Bifurcation theory methods for programming static or periodic attractors and their bifurcations in dynamic neural networks

Analytic methods of bifurcation theory are used to design algorithms for synthesis of analog neural networks with a precisely defined local vector field for pattern recognition that is created by a multiple bifurcation. For two memories thus far, static or oscillating spatial patterns can be stored, basin boundaries programmed, and the location of secondary bifurcations that introduce new attractors determined in the neighborhood of the bifurcation.<>