and [9]. We also combine the concepts of chaos and attractors and discuss chaotic attractors. 2. Basic definitions We start by giving the basic definitions needed to define a chaotic attractor. We give the definitions for a diffeomorphism (or map), but those for a system of differential equations are similar. The orbit of a point x∗ by F is the set O(x∗; F) = f Fi(x∗) : i 2 Z g. An invariant set for a diffeomorphism F is an set A in the domain such that F(A) = A.
    [Show full text]
  • Attractors of Dynamical Systems in Locally Compact Spaces
    Open Math. 2019; 17:465–471 Open Mathematics Research Article Gang Li* and Yuxia Gao Attractors of dynamical systems in locally compact spaces https://doi.org/10.1515/math-2019-0037 Received February 7, 2019; accepted March 4, 2019 Abstract: In this article the properties of attractors of dynamical systems in locally compact metric space are discussed. Existing conditions of attractors and related results are obtained by the near isolating block which we present. Keywords: Flow, attraction neighborhood, near isolating block MSC: 34C35 54H20 1 Introduction The dynamical system theory studies the rules of changes in the state which depends on time. In the investigation of dynamical systems, one of very interesting topics is the study of attractors (see [1 − 4] and the references therein). In [1 − 3], the authors gave some denitions of attractors and also made further investigations about the properties of them. In [5], the limit set of a neighborhood was used in the denition of an attractor, and in [6] Hale and Waterman also emphasized the importance of the limit set of a set in the analysis of the limiting behavior of a dynamical system. In [7 − 9], the authors dened intertwining attractor of dynamical systems in metric space and obtained some existing conditions of intertwining attractor. In [10], the author studied the properties of limit sets of subsets and attractors in a compact metric space. In [11], the author studied a positively bounded dynamical system in the plane, and obtained the conditions of compactness of the set of all bounded solutions. In [12], the uniform attractor was dened as the minimal compact uniformly pullback attracting random set, several existence criteria for uniform attractors were given and the relationship between uniform and co-cycle attractors was carefully studied.
    [Show full text]
  • Control of Chaos: Methods and Applications
    Automation and Remote Control, Vol. 64, No. 5, 2003, pp. 673{713. Translated from Avtomatika i Telemekhanika, No. 5, 2003, pp. 3{45. Original Russian Text Copyright c 2003 by Andrievskii, Fradkov. REVIEWS Control of Chaos: Methods and Applications. I. Methods1 B. R. Andrievskii and A. L. Fradkov Institute of Mechanical Engineering Problems, Russian Academy of Sciences, St. Petersburg, Russia Received October 15, 2002 Abstract|The problems and methods of control of chaos, which in the last decade was the subject of intensive studies, were reviewed. The three historically earliest and most actively developing directions of research such as the open-loop control based on periodic system ex- citation, the method of Poincar´e map linearization (OGY method), and the method of time- delayed feedback (Pyragas method) were discussed in detail. The basic results obtained within the framework of the traditional linear, nonlinear, and adaptive control, as well as the neural network systems and fuzzy systems were presented. The open problems concerned mostly with support of the methods were formulated. The second part of the review will be devoted to the most interesting applications. 1. INTRODUCTION The term control of chaos is used mostly to denote the area of studies lying at the interfaces between the control theory and the theory of dynamic systems studying the methods of control of deterministic systems with nonregular, chaotic behavior. In the ancient mythology and philosophy, the word \χαωσ" (chaos) meant the disordered state of unformed matter supposed to have existed before the ordered universe. The combination \control of chaos" assumes a paradoxical sense arousing additional interest in the subject.
    [Show full text]
  • ONE HUNDRED YEARS of COMPLEX DYNAMICS the Subject of Complex Dynamics, That Is, the Behaviour of Orbits of Holomorphic Functions
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Liverpool Repository ONE HUNDRED YEARS OF COMPLEX DYNAMICS MARY REES The subject of Complex Dynamics, that is, the behaviour of orbits of holomorphic functions, emerged in the papers produced, independently, by Fatou and Julia, almost 100 years ago. Although the subject of Dynami- cal Systems did not then have a name, the dynamical properties found for holomorphic systems, even in these early researches, were so striking, so unusually comprehensive, and yet so varied, that these systems still attract widespread fascination, 100 years later. The first distinctive feature of iter- ation of a single holomorphic map f is the partition of either the complex plane or the Riemann sphere into two sets which are totally invariant under f: the Julia set | closed, nonempty, perfect, with dynamics which might loosely be called chaotic | and its complement | open, possibly empty, but, if non-empty, then with dynamics which were completely classified by the two pioneering researchers, modulo a few simply stated open questions. Before the subject re-emerged into prominence in the 1980's, the Julia set was alternately called the Fatou set, but Paul Blanchard introduced the idea of calling its complement the Fatou set, and this was immediately universally accepted. Probably the main reason for the remarkable rise in interest in complex dynamics, about thirty-five years ago, was the parallel with the subject of Kleinian groups, and hence with the whole subject of hyperbolic geome- try. A Kleinian group acting on the Riemann sphere is a dynamical system, with the sphere splitting into two disjoint invariant subsets, with the limit set and its complement, the domain of discontinuity, having exactly similar properties to the Julia and Fatou sets.
    [Show full text]
  • Forward Omega Limit Sets of Nonautonomous Dynamical Systems
    DISCRETE AND CONTINUOUS doi:10.3934/dcdss.2020065 DYNAMICAL SYSTEMS SERIES S Volume 13, Number 4, April 2020 pp. 1103{1114 FORWARD OMEGA LIMIT SETS OF NONAUTONOMOUS DYNAMICAL SYSTEMS Hongyong Cui, Peter E. Kloeden and Meihua Yang School of Mathematics and Statistics, and Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan 430074, China Dedicated to J¨urgen Scheurle on his 65th birthday. Abstract. The forward !-limit set !B of a nonautonomous dynamical system ' with a positively invariant absorbing family B = fB(t); t 2 Rg of closed and bounded subsets of a Banach space X which is asymptotically compact is shown to be asymptotically positive invariant in general and asymptotic negative invariant if ' is also strongly asymptotically compact and eventually continuous in its initial value uniformly on bounded time sets independently of the initial time. In addition, a necessary and sufficient condition for a '- invariant family A = fA(t); t 2 Rg in B of nonempty compact subsets of X to be a forward attractor is generalised to this context. 1. Introduction. Omega limit sets play a fundamental role in characterising the behaviour of dynamical systems. Indeed, the attractor of an autonomous dynamical system is an omega limit set. In such systems the omega limit sets are invariant. The situation is more complicated in nonautonomous dynamical systems because the behaviour now depends on the actual time and not on the the time that has elapsed since starting. This leads to new concepts of nonautonomous attractors that consist of families of compact subsets.
    [Show full text]
  • Toward a Theory of Chaos
    December 3, 2003 12:13 00851 Tutorials and Reviews International Journal of Bifurcation and Chaos, Vol. 13, No. 11 (2003) 3147{3233 c World Scientific Publishing Company TOWARD A THEORY OF CHAOS A. SENGUPTA Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India [email protected] Received February 23, 2001; Revised September 19, 2002 This paper formulates a new approach to the study of chaos in discrete dynamical systems based on the notions of inverse ill-posed problems, set-valued mappings, generalized and multi- valued inverses, graphical convergence of a net of functions in an extended multifunction space [Sengupta & Ray, 2000], and the topological theory of convergence. Order, chaos and complexity are described as distinct components of this unified mathematical structure that can be viewed as an application of the theory of convergence in topological spaces to increasingly nonlinear mappings, with the boundary between order and complexity in the topology of graphical con- vergence being the region in (Multi(X)) that is susceptible to chaos. The paper uses results from the discretized spectral approximation in neutron transport theory [Sengupta, 1988, 1995] and concludes that the numerically exact results obtained by this approximation of the Case singular eigenfunction solution is due to the graphical convergence of the Poisson and conjugate Poisson kernels to the Dirac delta and the principal value multifunctions respectively. In (Multi(X)), the continuous spectrum is shown to reduce to a point spectrum, and we introduce a notion of latent chaotic states to interpret superposition over generalized eigenfunctions. Along with these latent states, spectral theory of nonlinear operators is used to conclude that nature supports complexity to attain efficiently a multiplicity of states that otherwise would remain unavailable to it.
    [Show full text]
  • A Poincaré-Bendixson Theorem for Hybrid Systems
    A POINCARE-BENDIXSON´ THEOREM FOR HYBRID SYSTEMS WILLIAM CLARK, ANTHONY BLOCH, LEONARDO COLOMBO Abstract. The Poincar´e-Bendixsontheorem plays an important role in the study of the qualitative be- havior of dynamical systems on the plane; it describes the structure of limit sets in such systems. We prove a version of the Poincar´e-BendixsonTheorem for two dimensional hybrid dynamical systems and describe a method for computing the derivative of the Poincar´ereturn map, a useful object for the stability analysis of hybrid systems. We also prove a Poincar´e-BendixsonTheorem for a class of one dimensional hybrid dynamical systems. 1. Introduction H. Poincar´econsidered the problem of characterizing the structure of limit sets of trajectories of analytic vector fields on the plane in 1886 [17]. I. Bendixson improved the solution proposed by Poincar´ein 1903 by solving the problem under the weaker hypothesis of C1 vector fields [2]. Since then, the investigation of the asymptotic behavior of dynamical systems has been essential to understanding their behavior. The theory of Poincar´e-Bendixsonstudies so-called limit sets. The classic version of the Poincar´e-BendixsonTheorem states that if a trajectory is bounded and its limit set does not contain any fixed points, then the limt set is a periodic orbit [16]. Therefore, the problem of determining the existence of limit cycles in planar continuous dynamics is well understood. Hybrid systems [6] are non-smooth dynamical systems which exhibit a combination of smooth and discrete dynamics, where the flow evolves continuously on a state space, and a discrete transition occurs when the flow intersects a co-dimension one hypersurface.
    [Show full text]
  • Some Applications of the Poincaré-Bendixson Theorem Robert Roussarie
    Some Applications of the Poincaré-Bendixson Theorem Robert Roussarie To cite this version: Robert Roussarie. Some Applications of the Poincaré-Bendixson Theorem. 2021. hal-03107562v2 HAL Id: hal-03107562 https://hal.archives-ouvertes.fr/hal-03107562v2 Preprint submitted on 19 Jan 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. SOME APPLICATIONS OF THE POINCARE-BENDIXSON´ THEOREM ROBERT ROUSSARIE Abstract. We consider a C1 vector field X defined on an open subset U of the plane, with compact closure. If X has no singular points and if U is simply connected, a weak version of the Poincar´e-BendixsonTheorem says that the limit sets of X in U are empty but that one can defined non empty extended limit sets contained into the boundary of U. We give an elementary proof of this result, independent of the classical Poincar´e-BendixsonTheorem. A trapping triangle T based at p, for a C1 vector field X defined on an open subset U of the plane, is a topological triangle with a corner at a point p located on the boundary @U and a good control of the tranversality of X along the sides.
    [Show full text]
  • Limit Sets of Foliations Annales De L’Institut Fourier, Tome 15, No 2 (1965), P
    ANNALES DE L’INSTITUT FOURIER RICHARD SACKSTEDER ART J. SCHWARTZ Limit sets of foliations Annales de l’institut Fourier, tome 15, no 2 (1965), p. 201-213 <http://www.numdam.org/item?id=AIF_1965__15_2_201_0> © Annales de l’institut Fourier, 1965, tous droits réservés. L’accès aux archives de la revue « Annales de l’institut Fourier » (http://annalif.ujf-grenoble.fr/) implique l’accord avec les conditions gé- nérales d’utilisation (http://www.numdam.org/conditions). Toute utilisa- tion commerciale ou impression systématique est constitutive d’une in- fraction pénale. Toute copie ou impression de ce fichier doit conte- nir la présente mention de copyright. Article numérisé dans le cadre du programme Numérisation de documents anciens mathématiques http://www.numdam.org/ Ann. Inst. Fourier, Grenoble 15, 2 (1965), 201-214. LIMIT SETS OF FOLIATIONS by Richard SACKSTEDER and Arthur J. SCHWARTZ 1. Introduction. Let V be an M-manifold with a foliated structure of co- dimension one. A leaf of the foliation is called proper if its topology as an (n—l)-manifold agrees with its topology as a subset of V. One type of theorem which is proved here asserts that proper leaves behave much like compact leaves with respect to certain stability properties. For example, Theorem 1 can be viewed as an extension of a theorem of Reeb on the behavior of leaves in a neighborhood of compact leaf to non-compact, proper leaves. Other theorems show that leaves whose holonomy is finite in a certain sense have properties like those of non-periodic solutions of differential equations on 2-manifolds.
    [Show full text]
  • On the Julia Set of a Typical Quadratic Polynomial with a Siegel Disk
    Annals of Mathematics, 159 (2004), 1–52 On the Julia set of a typical quadratic polynomial with a Siegel disk By C. L. Petersen and S. Zakeri To the memory of Michael R. Herman (1942–2000) Abstract Let 0 <θ<1 be an irrational number with continued fraction expansion 2πiθ θ =[a1,a2,a3,...], and consider the quadratic polynomial Pθ : z → e z + z2. By performing a trans-quasiconformal surgery on an associated Blaschke product model, we prove that if √ log an = O( n)asn →∞, then the Julia set of Pθ is locally connected and has Lebesgue measure zero. In particular, it follows that for almost every 0 <θ<1, the quadratic Pθ has a Siegel disk whose boundary is a Jordan curve passing through the critical point of Pθ. By standard renormalization theory, these results generalize to the quadratics which have Siegel disks of higher periods. Contents 1. Introduction 2. Preliminaries 3. A Blaschke model 4. Puzzle pieces and a priori area estimates 5. Proofs of Theorems A and B 6. Appendix: A proof of Theorem C References 1. Introduction 2πiθ 2 Consider the quadratic polynomial Pθ : z → e z + z , where 0 <θ<1 is an irrational number. It has an indifferent fixed point at 0 with multiplier 2πiθ − 2πiθ ∞ Pθ(0) = e , and a unique finite critical point located at e /2. Let Aθ( ) be the basin of attraction of infinity, Kθ = C Aθ(∞) be the filled Julia set, 2 C. L. PETERSEN AND S. ZAKERI and Jθ = ∂Kθ be the Julia set of Pθ. The behavior of the sequence of iterates { ◦n} Pθ n≥0 near Jθ is intricate and highly nontrivial.
    [Show full text]
  • Μ-Limit Sets of Cellular Automata from a Computational Complexity
    µ-Limit Sets of Cellular Automata from a Computational Complexity Perspective Laurent Boyer,Martin Delacourt,Victor Poupet,Mathieu Sablik, Guillaume Theyssier Abstract This paper concerns µ-limit sets of cellular automata: sets of configu- rations made of words whose probability to appear does not vanish with time, starting from an initial µ-random configuration. More precisely, we investigate the computational complexity of these sets and of related 0 decision problems. Main results: first, µ-limit sets can have a Σ3-hard language, second, they can contain only α-complex configurations, third, 0 any non-trivial property concerning them is at least Π3-hard. We prove complexity upper bounds, study restrictions of these questions to particu- lar classes of CA, and different types of (non-)convergence of the measure of a word during the evolution. 1 Introduction A cellular automaton (CA) is a complex system defined by a local rule which acts synchronously and uniformly on the configuration space. These simple models have a wide variety of different dynamical behaviors, in particular interesting asymptotic behaviors. In the dynamical systems context, it is natural to study the limit set of a cellular automaton: it is defined as the set of configurations that can appear arbitrarily far in time. This set captures the longterm behavior of the CA and has been widely studied since the end of the 1980s. Given a cellular automaton, arXiv:1309.6730v2 [cs.DM] 22 Jun 2015 it is difficult to determine its limit set. Indeed it is undecidable to know if it contains only one configuration [Kar92] and more generally, any nontrivial property of limit sets is undecidable [Kar94].
    [Show full text]
  • Arxiv:Math/9410221V1
    APPEARED IN BULLETIN OF THE AMERICAN MATHEMATICAL SOCIETY Volume 31, Number 2, October 1994, Pages 155-172 FRONTIERS IN COMPLEX DYNAMICS CURTIS T. MCMULLEN 1. Introduction Rational maps on the Riemann sphere occupy a distinguished niche in the general theory of smooth dynamical systems. First, rational maps are complex-analytic, so a broad spectrum of techniques can contribute to their study (quasiconformal mappings, potential theory, algebraic geometry, etc.). The rational maps of a given degree form a finite-dimensional manifold, so exploration of this parameter space is especially tractable. Finally, some of the conjectures once proposed for smooth dynamical systems (and now known to be false) seem to have a definite chance of holding in the arena of rational maps. In this article we survey a small constellation of such conjectures centering around the density of hyperbolic rational maps — those which are dynamically the best behaved. We discuss some of the evidence and logic underlying these conjectures, and sketch recent progress towards their resolution. Our presentation entails only a brief account of the basics of complex dynamics; a more systematic exposition can be found in the survey articles [Dou1], [Bl], and [EL]; the recent books [Bea] and [CG]; and Milnor’s lecture notes [Mil2]. 2. Hyperbolic rational maps A rational map f : C → C is a holomorphic dynamical system on the Riemann sphere C = C ∪ {∞}. Any such map can be written as a quotient b b d b P (z) a0z + ... + ad f(z) = = d Q(z) b0z + ... + bd of two relative prime polynomials P and Q. The degree of f can be defined topo- logically or algebraically; it is the number of preimages of a typical point z, as arXiv:math/9410221v1 [math.DS] 1 Oct 1994 well as the maximum of the degrees of P and Q.
    [Show full text]