The model of a simple perceptron using phaseencoded inputs and complex-valued weights is proposed. The aggregation function, activation function, and learning rule for the proposed neuron are derived and applied to Boolean logic functions. The complex-valued neuron (CVN) is shown to be superior to traditional perceptrons. Optical and analog circuit implementations are discussed and the CVN is shown to be very attractive for optical implementation since optical computations are naturally complex. The cost of the CVN is less in all cases than the traditional neuron when implemented optically. However, on those implementations dependent on standard serial computers, CVN will be more cost effective only in those applications where its increased power can offset the requirement for additional neurons.

The model of a simple perceptron using phase-encoded input and complex-valued weights is proposed. The aggregation function, activation function, and learning rule for the proposed neuron are derived and applied to two and three input Boolean logic functions. An improvement of 135% over the theoretical maximum of 104 linearly separable problems (of three variables) solvable by conventional perceptrons is achieved without additional logic, neuron stages, or higher order terms such as those required in polynomial logic gates. Such a network is very attractive for optical implementation since optical computations are naturally complex.

Two primary applications of display devices are reviewed: display for visualization and spatial light modulation. Important static and dynamic display parameters are discussed. Matrix computations and programmable logic array applications are highlighted.