Leading Edge Computer Science Research
The books in this series present leading-edge research in the field of computer research, technology and applications. Each contribution has been carefully selected for inclusion based on the significance of the research to the field. Summaries of all chapters are gathered at the beginning of the book and an in-depth index is presented to facilitate access.
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Foundations of the Neural Network Assembly Memory Model
An Abstract Programming System
Cooperate or Compete A Simple Agent Model for Iterated Arbitrary 2Player Games
Selection of Future Events from a Time Series in Relation to Estimations of Forecasting Uncertainty
abstract program algebraic covariant derivative assembly memory average axiom binary bits brain CFRule model chromosomes components convergence convolutional correct corresponding covariant derivative covariant derivative curvature crossover curvature tensors curves data variable decoding algorithm defined denotes derivative curvature tensors deterministic discrete Fourier transforms Equation error example exit-layer neuron feature vectors Figure forecasts formula function GA's gene genetic algorithms genotype Gray codes group ring Hamming distance Hoare logic idempotents initial input LDNNs logic logic programming matrix memory model memory retrieval memory trace method mutation neural network neurons NN memory NNAMM noise operations optimization output parameters particular AMU pattern payoff PL(L pmut population possible predictable events probability problem procedure definition procedure variable proof quicksort right ideals ROC curves Section semantics sequence side effects sparsely coded specific spikes symmetry classes synchronous target rules Theorem theory transformation values vectors x(d Young symmetrizer