Optical neural networks

That SPAs can have both electrical I/O as well as optical I/O renders them potentially useful to any application that interfaces between optical and electronic realms. Such applications are more numerous than already discussed in this section. The emerging SPA technology will likely result in new applications emerging for years to come. Some additional applications not previously mentioned are the parallel-processing planes of pipeline architectures (useful for pixel-level image processing) the various levels of optical neural networks the input, filter, and/or output planes of optical correlators and optical phased-array beam steering. 

The two components (SASLM 1 and SASLM 2) can now be combined with CGHs to route (fan-out) the light through the optical neural network, as in Fig. 64. SASLM 1 is used as the neurons and SASLM 2 makes up the synaptic weights. The system is almost entirely optical and the advantages are inherent compared to electronic neural nets, as the interconnections are total and performed in free space. The problem is that for n SASLM 1 neurons, SASLM 2 weighting smart pixels are required, which limits the processing power of the neural net due to VLSI constraints [75]. 

Since 1970 the subject of amoiphous semiconductors, in particular silicon, has progressed from obscurity to product commercialisation such as flat-panel hquid crystal displays, linear sensor arrays for facsimile machines, inexpensive solar panels, electrophotography, etc. Many other appHcations are at the developmental stage such as nuclear particle detectors, medical imaging, spatial light modulators for optical computing, and switches in neural networks (1,2). 

Artificial Neural Networks (ANNs) attempt to emulate their biological counterparts. McCulloch and Pitts (1943) proposed a simple model of a neuron, and Hebb (1949) described a technique which became known as Hebbian learning. Rosenblatt (1961), devised a single layer of neurons, called a Perceptron, that was used for optical pattern recognition. 

M.N. Tib and R. Narayanaswamy, Multichannel calibration technique for optical-fibre chemical sensor using artificial neural network. Sensors Actuators, B39 (1997) 365-370. 

Over the last several years, the number of studies on application of artificial neural network for solving modeling problems in analytical chemistry and especially in optical fibre chemical sensor technology, has increase substantially69. The constructed sensors (e.g. the optical fibre pH sensor based on bromophenol blue immobilized in silica sol-gel film) are evaluated with respect to prediction of error of the artificial neural network, reproducibility, repeatability, photostability, response time and effect of ionic strength of the buffer solution on the sensor response. 

Suah F.B.M., Ahmad M., Taib M.N., Applications of artificial neural network on signal processing of optical fibre pH sensor based on bromophenol blue doped with sol-gel film, Sens. Actuat B 2003 90 182-188. 

Optical Storage and Retrieval Memory, Neural Networks, and Fractals, edited by Francis T. S. Yu and Suganda Jutamulia... 

L. Ralbinot, P. Smichowski, S. Farias, M. A. Z. Arruda, C. Vodopivez and R. J. Poppi, Classification of Antarctic algae by applying Kohonen neural network with 14 elements determined by inductively coupled plasma optical emission spectrometry, Spectrochim. Acta, Part B, 60(5), 2005, 725-730. 

Empirical methods such as neural networking can be used in place of optical methods to estimate the size distribution of concentrated suspensions. The method determines particle size distribution and... 

There are other applications of photorefractive materials that have been investigated, including associative optical memories that identify a clear image from a corrupted input [1], novelty filters to detect only changing features in an image [2], and neural networking in analogy with the human brain [3],... 

Kawata, Y., Tanaka, T., and Kawata, S. Optical Memory and Neural Networks 1999, 8, 1. Kano, H., Wada, K., and Kawata, S. Extended Abstracts of the 43rd Spring Meeting of the Japan Society of Applied Physics and Related Societies, 1996, p. 886. 

Lyons, W. B. Lewis, E. Neural Networks and Pattern Recognition Techniques Applied to Optical Fibre Sensors. Trans. Inst. Measurm. Control 2000, 22, 385 104... 

---