The dense interconnections that characterize neural networks are most readily implemented
using optical signal processing. Optoelectronic'neurons' fabricated from semiconducting …

Deep neural networks (DNNs) are used to classify and reconstruct the input images from the
intensity of the speckle patterns that result after the inputs are propagated through …

While the present edition is bibliographically the third one of Vol. 8 of the Springer Series in
Information Sciences (IS 8), the book actually stems from Vol. 17 of the series …

Photorefractive materials combine photoconductive and electro-optic properties: light affects
their electrical conductivity; their optical properties (refractive index, etc.) are affected by …

The capabilities of photorefractive crystals as media for holographic interconnections in
neural networks are examined. Limitations on the density of interconnections and the …

The Handbook of Neural Computation is a practical, hands-on guide to the design and
implementation of neural networks used by scientists and engineers to tackle difficult and/or …

This paper reviews optical architectures for the implementation of hybrid neural networks.
Optics is mainly applied to implementing the matrix-vector or tensor-matrix multiplication. In …

The properties of higher order memories are described. The non-redundant, up to Nth order
polynomial expansion of N-dimensional binary vectors is shown to yield orthogonal feature …

An optical network is described that is capable of recognizing at standard video rates the
identity of faces for which it has been trained. The faces are presented under a wide variety …

Final Report To NSF of the Planning Workshop on Facial Expression Understanding Page 1
Final Report To NSF of the Planning Workshop on Facial Expression Understanding Title/Contents …