The NATO workshop on Disordered Systems and Biological Organization was attended, in march 1985, by 65 scientists representing a large variety of fields: Mathematics, Computer Science, Physics and Biology. It was the purpose of this interdisciplinary workshop to shed light on the conceptual connections existing between fields of research apparently as different as: automata theory, combinatorial optimization, spin glasses and modeling of biological systems, all of them concerned with the global organization of complex systems, locally interconnected. Common to many contributions to this volume is the underlying analogy between biological systems and spin glasses: they share the same properties of stability and diversity. This is the case for instance of primary sequences of biopo Iymers I ike proteins and nucleic acids considered as the result of mutation-selection processes [P. W. Anderson, 1983] or of evolving biological species [G. Weisbuch, 1984]. Some of the most striking aspects of our cognitive apparatus, involved In learning and recognttlon [J. Hopfield, 19821, can also be described in terms of stability and diversity in a suitable configuration space. These interpretations and preoccupations merge with those of theoretical biologists like S. Kauffman [1969] (genetic networks) and of mathematicians of automata theory: the dynamics of networks of automata can be interpreted in terms of organization of a system in multiple possible attractors. The present introduction outlInes the relationships between the contributions presented at the workshop and brIefly discusses each paper in its particular scientific context.

Inhalt

1-Automata Theory.- 1 Cellular automata models of disorder and organization.- 2 Dynamics and Self organization in one-dimensional arrays.- 3 Basic results for the behaviour of discrete iterations.- 4 On some dynamical properties of monotone networks.- 5 Inhomogeneous cellular automata (INCA).- 6 The Ising model and the Rudin Shapiro sequence.- 7 Dynamical properties of an automaton with memory.- 8 Dynamics of random boolean networks.- 9 Random fields and spatial renewal potentials.- 10 Lyapunov functions and their use in automata networks.- 11 Positive automata networks.- 12 Directional entropies of cellular automaton-maps.- 2- Physical Disordered Systems.- 13 On the statistical physics of spin glasses.- 14 Symbolic computation methods for some spin glasses problems.- 15 Order and defects in geometrically frustrated systems.- 3- Formal Neural Networks.- 16 Collective computation with continuous variables.- 17 Collective properties of neural networks.- 18 Determining the dynamic landscape of Hopfield networks.- 19 High resolution micro-fabrication and neural networks.- 20 Ultrametricity, Hopfield model and all that.- 21 The emergence of hierarchical data structures in parallel computation.- 22 Cognitive capabilities of a parallel system.- 23 Neural network design for efficient information retrieval.- 24 Learning process in an asymmetric threshold network.- 25 Layered networks for unsupervised learning.- 26 Statistical coding and short-term synaptic plasticity: a scheme for knowledge representation in the brain.- 27 A physiological neural network as an autoassociative memory.- 4- Combinatorial Optimization.- 28 Configuration space analysis for optimization problems.- 29 Statistical mechanics: a general approach to combinatorial optimization.- 30 Bayesian image analysis.- 31 The Langevin equation as a global minimization algorithm.- 32 Spin glass and pseudo boolean optimization.- 33 Local versus global minima, hysteresis, multiple meanings.- 5- Models of Biological Organization.- 34 Boolean systems, adaptive automata, evolution.- 35 Invariant cycles in the random mapping of n integers onto themselves. Comparison with Kauffman binary network.- 36 Fibroblasts, morphogenesis and cellular automata.- 37 Percolation and frustration in neural networks.- 38 Self organizing mathematical models: non linear evolution equations with a convolution term.- 39 Recurrent collateral inhibition simulated in a simple neuronal automata assembly.- 40 Cerebellum models: an interpretation of some features.