Revisão Bibliográfica: Autômatos Celulares
Title: Neural Networks and Cellular Automata Complexity
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- Title: Modeling Evolution and Immune System by Cellular Automata
Title: Neural Networks and Cellular Automata ComplexityAuthor: Gorodkin Srensen Winther Abstract: The genotype-phenotype 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 Li-Packard 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 Li-Packard 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 ... 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Like many... /...Korea E-mail : uribyul@candy.yonsei.ac.kr, sbcho@csai.yonsei.ac.kr Abstract CAM-Brain is a neural network based on cellular automata, which model complex phenomenon by simple rules, and optimized ... • Object Oriented Artificial Neural Network Implementations - Curt Lefebvre Jose (1993) ...can be modeled through local rules (e.g. adaptive signal processing, biological neural modeling, cellular automata and artificial life). 1.0 Introduction An ANN model is described by a set of dynamical... /... Object Oriented Artificial Neural Network Implementations W. Curt Lefebvre Jose C. Principe NeuroDimension, Incorporated... • THE CAM-BRAIN MACHINE (CBM) An FPGA-Based Hardware Tool that.. - Hugo De Garis ...(CBM), an FPGA-based piece of hardware that implements a genetic algorithm (GA) to evolve a cellular automata (CA) based neural network circuit module (of approximately 1,000 neurons) in seconds (i.e., a... /...of hardware that implements a genetic algorithm (GA) to evolve a cellular automata (CA) based neural network circuit module (of approximately 1,000 neurons) in seconds (i.e., a complete run of a GA, ... • Reliability Analysis of Self-Repairing Bio-inspired Cellular.. - Cesar Ortega And ...amt}@ohm.york.ac.uk 1. Introduction The systematic study of artificial cellular systems, like cellular automata, neural networks or processor arrays, has gained momentum during the past few years in Artificial ... /... 1. Introduction The systematic study of artificial cellular systems, like cellular automata, neural networks or processor arrays, has gained momentum during the past few years in Artificial Life... • Evolvable Hardware for Fault-Tolerant Applications - Cesar Ortega And (1998) ...be situated in the epigenesis axis. On the ontogeny axis would be the works on self-reproducing cellular automata such as the embryonics project. 3. The Embryonics project Embryonics is the application of... /...the work done on evolvable systems has generated some remarkable results. 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Surprisingly, it was found that the latter approach performed significantly better than ... • Co-evolutionary Search in Asymmetric Spaces - Bj Orn Olsson (2000) ...salesperson problem [24], classi cation neural networks [15,17], constraint satisfaction [16], cellular automata [18,10], path planning [19], tic-tac-toe endgame classi cation [10], game playing [22], and... /...been used for a number of tasks, including the traveling salesperson problem [24], classi cation neural networks [15,17], constraint satisfaction [16], cellular automata [18,10], path planning [19],... • The Advantages of Generative Grammatical Encodings for.. - Hornby, Pollack (2001) ...encoding is a more compact encoding of a solution. Some examples of generative systems are cellular automata rules to produce 2D shapes [9], context rules to produce 2D tiles [10], graph encoding for 3D... /... 2D tiles [10], graph encoding for 3D animated creatures [11], and cellular encoding for artificial neural networks [12]. 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Voltar 9.3) Autômatos Celulares e Sistemas Imunológicos Immunology is a rather new field of research; although the first description of immunization dates back to Thucydides' Historu of the Pelloponesian Wars and the first experiments on vaccinations were performed by Jenner in the 18th century; the birth of immunology as a modern science was in the late 19th century when Kock proved the infectious diseases were caused by pathogenic microorganisms, Pasteur devised vaccines against anthrax and rabies and finally von Behring discovered antibodies. Nowadays immunology is composed of a hard core of well accepted and proven facts covered by an unstable crust of many unproven theories and real facts of unknown relevance. In the context the use of mathematical models and computer simulations (in machine experiments) turns out to be a useful tool for testing hypothesis and planning real in vivo and in vitro experiments. Many different models have been proposed to study some of more controversial features of immune responses like diversity, memory and regulation. The defense mechanisms used by the body against an attack from foreign substances (antigens) are several, they include: physical barriers, phagocyte cells, different clones of particular white blood cells (lymphocytes) and various blood-borne molecules (e.g., antibodies). Some of these mechanisms are present prior to exposure to antigens and their response doesn't change upon further exposure to the same antigen. This kind of response is called natural immunity. There are other mechanisms with more specific behavior, antigens induce them and their response increases in magnitude and defense capabilities with successive exposure to the same antigens. These mechanisms are called acquired (or specific) immunity and are those considered in most of models. The main features of specific immunity are: Specificity, Diversity, Maturation, Memory, and Discrimination of self from non-self.
Many models of immune response based on cellular automata have been introduced. The first CA approach to immunology was the model proposed by Kaufman, Urbain and Thomas. They considered five populations: Antibodies A, helper cells H, suppressor cells S, cells B, and the virus V. All of them are considered binary variables: 1 corresponds to high concentrations, 0 to low ones. The evolution rules are (where primes indicates the updated state): A' = V and Band H H' = H or V and not S S' = H or S B' = H and (V or B) V' = V and not A Where OR, AND and Not are the usual logical operators. We have five states CA with long-range couplings. The system has the following fixed points
Starting from one of the first three states (virgin, vaccinated and immune) and adding a virus, we let the system reach the immune state. The transient time is shorter starting from vaccinated and immune state. this very simple model is then able to reproduce some basic features of immune response. This model has been extended and analyzed further by Kaufman and Thomas and Kaufman where they add different stages of activation for the cells and considerer also an analysis in term of differential equations. All possible five Boolean variables automata have been studied by Chowdhury and Stauffer. Autoimmune disease like multiple sclerosis, cancer and AIDS are simulated with similar automata. a probabilistic CA for AIDS was proposed and more AIDS models are discussed.
Author: Bezzi Sissa ViaArtigos relacionados: • Modeling biology by cellular automata - Bezzi (1999) • From Natural to Artificial Life: Biomimetic Mechanisms in Animat.. - Meyer (1997)
• -D cellular automata and short range molecular dynamics.. - Marian Bubak ...2-D cellular automata and short range molecular dynamics programs for simulations on networked workstations and... /...gas (LGA) investigations of fluid flows while the second one is appropriate for modelling of immune systems (CA). Lattice gas consists of unit mass particles moving with unit velocities on a hexagonal... • The Evolutionary Design of Collective Computation in.. - James Crutchfield ...Evolutionary Design of Collective Computation in Cellular Automata James P. Crutchfield y Santa Fe Institute 1399 Hyde Park Road Santa Fe, NM 87501... /...borrowing information-processing mechanisms found in natural systems such as brains [7, 28, 63], immune systems [26, 31], insect colonies [9, 21], economies [78, 80], and biological evolution [2, 29, 40]. The... • Modeling biology by cellular automata - Bezzi (1999) • Use of Phenotypic and Molecular Data for Genetic.. - Also Published On-Line • CRI Assessment of Management Actions Aimed at Snake River.. - Note That Cri • QTL Cartographer Version 1.13 - Christopher Basten Bruce • Scientific Challenges to Animal Breeding and Genetics - Ollivier • Modelling Macroevolutionary Patterns: an Ecological Perspective - Ricard Sol Complex • Metastable States in Spin Glasses and Disordered Ferromagnets - Newman Newman Cims • Exploring Protein Sequence Space Using Knowledge Based.. - Aderonke Babajide (1999) • Evolving Complete Agents using Arti cial Ontogeny - Josh Bongard Rolf • Long-range Correlations in the Fossil Record and.. - Sole, Manrubia.. • Searching for Rhythms in Asynchronous Random Boolean Networks - Ezequiel Di Paolo • Evolution in time-dependent fitness landscapes - Claus Wil Ke (1998) • Fitness Landscape Analysis and Memetic Algorithms for the.. - Merz, Freisleben (1999) • On the Performance of Memetic Algorithms in Combinatorial.. - Peter Merz University • The Island Model Genetic Algorithm: On Separability.. - Darrell Whitley Soraya • Structure Formation Of Biopolymers Is Complex, Their.. - Bornberg-Bauer • Anisotropy in Fitness Landscapes - Stadler, Grüner (1993) • Error Thresholds on Correlated Fitness Landscapes - Bonhoeffer, Stadler (1992) • The Advantages of Evolutionary Computation - David Fogel Natural • Statistics of RNA Melting Kinetics - Tacker, Fontana, Stadler, Schuster (1993) Voltar Directory: pub -> docs -> vonzuben pub -> Asilomar Conference on Circuits, Systems & Computers, Pacific Grove, ca, November 1978, pp. 55 58 pub -> Forthcoming meetings and conferences pub -> Harmonised compatibility and sharing conditions for video pmse in the 7 9 ghz frequency band, taking into account radar use docs -> A dragon in the tube vonzuben -> Revisão Bibliográfica: Learning Vector Quantization 28/04/2003 vonzuben -> Revisão bibliográfica redes neurais recorrentes docs -> Updated March 2017 Registration Quick Reference Card for Employees/Associates Download 445.54 Kb. Share with your friends:
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