Reversal-input superposing technique for all-optical neural networks.

The proposed technique for optical neural networks can perform all the neural operations in a positive range. Bipolar weights of the neurons are represented by unipolar weights with a positive constant. By superposing the reversal inputs to the weighted sums, we can perform subtraction in a neuron by the nonlinear output function with a negative offset constant. This means that the number of processing elements needed in the proposed system is the same as that of neurons in the original neural network model. An experimental neural system is demonstrated for verification of this technique. The Hopfield model is adapted as an example of the neural networks implemented in the experimental neural system.