Neural Networks, Pattern Recognition, and Fingerprint Hallucination

Abstract

Many interesting and globally ordered patterns of behavior, such as solidification, arise in statistical physics and are generally referred to as collective phenomena. The obvious analogies to parallel computation can be extended quite far, so that simple computations may be endowed with the most desirable properties of collective phenomena: robustness against circuit defects, extreme parallelism, asynchronous operation and efficient implementation in silicon. To obtain these advantages for more complicated and useful computations, the relatively simple pattern recognition task of fingerprint identification has been selected. Simulations show that an intuitively understandable neural network can generate fingerprint-like patterns within a framework which should allow control of wire length and scale invariance. The purpose of generating such patterns is to create a network whose stable states are noiseless fingerprint patterns, so that noisy fingerprint patterns used as input to the network will evoke the corresponding noiseless patterns as output. There is a developing theory for predicting the behavior of such networks and thereby reducing the amount of simulation that must be done to design them.