Boolean Systems, Adaptive Automata, Evolution

  • Stuart A. Kauffman
Conference paper
Part of the NATO ASI Series book series (volume 20)


The past decade has seen renewed interest in non Von Neuman computation by parallel processing systems. This interest on the part of solid state physicists and others has led to models of pattern recognition and associative memory (1,2,3). In these models, it is largely the dynamical attractors which are of interest as the classes, or memories, stored in the systems. Further, the mathematical tractability of threshold systems with symmetric coupling, that is, in which each binary device “fires” if a weighted sum of excitation minus inhibition exceeds some threshold, and couplings between two binary devices are symmetrical, has focused particular attention on this subclass of automata. The marked advantage of this subclass of automata is the existence of a potential function allowing prescription of weightings on inputs to each binary device in order to choose steady state attractors with desired properties such as location in state space, and stability to perturbation (1,2,3).


Boolean Function Cellular Automaton Cycle Length Binary Device State Cycle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1986

Authors and Affiliations

  • Stuart A. Kauffman
    • 1
  1. 1.Department of Biochemistry and BiophysicsUniversity of Pennsylvania School of MedicinePennsylvaniaUSA

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