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To address the issue of scaling, we may need to learn how to combine small networks and to place them under the control of other networks. Of course, a "small" network in the brain challenges our current simulation capabilities, so we do not know exactly what the limitations are. The technology, although over 30 years old at this writing, is still emerging and deserves close scrutiny. We should always be aware of both the strengths and the limitations of our tools. 3 ANS SIMULATION We will now consider several techniques for simulating ANS processing models using conventional programming methodologies.

This philosophy carries through the remainder of the text as well, specifically in the sections in each chapter that describe how to implement the learning algorithms for the network being discussed. 5 We hope that you will have little difficulty translating our simulator algorithms to your own preferrred data structures and programming languages. Part of the purpose of this text, however, is to illustrate the design of the algorithms needed to construct simulators for the various neural-network models we shall present.

D. Malsburg, editors, Neural Computers, pages 445-454. Springer-Verlag, 1988. NATO ASI Series F: Computers and System Sciences Vol. 41. [12] Robert Hecht-Nielsen. Neurocomputing. Addison-Wesley, Reading, MA, 1990. [13] Geoffrey E. Hinton and James A. Anderson, editors. Parallel Models of Associative Memory. Lawrence Erlbaum Associates, Hillsdale, NJ, 1981. [14]Willian Y. Huang and Richard P. Lippmann. Comparison between neural net and conventional classifiers. In Proceedings of the IEEE First International Conference on Neural Networks, San Diego, CA, pp.

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