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 the 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 average activation. The covariance model could give an elaborate mean field approximation, and to calculate higher moments we have to invoke a higher order mean field model. The covariance Hebbian self-organizing rule and Boltzman learning rule are then investigated on this model