Dynamics of Learning in Recurrent Feature-Discovery Networks

Part of Advances in Neural Information Processing Systems 3 (NIPS 1990)

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Todd Leen


The self-organization of recurrent feature-discovery networks is studied from the perspective of dynamical systems. Bifurcation theory reveals pa(cid:173) rameter regimes in which multiple equilibria or limit cycles coexist with the equilibrium at which the networks perform principal component analysis.