Attractor dynamics in feedforward neural networks.
We study the probabilistic generative models parameterized by feedforward neural networks. An attractor dynamics for probabilistic inference in these models is derived from a mean field approximation for large, layered sigmoidal networks. Fixed points of the dynamics correspond to solutions of the mean field equations, which relate the statistics of each unit to those of its Markov blanket. We establish global convergence of the dynamics by providing a Lyapunov function and show that the dynamics generate the signals required for unsupervised learning. Our results for feedforward networks provide a counterpart to those of Cohen-Grossberg and Hopfield for symmetric networks.
AT&T Labs--Research, Florham Park, NJ 07932, USA.
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LK
Saul
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Jordan
D001499:Bayes Theorem; D005246:Feedback; D008390:Markov Chains; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics