Acoustic signal recognition with a photorefractive time-delay neural network

Optical finite impulse response neural networks

The finite impulse response neural network is described in detail. Different algorithms capable of temporal back-propagation are considered, including a novel modification to the conventional algorithm, called the delayed-feedback back-propagation algorithm. We present and analyze different optoelectronic processors making use of adaptive volume holograms and three-dimensional optical processing. Two single-layer architectures are presented: the input delay plane architecture and the output delay plane architecture. By combining them it is possible to implement both forward and backward propagation in two multi-layer architectures: the first making use of the conventional temporal back-propagation and the second making use of delayed-feedback back-propagation.

Optical associative memory with bipolar edge-enhanced learning that uses a binary spatial light modulator and a BaTiO(3) crystal

An optical associative memory with bipolar edge-enhanced feature learning that uses a ferroelectric liquid-crystal spatial light modulator and a barium titanate crystal is presented. During the learning procedure the bipolar edge-enhanced versions of the patterns are employed, which enable the associative memory to have a high discrimination capability. Experimental results and computer simulations are given.

Self-organized learning of purely temporal information in a photorefractive optical resonator

A photorefractive resonator containing an optical delay line is shown to learn temporal information through a self-organization process. We present experiments in which a resonator mode selectively learns the mostfrequently presented signals at the input. We also demonstrate the self-organized association of two different analog signals with two different resonator modes.