We describe a linear network that models correlations between real-valued visible variables using one or more real-valued hidden variables—a factor analysis model. This model can be seen as a linear version of the Helmholtz machine, and its parameters can be learned using the wake sleep method, in which learning of the primary generative model is as sisted by a recognition model, whose role is to fill in the values of hidden variables based on the values of visible variables. The generative and recognition models are jointly learned in wake and sleep phases, using just the delta rule. This learning procedure is comparable in simplicity to Hebbian learning, which produces a somewhat different representation of correlations in terms of principal components. We argue that the simplicity of wake-sleep learning makes factor analysis a plausible alternative to Hebbian learning as a model of activity-dependent cortical plasticity.

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