Covariance phasor neural network

We present a phase covariance model that can well represent stimulus intensity as well as feature binding (i.e., covariance). The model is represented by complex neural equations, which is a mean field model of stochastic neural model such as Boltzman machine and sigmoid belief networks. The 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 avarage activation. The covariance model could give an elaborate mean field approximation in that to calculate higer moments we have to invoke a higher order mean field model. A covariance Hebbian self-organizing rule and Boltzman learning rule are then investigated on this model.