Skip to main content

Abstract

This paper reviews a class of generic dissipative dynamical systems called N-K models. In these models, the dynamics of N elements, defined as Boolean variables, develop step by step, clocked by a discrete time variable. Each of the N Boolean elements at a given time is given a value which depends upon K elements in the previous time step. We review the work of many authors on the behavior of the models, looking particularly at the structure and lengths of their cycles, the sizes of their basins of attraction, and the flow of information through the systems. In the limit of infinite N, there is a phase transition between a chaotic and an ordered phase, with a critical phase in between. We argue that the behavior of this system depends significantly on the topology of the network connections. If the elements are placed upon a lattice with dimension d,the system shows correlations related to the standard percolation or directed percolation phase transition on such a lattice. On the other hand, a very different behavior is seen in the Kauffman net in which all spins are equally likely to be coupled to a given spin. In this situation, coupling loops are mostly suppressed, and the behavior of the system is much more like that of a mean field theory. We also describe possible applications of the models to, for example, genetic networks, cell differentiation, evolution, democracy in social systems and neural networks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Abarbanel, H. D. I., M. I. Rabinovich, A. Selverston, and M. V. Bazhenov [ 1996 ], Synchronization in Neural Networks, Physics-Uspeki 39, 337–362.

    Article  Google Scholar 

  • Albert, R. and A.-L. Barabâsi [ 2000 ], Dynamics of Complex Systems: Scaling Laws for the Period of Boolean Networks, Physical Review Letters 84, 56605663.

    Google Scholar 

  • Albert, R. and A.-L. Barabâsi [ 2002 ], Statistical Mechanics of Complex Networks, Reviews of Modern Physics 74, 47–97.

    Article  MathSciNet  MATH  Google Scholar 

  • Alberts, B., D. Bray, J. Lewis, M. Raff, K. Roberts, and J. D. Watson [ 1994 ], Molecular Biology of the Cell, Third Edition. Garland Publishing, New York.

    Google Scholar 

  • Andrecut, M. and M. K. Ali [ 2001 ], Chaos in a Simple Boolean Network, International Journal of Modern Physics B 15, 17–23.

    Article  MathSciNet  Google Scholar 

  • Atlan, H., F. Fogelman-Soulie, J. Salomon, and G. Weisbuch [ 1981 ], Random Boolean Networks, Cybernetics and Systems 12, 103–121.

    Google Scholar 

  • Bagley, R. J. and L. Glass [ 1996 ], Counting and Classifying Attractors in High Dimensional Dynamical Systems, Journal of Theoretical Biology 183, 269–284.

    Article  Google Scholar 

  • Baillie, C. F. and D. A. Johnston [ 1994 ], Damaging 2D Quantum Gravity, Physics Letters B 326, 51–56.

    Google Scholar 

  • Bak, P., H. Flyvbjerg, and B. Lautrup [ 1992 ], Coevolution in a Rugged Fitness Landscape, Physical Review A 46, 6724–6730.

    Google Scholar 

  • Barabâsi, A.-L. [ 2002 ], Linked: The New Science of Networks. Perseus Publising, Cambridge, Massachusetts.

    Google Scholar 

  • Bastolla, U. and G. Parisi [ 1996 ], Closing Probabilities in the Kauffman Model: an Annealed Computation, Physica D 98, 1–25.

    Google Scholar 

  • Bastolla, U. and G. Parisi [ 1997 ], A Numerical Study of the Critical Line of Kauffman Networks, Journal of Theoretical Biology 187, 117–133.

    Google Scholar 

  • Bastolla, U. and G. Parisi [ 1998a ], The Modular Structure of Kauffman Networks, Physica D 115, 219–233.

    Google Scholar 

  • Bastolla, U. and G. Parisi]1998b], Relevant Elements, Magnetization and Dynamical Properties in Kauffman Networks: a Numerical Study, Physica D 115203–218.

    Google Scholar 

  • Bhattacharjya, A. and S. Liang [ 1996a ], Median Attractor and Transients in Random Boolean Nets, Physica D 95, 29–34.

    Google Scholar 

  • Bhattacharjya, A. and S. Liang[1996b], Power-Law Distributions in Some Random Boolean Networks, Physical Review Letters 77, 1644–1647.

    Google Scholar 

  • Bilke, S. and F. Sjunnesson [ 2001 ], Stability of the Kauffman Model, Physical Review E 65, 016129.

    Google Scholar 

  • Bornholdt, S. [ 1998 ], Genetic Algorithm Dynamics on a Rugged Landscape, Physical Review E 57, 3853–3860.

    Google Scholar 

  • Bornholdt, S. and T. Rohlf [ 2000 ], Topological Evolution of Dynamical Networks: Global Criticality From Local Dynamics, Physical Review Letters 84, 61146117.

    Google Scholar 

  • Bornholdt, S. and K. Sneppen [ 1998 ], Neutral Mutations and Punctuated Equi- librium in Evolving Genetic Networks, Physical Review Letters 81, 236–239.

    Google Scholar 

  • Bornholdt, S. and K. Sneppen [ 2000 ], Robustness as an Evolutionary Principle, Proc. Royal Soc. Lond. B 266, 2281–2286.

    Google Scholar 

  • Bull, L. [ 1999 ], On the Baldwin Effect, Artificial Life 5, 241–246.

    Article  Google Scholar 

  • Burda, Z., J. Jurkiewicz, and H. Flyvbjerg [ 1990 ], Classification of Networks of Automata By Dynamic Mean-Field Theory, Journal of Physics A: Mathematical and General 23, 3073–3081.

    Google Scholar 

  • Castagnoli, G. [ 1998 ], Merging Quantum Annealing Computation and Particle Statistics: A Prospect in the Search of Efficient Solutions to Intractable Problems, International Journal of Theoretical Physics 37, 457–462.

    Article  MathSciNet  MATH  Google Scholar 

  • Cheng, B. and D. M. Titterington [ 1994 ], Neural networks: a review from a statistical perspective, Statistical Science 9, 2–54.

    Article  MathSciNet  MATH  Google Scholar 

  • Coppersmith, S. N., L. P. Kadanoff, and Z. Zhang [ 2001a ], Reversible Boolean Networks I: Distribution of Cycle Lengths, Physica D 149, 11–29.

    Article  MathSciNet  MATH  Google Scholar 

  • Coppersmith, S. N., L. P. Kadanoff, and Z. Zhang [ 2001b ], Reversible Boolean Networks II: Phase Transitions, Oscilations and Local Structures., Physica D 157, 54–74.

    Article  MathSciNet  MATH  Google Scholar 

  • Corsten, M. and P. Poole [ 1988 ], Initiation of Damage in the Kauffman Model, Journal of Statistical Physics 50, 461–463.

    Article  Google Scholar 

  • Dawkins, R. [ 1986 ], The Blind Watchmaker. W.W. Norton and Company, USA.

    Google Scholar 

  • Dawkins, R. [ 1989 ], The Selfish Gene. Oxford University Press, Oxford, second edition.

    Google Scholar 

  • De Sales, J. A., M. L. Martins, and D. A. Stariolo [ 1997 ], Cellular Automata Model for Gene Networks, Physical Review E 55, 3262–3270.

    Article  Google Scholar 

  • Derrida, B. [ 1980 ], Random-Energy Model: Limit of a Family of Disordered Models, Physical Review Letters 45, 79–82.

    Article  MathSciNet  Google Scholar 

  • Derrida, B. [ 1987a ], Dynamical Phase Transitions in Non-Symmetric Spin Glasses, Journal of Physics A: Mathematical and General 20, L721 — L725.

    Article  MathSciNet  Google Scholar 

  • Derrida, B. [ 1987b ], Valleys and Overlaps in Kauffman Model, Philosophical Magazine B: Physics of Condensed Matter, Statistical Mechanics, Electronic, Optical and Magnetic Properties 56, 917–923.

    Google Scholar 

  • Derrida, B. and D. Bessis [ 1988 ], Statistical Properties of Valleys in the Annealed Random Map Model, Journal of Physics A: Mathematical and General 21, L509 — L515.

    Article  MathSciNet  Google Scholar 

  • Derrida, B. and H. Flyvbjerg [ 1986 ], Multivalley Structure in Kauffman Model–Analogy With Spin-Glasses, Journal of Physics A: Mathematical and General 19, 1003–1008.

    Article  Google Scholar 

  • Derrida, B. and H. Flyvbjerg [ 1987a ], Distribution of Local Magnetizations in Random Networks of Automata, Journal of Physics A: Mathematical and General 20, L1107 — L1112.

    Article  MathSciNet  Google Scholar 

  • Derrida, B. and H. Flyvbjerg [ 1987b ], The Random Map Model: a Disordered Model With Deterministic Dynamics, Journal De Physique 48, 971–978.

    Article  MathSciNet  Google Scholar 

  • Derrida, B., E. Gardner, and A. Zippelius [ 1987 ], An Exactly Solvable Asymmetric Neural Network Model, Europhysics Letters 4, 167–173.

    Article  Google Scholar 

  • Derrida, B. and Y. Pomeau [ 1986 ], Random Networks of Automata–a Simple Annealed Approximation, Europhysics Letters 1, 45–49.

    Article  Google Scholar 

  • Derrida, B. and D. Stauffer [ 1986 ], Phase-Transitions in Two-Dimensional Kauffman Cellular Automata, Europhysics Letters 2, 739–745.

    Article  Google Scholar 

  • Derrida, B. and G. Weisbuch [ 1986 ], Evolution of Overlaps Between Configura- tions in Random Boolean Networks, Journal De Physique 47, 1297–1303.

    Article  Google Scholar 

  • Domany, E. and W. Kinzel [ 1984 ], Equivalence of Cellular Automata to Ising Models and Directed Percolation, Physical Review Letters 53, 311–314.

    Google Scholar 

  • Fambrough, D., K. Mcclure, A. Kazlauskas, and E. S. Lander [ 1999 ], Diverse Signaling Pathways Activated By Growth Factor Receptors Induce Broadly Overlapping, Rather That Independent, Sets of Genes, Cell 97, 727–741.

    Google Scholar 

  • Farmer, J. D. [ 1990 ], A Roseta Stone for Connectionism, Physica D 42, 153–187.

    Google Scholar 

  • Flyvbjerg, H. [ 1988 ], An Order Parameter for Networks of Automata, Journal of Physics A: Mathematical and General 21, L955 — L960.

    Google Scholar 

  • Flyvbjerg, H. [ 1989 ], Recent Results for Random Networks of Automata, Acta Physica Polonica B 20, 321–349.

    Google Scholar 

  • Flyvbjerg, H. and N. J. Kjaer [ 1988 ], Exact Solution of Kauffman Model with Connectivity One, Journal of Physics A: Mathematical and General 21, 16951718.

    Google Scholar 

  • Flyvbjerg, H. and B. Lautrup [ 1992 ], Evolution in a Rugged Fitness Landscape, Physical Review A 46, 6714–6723.

    Google Scholar 

  • Fogelman-Soulie, F. [ 1984 ], Frustration and Stability in Random Boolean Networks, Discrete Applied Mathematic 9, 139–156.

    Article  MathSciNet  MATH  Google Scholar 

  • Fogelman-Soulie, F. [ 1985 ], Parallel And Sequential Computation On Boolean Networks, Theor. Comp. Sci. 40, 275–300.

    Article  MathSciNet  MATH  Google Scholar 

  • Genoud, T. and J.-P. Metraux [ 1999 ], Crosstalk in Plant Cell Signaling: Structure and Function of the Genetic Network, Trends in Plant Science 4, 503–507.

    Article  Google Scholar 

  • Glass, L. and C. Hill [ 1998 ], Ordered and Disordered Dynamics in Random Networks, Europhysics Letters 41, 599–604.

    Article  Google Scholar 

  • Golinelli, O. and B. Derrida [ 1989 ], Barrier Heights in the Kauffman Model, Journal De Physique 50, 1587–1601.

    Google Scholar 

  • Griffiths, R. [ 1969 ], Nonanalytic Behavior Above the Critical Point in a Random Ising Ferromagnet, Physical Review Letters 23, 17–19.

    Google Scholar 

  • Hansen, A. [ 1988a ], A Connection Between the Percolation Transition and the Onset of Chaos In the Kauffman Model, Journal of Physics A: Mathematical and General 21, 2481–2486.

    Article  MathSciNet  MATH  Google Scholar 

  • Hansen, A. [ 1988b ], Percolation and Spreading of Damage in a Simplified Kauffman Model, Physica A 153, 47–56.

    Google Scholar 

  • Harris, B. [ 1960 ], Probability Distributions Related to Random Mappings, Annals of Mathematical Statistics 31, 1045–1062.

    Article  MathSciNet  MATH  Google Scholar 

  • Herrmann, H. J. [ 1992 ], Simulation of Random Growth-Processes, Topics in Applied Physics 71, 93–120.

    Article  Google Scholar 

  • Hilhorst, H. J. and M. Nijmeijer [ 1987 ], On the Approach of the Stationary State in Kauffmans Random Boolean Network, Journal De Physique 48, 185–191.

    Article  MathSciNet  Google Scholar 

  • Hopfield, J. J. [ 1982 ], Neural Networks and Physical Systems with Emergent Collective Computational Abilities, Proceedings of the National Academy of Sciences 79, 2554–2558.

    Google Scholar 

  • Hopfield, J. J. [ 1999 ], Brain, Neural Networks and Computation, Reviews of Modern Physics 71, 5431–5437.

    Article  Google Scholar 

  • Huang, S. and D. E. Ingber [ 2000 ], Shape-Dependent Control of Cell Growth, Differentiation, and Apoptosis: Switching Between Attractors in Cell Regulatory Networks, Experimental Cell Research 261, 91–103.

    Article  Google Scholar 

  • Huepe, C. and M. Aldana-González [ 2002 ], Dynamical Phase Transition in a Neural Network Model with Noise: An Exact Solution, Journal of Statistical Physics 108, (3/4), 527–540.

    Article  MathSciNet  MATH  Google Scholar 

  • Ito, K. and Y.-P. Gunji [ 1994 ], Self-Organization of Living Systems Towards Criticality at the Edge of Chaos, Biosystems 33, 17–24.

    Article  Google Scholar 

  • Jan, N. [1988], Multifractality and the Kauffman Model Journal of Physics A: Mathematical and General 21 L899–L902.

    Google Scholar 

  • Kadanoff, L. P. [2000] Statistical Physics: Statics Dynamics and Renormalization. World Scientific, Singapore.

    Google Scholar 

  • Kauffman, S. [1984], Emergent Properties in Random Complex Automata Physica D 10 145–156.

    Google Scholar 

  • Kauffman, S. A. [ 1969 ], Metabolic Stability and Epigenesis in Randomly Constructed Nets, Journal of Theoretical Biology 22, 437–467.

    Article  MathSciNet  Google Scholar 

  • Kauffman, S. A. [ 1974 ], The Large Scale Structure and Dynamics of Genetic Control Circuits: an Ensemble Approach, Journal of Theoretical Biology 44, 167–190.

    Article  Google Scholar 

  • Kauffman, S. A. [ 1990 ], Requirements for Evolvability in Complex Systems–Orderly Dynamics and Frozen Components, Physica D 42, 135–152.

    Article  Google Scholar 

  • Kauffman, S. A. [1993] The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, Oxford.

    Google Scholar 

  • Kauffman, S. A. [1995] At Home in the Universe: the Search for Laws of Self-Organization and Complexity. Oxford University Press, Oxford.

    Google Scholar 

  • Kauffman, S. A. and W. G. Macready [ 1995 ], Search Strategies for Applied Molecular Evolution, Journal of Theoretical Biology 173, 427–440.

    Article  Google Scholar 

  • Kauffman, S. A. and E. D. Weinberger [ 1989 ], The NK Model of Rugged Fitness Landscapes and Its Application To Maturation of the Immune Response, Journal of Theoretical Biology 141, 211–245.

    Article  Google Scholar 

  • Kaufman, J. H., D. Brodbeck, and O. M. Melroy [ 1998 ], Critical Biodiversity, Conservation Biology 12, 521–532.

    Article  Google Scholar 

  • Kirillova, O. V. [ 1999 ], Influence of a Structure on Systems Dynamics on Example of Boolean Networks, International Journal of Modern Physics C 10, 12471260.

    Google Scholar 

  • Klüver, J. and J. Schmidt [ 1999 ], Control Parameters in Boolean Networks and Cellular Automata Revisited from a Logical and Sociological Point of View, Complexity 5, 45–52.

    Article  Google Scholar 

  • Krapivsky, P. L., S. Redner, and F. Leyvraz [ 2000 ], Connectivity of Growing Random Networks, Physical Review Letters 85, 4629–4632.

    Article  Google Scholar 

  • Kulakowski, K. [ 1995 ], Relaxation and Limit-Cycles in a Global Version of the Quenched Kauffman Model, Physica A 216, 120–127.

    Article  Google Scholar 

  • Kürten, K. E. [1988a], Correspondence Between Neural Threshold Networks and Kauffman Boolean Cellular Automata Journal of Physics A: Mathematical and General 21 L615–L619.

    Google Scholar 

  • Kürten, K. E. [ 1988b ], Critical Phenomena in Model Neural Netwoks, Physics Letters A 129, 157–160.

    Article  MathSciNet  Google Scholar 

  • Kürten, K. E. and H. Beer [ 1997 ], Inhomogeneous Kauffman Models at the Borderline Between Order and Chaos, Journal of Statistical Physics 87, 929–935.

    Article  MATH  Google Scholar 

  • Lam, P. M. [ 1988 ], A Percolation Approach to the Kauffman Model, Journal of Statistical Physics 50, 1263–1269.

    Article  Google Scholar 

  • Langton, C. G. [ 1990 ], Computations at the Edge of Chaos: Phase Transitions and Emergent Computation, Physica D 42, 12–37.

    Article  MathSciNet  Google Scholar 

  • Lee, C.-Y. and S. K. Han [ 1998 ], Evolutionary Optimization Algorithm By En-tropic Sampling, Physical Review E 57, 3611–3617.

    Article  Google Scholar 

  • Levitan, B. and S. Kauffman [ 1995 ], Adaptive Walks With Noisy Fitness Measurements, Molecular Diversity 1, 53–68.

    Article  Google Scholar 

  • Little, W. A. [ 1974 ], The Existence of Persistent States in the Brain, Mathematical Bioscience 19, 101–120.

    Article  MATH  Google Scholar 

  • Luczak, T. and J. E. Cohen [ 1991 ], Stability of Vertices in Random Boolean Cellular Automata, Random Structures and Algorithms 2, 327–334. reference from Lynch.

    Google Scholar 

  • Luque, B. and R. V. Solé [ 1997a ], Controlling Chaos in Random Boolean Networks, Europhysics Letters 37, 597–602.

    Article  Google Scholar 

  • Luque, B. and R. V. Solé [ 1997b ], Phase Transitions in Random Networks: Simple Analytic Determination of Critical Points, Physical Review E 55, 257–260.

    Article  Google Scholar 

  • Luque, B. and R. V. Solé [1998], Stable Core and Chaos Control in Random Boolean Networks, Journal of Physics A: Mathematical and General 31, 1533 1537.

    Google Scholar 

  • Luque, B. and R. V. Solé [ 2000 ], Lyapunov Exponents in Random Boolean Networks, Physica A 284, 33–45.

    Article  Google Scholar 

  • Lynch, J. F. [ 1993a ], Antichaos in a Class of Random Boolean Cellular-Automata, Physica D 69, 201–208.

    Article  MathSciNet  MATH  Google Scholar 

  • Lynch, J. F. [ 1993b ], A Criterion for Stability in Random Boolean Cellular-Automata, Los Alamos Data Base http://arXiv.org/abs/adaporg/9305001.

    Google Scholar 

  • Lynch, J. F. [ 1995 ], On the Threshold of Chaos in Random Boolean Cellular-Automata, Random Structures and Algorithms 6, 239–260.

    Article  MathSciNet  MATH  Google Scholar 

  • Ma, S. [ 1976 ], Modern Theory of Critical Phenomena. Benjamin, Reading Pa.

    Google Scholar 

  • Macisaac, A. B., D. L. Hunter, M. J. Corsten, and N. Jan [ 1991 ], Determinism and Thermodynamics–Ising Cellular Automata, Physical Review A 43, 3190–319.

    Article  Google Scholar 

  • Manrubia, S. C. and A. S. Mikhailov [ 1999 ], Mutual Synchronization and Clustering in Randomly Coupled Chaotic Dynamical Networks, Physical Review E 60, 1579–1589.

    Article  Google Scholar 

  • McCulloch, W. S. and W. Pitts [ 1943 ], A Logical Calculus of Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics 5, 115–133.

    Article  MathSciNet  MATH  Google Scholar 

  • Mestl, T., R. J. Bagley, and L. Glass [ 1997 ], Common Chaos in Arbitrarily Complex Feedback Networks, Physicl Review Letters 79, 653–656.

    Article  Google Scholar 

  • Metropolis, N. and S. Ulam [ 1953 ], A Property of Randomness of an Arithmetical Function, American Mathematical Monthly 60, 252–253.

    Article  MathSciNet  MATH  Google Scholar 

  • Mezard, M., G. Parisi, and M. A. Virasoro [ 1987 ], Spin Glass Theory and Beyond. World Scientific, Singapore.

    MATH  Google Scholar 

  • Miranda, E. N. and N. Parga [ 1988 ], Ultrametricity in the Kauffman Model - a Numerical Test, Journal of Physics A: Mathematical and General 21, L357 — L361.

    Article  MathSciNet  Google Scholar 

  • Miranda, E. N. and N. Parga [ 1989 ], Noise Effects in the Kauffman Model, Europhysics Letter 10, 293–298.

    Article  Google Scholar 

  • Nirei, M. [ 1999 ], Critical Fluctuations in a Random Network Model, Physica A 269, 16–23.

    Article  MathSciNet  Google Scholar 

  • Obukhov, S. P. and D. Stauffer [1989], Upper Critical Dimension of Kauffman Cellular Automata, Journal of Physics A: Mathematical and General 22, 1715 1718.

    Google Scholar 

  • Ohta, T. [ 1997a ], The Meaning of Near-Neutrality at Coding and Non-Coding Regions, Gene 205, 261–267.

    Article  Google Scholar 

  • Ohta, T. [ 1997b ], Role of Random Genetic Drift in the Evolution of Interactive Systems, Journal of Molecular Evolution 44, S9 — S14.

    Article  Google Scholar 

  • Ohta, T. [ 1998 ], Evolution By Nearly Neutral Mutations, Genetica 103, 83–90.

    Article  Google Scholar 

  • Owezarek, A., A. Rechnitzer, and A. J. Guttmann [ 1997 ], On the Hulls of Directed Percolation Clusters, Journal of Physics A: Mathematical and General 30, 6679–6691.

    Article  MathSciNet  Google Scholar 

  • Petters, D. [ 1997 ], Patch Algorithms in Spin Glasses, International Journal of Modern Physics C 8, 595–600.

    Article  Google Scholar 

  • Preisler, H. D. and S. Kauffman [ 1999 ], A Proposal Regarding the Mechanism Which Underlies Lineage Choice During Hematopoietic Differentiation, Leukemia Research 23, 685–694.

    Article  Google Scholar 

  • Qu, X., L. Kadanoff, and M. Aldana [ 2002 ], Numerical and Theoretical Studies of Noise Effects in Kauffman Model, Journal of Statistical Physics, 109, (5/6), 967–986.

    Article  MATH  Google Scholar 

  • Randeria, M., J. Sethna, and R. Palmer [ 1985 ], Low-Frequency Relaxation in Ising Spin-Glasses, Physical Review Letters 54, 1321–1324.

    Article  Google Scholar 

  • Rosser, J. B. and L. Schoenfeld [ 1962 ], Approximate Formulas for some Functions of Prime Numbers, Illinois Journal of Mathematics 6, 64–94.

    MathSciNet  MATH  Google Scholar 

  • Sakai, K. and Y. Miyashita [ 1991 ], Neural Organization for the Long-TermMemory of Paired Associates, Nature 354, 152–155.

    Article  Google Scholar 

  • Serra, R. and M. Villani [ 1997 ], Modelling Bacterial Degradation of Organic Compounds With Genetic Networks, Journal of Theoretical Biology 189, 107119.

    Google Scholar 

  • Shelling, T. C. [ 1971 ], Dynamic Models of Segregation, Journal of Mathematical Sociology 1, 143–186.

    Article  Google Scholar 

  • Sherrington, D. and K. Y. M. Wong [ 1989 ], Random Boolean Networks for Autoassociative Memory, Physics Reports: Review Section of Physics Letters 184, 293–299.

    MathSciNet  Google Scholar 

  • Sherrington, D. and S. Kirkpatrick [ 1975 ], Solvable Model of a Spin-Glass, Physical Review Letters 35, 1792–1796.

    Article  Google Scholar 

  • Sibani, P. and A. Pedersen [ 1999 ], Evolution Dynamics in Terraced NK Landscapes, Europhysics Letters 48, 346–352.

    Article  Google Scholar 

  • Simon, H. A. [ 1969 ], The Sciences of the Artificial. The MIT Press, Cambridge, MA.

    Google Scholar 

  • Solov, D., A. Burnetas, and M.-C. Tsai [ 1999 ], Understanding and Attenuating the Complexity Catastrophe In Kauffman’s NK Model of Genome Evolution, Complexity 5, 53–66.

    Article  MathSciNet  Google Scholar 

  • Solow, D., A. Burnetas, T. Roeder, and N. S. Greenspan [ 1999 ], Evolutionary Consequences of Selected Locus-Specific Variations In Epistasis and Fitness Contribution in Kauffman’s NK Model, Journal of Theoretical Biology 196, 181–196.

    Article  Google Scholar 

  • Somogyi, R. and C. A. Sniegoski [ 1996 ], Modeling the Complexity of Genetic Networks: Understanding Multigenetic and Pleiotropic Regulation, Complexity 1, 45–63.

    MathSciNet  Google Scholar 

  • Somogyvdri, Z. and S. Payrits [ 2000 ], Length of State Cycles of Random Boolean Networks: an Analytic Study, Journal of Physics A: Mathematical and General 33, 6699–6706.

    Article  MathSciNet  Google Scholar 

  • Stadler, P. F. and R. Happel [ 1999 ], Random Field Models for Fitness Landscapes, Journal of Mathematical Biology 38, 435–478.

    Article  MathSciNet  MATH  Google Scholar 

  • Stauffer, D. [ 1985 ], Introduction to Percolation Theory. Taylor and Francis, London.

    Book  MATH  Google Scholar 

  • Stauffer, D. [ 1994 ], Evolution By Damage Spreading in Kauffman Model, Journal of Statistical Physics 74, 1293–1299.

    Article  Google Scholar 

  • Stauffer, D. [ 1987a ], On Forcing Functions in Kauffman Random Boolean Networks, Journal of Statistical Physics 46, 789–794.

    Article  MathSciNet  Google Scholar 

  • Stauffer, D. [ 1987b ], Random Boolean Networks–Analogy With Percolation, Philosophical Magazine B: Physics of Condensed Matter, Statistical Mechanics, Electronic, Optical and Magnetic Properties 56, 901–916.

    Google Scholar 

  • Stauffer, D. [ 1988 ], Percolation Thresholds in Square-Lattice Kauffman Model, Journal of Theoretical Biolog 135, 255–261.

    Article  MathSciNet  Google Scholar 

  • Stauffer, D. [ 1989 ], Hunting for the Fractal Dimension of the Kauffman Model, Physica D 38, 341–344.

    Article  MathSciNet  Google Scholar 

  • Stauffer, D. [ 1991 ], Computer Simulations of Cellular Automata, Journal of Physics A: Mathematical and General 24, 909–927.

    Article  MathSciNet  MATH  Google Scholar 

  • Stern, M. D. [ 1999 ], Emergence of Homeostasis and Noise Imprinting in an Evolution Model, Proceedings of the National Academy of Sciences of the U.S.A. 96, 10746–10751.

    Article  Google Scholar 

  • Stölzle, S. [ 1988 ], Universality Two-Dimensional Kauffman Model for Parallel and Sequential Updating, Journal of Statistical Physics 53, 995–1004.

    Article  Google Scholar 

  • Strogatz, S. H. [ 2001 ], Exploring Complex Networks, Nature 410, 268–276.

    Article  Google Scholar 

  • Thieffry, D. and D. Romero [ 1999 ], The Modularity of Biological Regulatory Networks, Biosystems 50, 49–59.

    Article  Google Scholar 

  • Toffoli, T. and N. H. Margolus [ 1990 ], Invertible Cellular Automata: a Review, Physica D 45, 229–253.

    Google Scholar 

  • Volkert, L. G. and M. Conrad [ 1998 ], The Role of Weak Interactions in Biological Systems: the Dual Dynamics Model, Journal of Theoretical Biology 193, 287306.

    Google Scholar 

  • Waelbroeck, H. and F. Zertuche [ 1999 ], Discrete Chaos, Journal of Physics A: Mathematical and General 32, 175–189.

    Google Scholar 

  • Wang, L., E. E. Pichler, and J. Ross [ 1990 ], Oscillations and Chaos in Neural Networks an Exactly Solvable Model, Proceedings of the National Academy of Sciences of the United States of America 87, 9467–9471.

    Article  MATH  Google Scholar 

  • Weinberger, E. D. [ 1991 ], Local Properties of Kauffman NK Model–a Tunably Rugged Energy Landscape, Physical Review A 44, 6399–6413.

    Article  Google Scholar 

  • Weisbuch, G. and D. Stauffer [ 1987 ], Phase Transitions in Cellular Random Boolean Networks, Jounal De Physique 48, 11-18.

    Google Scholar 

  • Wilke, C. O., C. Ronnenwinkel, and T. Martinetz [ 2001 ], Dynamic Fitness Landscapes in Molecular Evolution, Physics Reports 349, 395–446.

    Google Scholar 

  • Wolfram, S. [ 1983 ], Statistical Mechanics of Cellular Automata, Reviews of Modern Physics 55, 601–644.

    Google Scholar 

  • Wuensche, A. [ 1999 ], Discrete Dynamical Networks and their Attractor Basins, Complexity International 6, http://www.csu.edu.au/ci/idx—volume.html.

    Google Scholar 

  • Zawidzki, T. W. [ 1998 ], Competing Models of Stability in Complex, Evolving Systems: Kauffman Vs. Simon, Biology and Philosophy 13, 541–554.

    Article  Google Scholar 

  • Zoli, M., D. Guidolin, K. Fuxe, and L. F. Agnati [ 1996 ], The Receptor Mosaic Hypothesis of the Engram: Possible Relevance of Boolean Network Modeling, International Journal of Neural Systems 7, 363–368.

    Article  Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Additional information

To Larry Sirovich, on the occasion of his 70th birthday.

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag New York, Inc.

About this chapter

Cite this chapter

Aldana, M., Coppersmith, S., Kadanoff, L.P. (2003). Boolean Dynamics with Random Couplings. In: Kaplan, E., Marsden, J.E., Sreenivasan, K.R. (eds) Perspectives and Problems in Nolinear Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21789-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-21789-5_2

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4684-9566-9

  • Online ISBN: 978-0-387-21789-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics