Covariance phasor neural network as a mean field model
Haruhisa Takahashi · 2004
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.