Covariance phasor neural network as a mean field model

Covariance model can represent covariance between two units of stochastic machines as cosine of the phase difference. This enables us to calculate the covariance between two units in a deterministic manner as well as average activation. The covariance model could give an elaborate mean field approximation without invoking a higher order mean field model. A covariance Hebbian self organizing rule and Boltzmann learning rule are then investigated on this model.