Memory, neural network models

The second main category of neural networks is the feedforward type. In this type of network, the signals go in only one direction there are no loops in the system as shown in Fig. 3. The earliest neural network models were linear feed forward. In 1972, two simultaneous articles independently proposed the same model for an associative memory, the linear associator. J. A. Anderson [17], neurophysiologist, and Teuvo Kohonen [18], an electrical engineer, were unaware of each other s work. Today, the most commonly used neural networks are nonlinear feed-forward models. 

Neural network model composed of formal neurons without the capacity of memory storage cannot be applicable to the study of information processing of real neural networks. 

We will now review the neural network model for memory and study how the knowledge of orthogonalization methods can help us in understanding certain cognitive functions. 

Carpenter, G. 1989. Neural network models for pattern recognition and associative memory. Neural Networks, 2 243-258. 

In previous chapters, we have examined a variety of generalized CA models, including reversible CA, coupled-map lattices, reaction-diffusion models, random Boolean networks, structurally dynamic CA and lattice gases. This chapter covers an important field that overlaps with CA neural networks. Beginning with a short historical survey, chapter 10 discusses zissociative memory and the Hopfield model, stocheistic nets, Boltzman machines, and multi-layered perceptrons. 

Lisman, J.E., Talamini, L.M., and Raffone, A. Recall of memory sequences by interaction of the dentate and CA3 A revised model of the phase precession. Neural Networks, in press, corrected proof... 

Matrix modulators or controlled transparencies could be used in data-processing systems for correction of the optical aberration in real-time images [49], as well as writing and reconstructing the holographic information [49, 50], which is a basic tool for optical pattern classification [51], modeling of neural networks [52], optical associative memory [53], phase conjugation of low-power optical beams [54], etc. 

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