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<p>A means for analyzing the faulty behavior of neural networks is presented. Using an analogy between statistical physics and neural networks, a method for assessing the performance of a synchronous neural network model in the presence of faults is developed. Analytical predictions are computed using the statistical physics analogy and compared with the simulated behavior for two neuron models. An example of the analytical technique applied to an autoassociative memory is described.</p>
performance assessment; analytical predictions; faulty behavior; synchronous neural networks; statistical physics; simulated behavior; content-addressable storage; fault tolerant computing; neural nets.

B. Johnson and L. Belfore, II, "The Analysis of the Faulty Behavior of Synchronous Neural Networks," in IEEE Transactions on Computers, vol. 40, no. , pp. 1424-1429, 1991.
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