## 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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### Contents

Connectionist Learning through Gradient Following R J Williams | 3 |

Efficient Stochastic Gradient Learning Algorithm for Neural | 27 |

Information Storage in Fully Connected Networks | 51 |

Neuronic Equations and their Solutions E R Caianiello | 91 |

The Dynamics of Searches Directed by Genetic Algorithms | 111 |

Probabilistic Neural Networks J W Clark | 129 |

Some Quantitative Issues in the Theory of Perception A Zee | 183 |

Speech Perception and Production by a SelfOrganizing Neural Network | 217 |

Learning to Predict the Secondary Structure of Globular Proteins | 257 |

Exploiting Chaos to Predict the Future and Reduce Noise | 277 |

Contents | 279 |

Scaling of Error Estimates | 296 |

Experimental Data Analysis | 314 |

Adaptive Dynamics | 322 |

How Neural Nets Work A Lapedes R Farber | 331 |

A Neural Network Model for Visual Pattern | 233 |

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### Common terms and phrases

accuracy applied approach associative memory assume attractor auditory average back-propagation ball scaling Barto behavior binary cell cell-plane chaos chaotic cognitive connectionist connections corresponding data points defined denote depends deterministic dimensional direct forecasts discussed distribution dynamical systems eigenvalues equation error estimates example feature Figure fractal dimension function given higher order Hopfield hyperplane IEEE input pattern iterated forecasts k-d tree Lapedes learning algorithm linear Lyapunov exponents matrix methods neighborhoods neocognitron neural net neural nets neural networks neurons noise nonlinear number of data optimal order of approximation outer product outer-product output parameters partition pattern recognition perceptron performance phoneme polynomial prediction presynaptic probability problem procedure properties proteins random reinforcement learning representation Rumelhart S-cell scaling scheme sequence signal space spectral stable statistical stochastic storage structure supervised learning synaptic synchronous techniques theorem theory threshold training set trajectory units variables vector weights