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History of Neural Computing

History of Neural Computing. McCulloch - Pitts 1943 - showed that a ”neural network” with simple logical units computes any computable function - beginning of Neural Computing, Artificial Intelligence, and Automaton Theory Wiener 1948

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History of Neural Computing

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  1. History of Neural Computing • McCulloch - Pitts 1943 - showed that a ”neural network” with simple logical units computes any computable function - beginning of Neural Computing, Artificial Intelligence, and Automaton Theory • Wiener 1948 - Cybernetics, first time statistical mechanics model for computing - - compare Hopfield 1982 • Hebb 1949 - physiological learning rule based on the synaptic modification, Hebbian learning - - repeated synaptic activity strenghen synaptic response

  2. Marvin Minsky 1954 - ”Neural - anlog system & brain model” Ph.D. thesis at Princeton - An article ”Toward AI” in 1961, a chapter ”Neural Computing” - The book ”Computation: Finite and infinite machines” transform McCulloch - Pitts results into Automaton theory • Gabor 1954 - nonlinear adaptive filter • Taylor 1956 - associative memory -> learning matrix - also early works for correlation matrix memory (Anderson 1972, Kohonen 1972, Nakano 1972)

  3. PERCEPTRON

  4. PERCEPTRON • Rosenblatt 1958 - a new method for supervised learning ”perceptron convergence theorem” • Widrow - Hoff 1960 - LMS-algorithm for learning Adaline • Widrow 1962 - Madaline: leyered neural networks • Amari 1967 - stochastic gradient method • Nilsson 1965 - linearly separable sets During golden era of Perceptrons, in 60’s, it was believed that they solve all problems.

  5. PERCEPTRON • Minsky - Papert 1969 - the book ”Perceptrons” - showed mathematically the restrictions of 1-leyer perceptrons - they doubted that more leyers do not bring essentially more power Neural Network research went into ”HALT” state • The research was low about ten years - reasons: low computing power psychologically math results • research was continued in neurosciences and in psychology

  6. Self-Organizing Maps • This reseach was continued during ”Perceptron-halt” • von der Malsburg 1973 - first demonstration of self-organization - first paper was inspired by topological maps in brain • Grossberg 1980 - a new form of self-organization; ART • Kohonen 1982 - 1 and 2 dimensional lattice, different to von der Malsburg - nowadays a benchmark SOM

  7. Self-Organizing Maps

  8. Hopfield networks • Hopfield 1982 - formulation of an energy function for understanding how attraction network work - popular in 80’s: feedback Neural Net = Hopfield Net - no neurophysiologically adequate, but interesting since information could be stored into a stable net • Paper triggered a new era of Neural Networks • Paper caused much controversy, there were similar ideas in the literature: Cragg-Tamperley (1954), Cowan (1967), Grossberg (1967)

  9. New rise of NN • Kirkpatrick - Gelatt - Vecchi 1983 - Simulated annealing for combinatorial optimization problem - idea from statistical mechanics model for cooling in crystal formation • Ackley - Hinton - Sejnowski 1985 - Bolzmann machine, first succeeded realization of multileyer network --> earlier psychological barrier was broken • Barto - Sutton - Anderson 1983 - reinforcement learning, balance of a broomstick

  10. MULTILEYER PERCEPTRON

  11. (Error) Back Propagation • Problem in multileyer perceptron network: How to update the weights? • Rumelhart - Hinton - Williams 1986 - The book: Parallel Distributed Processing - back propagation algorithm solve problem - most popular learning algorithm for MLP’s • found also by Parker 1985, LeCun 1985 • earlier by Werbos 1974 (Bryson-Ho 1969)

  12. MULTILEYER PERCEPTRON

  13. Latest additions • Broomhead - Lowe 1988 - Radial basis functions (RBF) - input leyer : nonlinear hidden leyer : linear output leyer - link neural networks to numerical analysis • Linsker 1988 - self organization in perceptual networks - triggered again interest of information theorists • Bell - Sejnowski 1995 - blind source separation

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