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The Many Facets of Natural Computing
"... related. I am confident that at their interface great discoveries await those who seek them. ” (L.Adleman, [3]) 1. FOREWORD Natural computing is the field of research that investigates models and computational techniques inspired by nature and, dually, attempts to understand the world around us in t ..."
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Cited by 29 (1 self)
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related. I am confident that at their interface great discoveries await those who seek them. ” (L.Adleman, [3]) 1. FOREWORD Natural computing is the field of research that investigates models and computational techniques inspired by nature and, dually, attempts to understand the world around us in terms of information processing. It is a highly interdisciplinary field that connects the natural sciences with computing science, both at the level of information technology and at the level of fundamental research, [98]. As a matter of fact, natural computing areas and topics come in many flavours, including pure theoretical research, algorithms and software applications, as well as biology, chemistry and physics experimental laboratory research. In this review we describe computing paradigms abstracted
CRYSTALLINE COMPUTATION
 CHAPTER 18 OF FEYNMAN AND COMPUTATION (A. HEY, ED.)
, 1999
"... Discrete lattice systems have had a long and productive history in physics. Examples range from exact theoretical models studied in statistical mechanics to approximate numerical treatments of continuum models. There has, however, been relatively little attention paid to exact lattice models which o ..."
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Cited by 28 (7 self)
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Discrete lattice systems have had a long and productive history in physics. Examples range from exact theoretical models studied in statistical mechanics to approximate numerical treatments of continuum models. There has, however, been relatively little attention paid to exact lattice models which obey an invertible dynamics: from any state of the dynamical system you can infer the previous state. This kind of microscopic reversibility is an important property of all microscopic physical dynamics. Invertible lattice systems become even more physically realistic if we impose locality of interaction and exact conservation laws. In fact, some invertible and momentum conserving lattice dynamics—in which discrete particles hop between neighboring lattice sites at discrete times—accurately reproduce hydrodynamics in the macroscopic limit. These kinds of discrete systems not only provide an intriguing informationdynamics approach to modeling macroscopic physics, but they may also be supremely practical. Exactly the same properties that make these models physically realistic also make them efficiently realizable. Algorithms that incorporate constraints such as locality of interaction and invertibility can be run on microscopic physical hardware that shares these constraints. Such hardware can, in principle, achieve a higher density and rate of computation than any other kind of computer. Thus it is interesting to construct discrete lattice dynamics which are more physicslike both in order to capture more of the richness of physical dynamics in informational models, and in order to improve our ability to harness physics for computation. In this chapter, we discuss techniques for bringing discrete lattice dynamics closer to physics, and some of the interesting consequences of doing so.
An Architecture for modelling emergence in CAlike systems
 IN ECAL 2005, LNCS
, 2005
"... We consider models of emergence, adding downward causation to conventional models where causation permeates from lowlevel elements to highlevel behaviour. We describe an architecture and prototype simulation medium for tagging and modelling emergent features in CAlike systems. This is part of ong ..."
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Cited by 21 (15 self)
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We consider models of emergence, adding downward causation to conventional models where causation permeates from lowlevel elements to highlevel behaviour. We describe an architecture and prototype simulation medium for tagging and modelling emergent features in CAlike systems. This is part of ongoing work on engineering emergence.
Embodied computation
, 2007
"... The traditional computational devices and models, such as the von Neumann architecture or the Turing machine, are strongly influenced by concepts of central control and perfection. The standard models of computation seem to cover the reality of computation only partially and lack, in particular, in ..."
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Cited by 9 (2 self)
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The traditional computational devices and models, such as the von Neumann architecture or the Turing machine, are strongly influenced by concepts of central control and perfection. The standard models of computation seem to cover the reality of computation only partially and lack, in particular, in the ability to describe more natural forms of computation. In this paper we propose the concept of embodied computation, a straight forward advancement of well known concepts such as amorphous computing, emergent phenomena and embodied cognitive science. Many embodied microscopic computational devices form a single macroscopic device of embodied computation. The solution to computational problems emerges from a huge amount of local interactions. The system’s memory is the sum of the positional information and possibly of the internal states. Such systems are very robust and allow different methodologies to analyze computation. To back this theoretic concept some results based on simulations are given and potential benefits of this approach are discussed.
Discrete, Amorphous Physical Models
 Master’s thesis, MIT
, 2001
"... Introduction Physical modelling is the process of finding a mathematical description that is consistent with observations of the physical world. Many models, from the heat equation to the Schrodinger equation, are formulated in the continuous language of di#erential equations. Space and time are th ..."
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Cited by 7 (3 self)
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Introduction Physical modelling is the process of finding a mathematical description that is consistent with observations of the physical world. Many models, from the heat equation to the Schrodinger equation, are formulated in the continuous language of di#erential equations. Space and time are thought of as a continuum, hence the models potentially specify detail down to arbitrarily fine scales. Discrete physical models are an attractive alternative to continuous models such as partial di#erential equations. In discrete models, space is treated as a lattice, and time is discrete. Physical processes are modelled by rules that typically depend on a small number of nearby locations. From a theoretical standpoint, such models have the advantage that they do not have infinitely many locations per unit volume. From a practical standpoint, they correspond to the discrete structure of digital computing machines, and this makes them natural for simulation. Formulations in which space and t
The Matrix as metaphysics
 Retrieved June
"... The Matrix presents a version of an old philosophical fable: the brain in a vat. A disembodied brain is floating in a vat, inside a scientist’s laboratory. The scientist has arranged that the brain will be stimulated with the same sort of inputs that a normal embodied brain receives. To do this, the ..."
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The Matrix presents a version of an old philosophical fable: the brain in a vat. A disembodied brain is floating in a vat, inside a scientist’s laboratory. The scientist has arranged that the brain will be stimulated with the same sort of inputs that a normal embodied brain receives. To do this, the brain is connected to a giant computer simulation of a world. The simulation determines which inputs the brain receives. When the brain produces outputs, these are fed back into the simulation. The internal state of the brain is just like that of a normal brain, despite the fact that it lacks a body. From the brain’s point of view, things seem very much as they seem to you and me. The brain is massively deluded, it seems. It has all sorts of false beliefs about the world. It believes that it has a body, but it has no body. It believes that it is walking outside in the sunlight, but in fact it is inside a dark lab. It believes it is one place, when in fact it may be
Neural Networks and Cellular Automata Complexity
 Complex Systems
, 1993
"... . The genotypephenotype relation for the 256 elementary cellular automata is studied using neural networks. Neural network are trained to learn the mapping from each genotype rule to its corresponding LiPackard phenotype class. By investigating learning curves and networks pruned with Optimal ..."
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Cited by 3 (2 self)
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. The genotypephenotype relation for the 256 elementary cellular automata is studied using neural networks. Neural network are trained to learn the mapping from each genotype rule to its corresponding LiPackard phenotype class. By investigating learning curves and networks pruned with Optimal Brain Damage on all 256 rules, we find that there is a correspondence between the complexity of the phenotype class and the complexity (net size needed and test error) of the net trained on the class. For LiPackard Class A (null rules) it is possible to extract a simple logical relation from the pruned network. The observation that some rules are harder for the networks to classify leads to an investigation of rule 73 and its conjugate rule 109. Experiments reveal 3cycles in magnetization in agreement with observations in higher dimensional cellular automata systems. 1
Looking at Nature as a Computer
, 2002
"... Although not always identified as such, information has been a fundamental quantity in Physics since the advent of Statistical Mechanics, which recognized “counting states” as the fundamental operation needed to analyze thermodynamic systems. Quantum Mechanics (QM) was invented to fix the infinities ..."
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Cited by 2 (0 self)
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Although not always identified as such, information has been a fundamental quantity in Physics since the advent of Statistical Mechanics, which recognized “counting states” as the fundamental operation needed to analyze thermodynamic systems. Quantum Mechanics (QM) was invented to fix the infinities that arose classically in trying to count the states of Black Body radiation. In QM, both amount and rate of change of information in a finite physical system are finite. As Quantum Statistical Mechanics developed, classical finitestate models naturally played a fundamental role, since only the finitestate character of the microscopic substratum normally enters into the macroscopic counting. Given more than a century of finitestate underpinnings, one might have expected that by now all of physics would be based on informational and computational concepts. That this isn’t so may simply reflect the stubborn legacy of the continuum, and the recency and macroscopic character of computer science. In this paper, I discuss the origins of informational concepts in physics, and reexamine computationally some fundamental dynamical quantities. KEY WORDS: information; entropy; energy; action; cellular automaton; quantum mechanics.
F.R.M.: An Architecture for modelling emergence in CAlike systems
 In ECAL 2005, LNCS
"... Abstract. We consider models of emergence, adding downward causation to conventional models where causation permeates from lowlevel elements to highlevel behaviour. We describe an architecture and prototype simulation medium for tagging and modelling emergent features in CAlike systems. This is p ..."
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Cited by 1 (1 self)
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Abstract. We consider models of emergence, adding downward causation to conventional models where causation permeates from lowlevel elements to highlevel behaviour. We describe an architecture and prototype simulation medium for tagging and modelling emergent features in CAlike systems. This is part of ongoing work on engineering emergence.
The ChurchTuring Thesis as an Immature Form of the ZuseFredkin Thesis (More Arguments in Support of the “Universe as a Cellular Automaton” Idea)
"... In [1] we have shown a strong argument in support of the "Universe as a computer " idea. In the current work, we continue our exposition by showing more arguments that reveal why our Universe is not only "some kind of computer", but also a concrete computational model known as a ..."
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In [1] we have shown a strong argument in support of the "Universe as a computer " idea. In the current work, we continue our exposition by showing more arguments that reveal why our Universe is not only "some kind of computer", but also a concrete computational model known as a "cellular automaton".