A Generative Model for Attractor Dynamics

However, designing a net to have a given set of attractors is notoriously tricky; training procedures are CPU intensive and often produce spurious afuactors andill-conditioned attractor basins. These difficulties occur because each connection in the network participates in the encoding ofmultiple attractors. We describe an alternative formulation of attractor networks in which the encoding of knowledge is local, not distributed. Although localist attractor networks have similar dynamics to their distributed counterparts, they are much easier to work with and interpret. Attractor networks map an input space, usually continuous, to a sparse output space composed of a discrete set of alternatives.