## Code Recognition and Set Selection with Neural Networks |

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### Contents

Chapter 1Neural Networks as Dynamical Systems | 9 |

Chapter 2Hypergraphs and Neural Networks | 33 |

Chapter 3The Memory Model | 53 |

Copyright | |

8 other sections not shown

### Common terms and phrases

Aa(g algebraic analog answer set approach a constant associated attractor region barycenter binary strings block cell Chapter code word column constant attractor trajectories content addressable memory convergence cyclic trajectory define difference equation dVdt dx^dt dxi/dt eigenvalues equation dynamical system error-correcting code example finite foundation function gain function Hadamard code Hamming distance high order neural hypergraph image products input iteration level cylinder level sets limit cycle linear approximation matrix loop mathematical memory model n-cube n-dimensional space n-space neural network model nonconstant trajectory order neural network orthant containing orthant relative permutation matrix points positive constant positive number PROOF quadratic assignment problem ramp function real numbers region of attraction set selection model set selection problem shown in Fig simulations solution spreadsheet stable constant trajectory steps subsets summand Suppose system functions trajectory for 1.1 trajectory starting trajectory which starts two-dimensional Typical trajectories vector vertex vertices