## Evolution, Learning And CognitionThis review volume represents the first attempt to provide a comprehensive overview of this exciting and rapidly evolving development. The book comprises specially commissioned articles by leading researchers in the areas of neural networks and connectionist systems, classifier systems, adaptive network systems, genetic algorithm, cellular automata, artificial immune systems, evolutionary genetics, cognitive science, optical computing, combinatorial optimization, and cybernetics. |

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approach approximation associative memory assume attractor auditory average back-propagation ball scaling Barto behavior binary brain cell cell-plane chaotic cognitive connectionism connectionist connections content-addressable memory convergence rate corresponding defined denote depends deterministic dimension dimensional discussed distribution dynamics eigenvalues equation error estimate example feature Figure firing function given Hence hidden layers Hopfield hyperplane input pattern iterated forecasts learning algorithm lemma linear Little model Lyapunov exponents matrix methods neighborhoods neocognitron neural net neural nets neural networks neurons noise nonlinear optimal order of approximation orthogonal outer-product output parameters pattern recognition perceptron performance phoneme points polynomial prediction presynaptic probability procedure processing properties proteins random reinforcement learning representation Rumelhart S-cell scheme Sejnowski sequence signal simulations space spectral stable statistical stochastic storage strategy supervised learning symmetric synaptic synchronous techniques theorem theory thermodynamic thermodynamic equilibrium threshold training set trajectory updating variables vector visual system weights