Modeling of fault tolerance in neural networks

The authors present an analytical technique for assessing the fault tolerance of neural networks. The basis of the technique is developed through an analogy with magnetic spin systems using statistical mechanics. It is shown that neural networks can be analyzed using statistical mechanics. Simulated results are compared with analytical results, showing that the analytical model does indeed conform to the simulation model. The primary example presented is an associative memory.<>