Disordered Systems and Biological Organization

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.

[1]  E. B. Wilson Mathematics and Statistics , 1930 .

[2]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[3]  J. von Neumann,et al.  Probabilistic Logic and the Synthesis of Reliable Organisms from Unreliable Components , 1956 .

[4]  Frank H. Sumner,et al.  Reliable computation in the presence of noise , 1965 .

[5]  B. Katz Nerve, Muscle and Synapse , 1966 .

[6]  C. H. WADDINGTON,et al.  Towards a Theoretical Biology , 1968, Nature.

[7]  K. Akert,et al.  The cerebellum as a neuronal machine , 1969 .

[8]  J.A. Anderson Two models for memory organization using interacting traces , 1970 .

[9]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[10]  Teuvo Kohonen,et al.  Representation of Associated Data by Matrix Operators , 1973, IEEE Transactions on Computers.

[11]  Edsger W. Dijkstra,et al.  Self-stabilizing systems in spite of distributed control , 1974, CACM.


[13]  C. Malsburg,et al.  How patterned neural connections can be set up by self-organization , 1976, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[14]  Allen R. Hanson,et al.  Computer Vision Systems , 1978 .

[15]  René Thomas,et al.  Kinetic logic : a Boolean approach to the analysis of complex regulatory systems : proceedings of the EMBO course "Formal analysis of genetic regulation," held in Brussels, September 6-16, 1977 , 1979 .

[16]  P. J. Davis,et al.  Nonanalytic aspects on mathematics and their implication on research and education , 1979 .

[17]  S. Kauffman Assessing the Probable Regulatory Structures and Dynamics of the Metazoan Genome , 1979 .

[18]  V. Bryksin,et al.  Some exact results for the 2D Ising model with regular disposition of the frustrated squares , 1980 .

[19]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

[20]  H. Keng,et al.  Applications of number theory to numerical analysis , 1981 .

[21]  Professor Moshe Abeles,et al.  Local Cortical Circuits , 1982, Studies of Brain Function.

[22]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[23]  G. Edelman Group selection and phasic reentrant signaling a theory of higher brain function , 1982 .

[24]  D. Mastronarde Interactions between ganglion cells in cat retina. , 1983, Journal of neurophysiology.

[25]  Geoffrey E. Hinton,et al.  OPTIMAL PERCEPTUAL INFERENCE , 1983 .


[27]  G. Toulouse Frustration and disorder new problems in statistical mechanics spin glasses in a historical perspective , 1983 .

[28]  Steven W. Zucker,et al.  On the Foundations of Relaxation Labeling Processes , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  S. Wolfram Statistical mechanics of cellular automata , 1983 .

[30]  W. F. Wolff,et al.  Spin glasses and frustration models: analytical results , 1983 .

[31]  T. Poggio,et al.  A theoretical analysis of electrical properties of spines , 1983, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[32]  J. Schmidt,et al.  Activity sharpens the map during the regeneration of the retinotectal projection in goldfish , 1983, Brain Research.

[33]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[34]  J.A. Anderson,et al.  Theory of categorization based on distributed memory storage. , 1984 .

[35]  J. Marroquín Surface Reconstruction Preserving Discontinuities , 1984 .

[36]  Azriel Rosenfeld,et al.  Multiresolution image processing and analysis , 1984 .

[37]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  J. Hopfield Neurons withgraded response havecollective computational properties likethoseoftwo-state neurons , 1984 .

[39]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[40]  Xerox Palo Dynamics of Self-Organization in Complex Adaptive Networks , 1984 .

[41]  C. Malsburg Nervous Structures with Dynamical Links , 1985 .

[42]  Alan H. Kawamoto,et al.  Dynamic processes in the (re)solution of lexical ambiguity , 1985 .

[43]  B. Gidas Nonstationary Markov chains and convergence of the annealing algorithm , 1985 .

[44]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[45]  Sompolinsky,et al.  Spin-glass models of neural networks. , 1985, Physical review. A, General physics.

[46]  C. von der Malsburg,et al.  Algorithms, brain and organization. , 1985 .

[47]  J. Miller,et al.  Synaptic amplification by active membrane in dendritic spines , 1985, Brain Research.

[48]  W. Kinzel Learning and pattern recognition in spin glass models , 1985 .

[49]  J. Besag On the Statistical Analysis of Dirty Pictures , 1986 .


[51]  David B. Cooper,et al.  Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Noncausal Markovian Random Fields , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[52]  D. Amit,et al.  Spin-glass models of neural networks , 1987 .

[53]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[54]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[55]  Jerome A. Feldman,et al.  Connectionist Models and Their Properties , 1982, Cogn. Sci..

[56]  Bruce E. Hajek,et al.  Cooling Schedules for Optimal Annealing , 1988, Math. Oper. Res..