C++ Neural Networks and Fuzzy Logic: Preface

Short−Term Memory and Long−Term Memory

We alluded to short−term memory (STM) and long−term memory (LTM) in the previous paragraph. STM is

basically the information that is currently and perhaps temporarily being processed. It is manifested in the

patterns that the network encounters. LTM, on the other hand, is information that is already stored and is not

being currently processed. In a neural network, STM is usually characterized by patterns and LTM is

characterized by the connections’ weights. The weights determine how an input is processed in the network to

yield output. During the cycles of operation of a network, the weights may change. After convergence, they

represent LTM, as the weight levels achieved are stable.

Summary

You saw in this chapter, the C++ implementations of a simple Hopfield network and of a simple Perceptron

network. What have not been included in them is an automatic iteration and a learning algorithm. They were

not necessary for the examples that were used in this chapter to show C++ implementation, the emphasis was

on the method of implementation. In a later chapter, you will read about the learning algorithms and examples

of how to implement some of them.

Considerations in modeling a neural network are presented in this chapter along with an outline of how

Tic−Tac−Toe is used as an example of an adaptive neural network model.

C++ Neural Networks and Fuzzy Logic:Preface

Stability for a Neural Network

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You also were introduced to the following concepts: stability, plasticity, short−term memory, and long−term

memory (discussed further in later chapters). Much more can be said about them, in terms of the so−called

noise−saturation dilemma, or stability–plasticity dilemma and what research has developed to address them

(for further reading, see References).

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C++ Neural Networks and Fuzzy Logic:Preface

Stability for a Neural Network

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