Abstract: A neural network layer (1) is made up of nodes or neurons which each comprise a pair of physically separate and optically coupled sub-units (X.sub.1, Y.sub.1). One sub-unit broadcasts excitatory and receives inhibitory signals, whereas the other sub-unit broadcasts inhibitory and receives the excitatory signals. An electrical feedback connection (20) is provided between corresponding sub-units for determination of net node activation. Diffractive or holographic optical elements (2) are used for optical coupling.

Abstract: An optical system of an optical neural network model for parallel data processing is disclosed. Taking advantage of the fact that an auto-correlation matrix is symmetric with respect to a main diagonal and the weights for modulating the values of diagonals of the auto-correlation matrix are equal to each other, the configuration of an optical modulation unit is simplified by a one-dimensional modulation array on the one hand, and both positive and negative weights are capable of being computed at the same time on the other hand. In particular, the optical system makes up a second-order neural network exhibiting invariant characteristics against the translation and scale.

Abstract: An optical system of an optical neural network model for parallel data processing is disclosed. Taking advantage of the fact that an auto-correlation matrix is symmetric with respect to a main diagonal and the weights for modulating the values of diagonals of the auto-correlation matrix are equal to each other, the configuration of an optical modulation unit is simplified by a one-dimensional modulation array on the one hand, and both positive and negative weights are capable of being computed at the same time on the other hand. In particular, the optical system makes up a second-order neural network exhibiting invariant characteristics against the translation and scale.

Abstract: An optical system of an optical neural network model for parallel data processing is disclosed. Taking advantage of the fact that an auto-correlation matrix is symmetric with respect to a main diagonal and the weights for modulating the values of diagonals of the auto-correlation matrix are equal to each other, the configuration of an optical modulation unit is simplified by a one-dimensional modulation array on the one hand, and both positive and negative weights are capable of being computed at the same time on the other hand. In particular, the optical system makes up a second-order neural network exhibiting invariant characteristics against the translation and scale.