SYMMETRIES IN NEURAL NETWORKS - A LINEAR GROUP ACTION APPROACH

Neural networks storing Hadamard patterns have been completely classif ied with respect to permutation symmetry. The symmetry group of the Ha damard patterns is found to be isomorphic to GL(n, F2), and the symmet ry groups of the networks are explicitly constructed for the most impo rtant classes. The volumes of different equivalence classes have been calculated.