A Bifurcation Theory Approach to the Programming of Periodic Attractors in Network Models of Olfactory Cortex

A new learning algorithm for the storage of static and periodic attractors in biologically inspired recurrent analog neural networks is introduced. For a network of n nodes, n static or n/2 periodic attractors may be stored. The algorithm allows programming of the network vector field indepen(cid:173) dent of the patterns to be stored. Stability of patterns, basin geometry, and rates of convergence may be controlled. For orthonormal patterns, the l grning operation reduces to a kind of periodic outer product rule that allows local, additive, commutative, incremental learning.