Dynamics of Learning in Recurrent Feature-Discovery Networks
–Neural Information Processing Systems
The self-organization of recurrent feature-discovery networks is studied from the perspective of dynamical systems. Bifurcation theory reveals parameter regimes in which multiple equilibria or limit cycles coexist with the equilibrium at which the networks perform principal component analysis.
Neural Information Processing Systems
Dec-31-1991