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One of the prilnary advantages of artificial neural networks is their inherent ability to perform massively parallel, nonlinear signal processing. However, if such networks must operate in a concurrently asynchronous regime, a phenomenon referred to as “computational chaos” may occur. which impedes the efficient retrieval of information usually stored in the system's attractors. In this paper. we characterize the computational chaos occurring in a widely used neural network model by estimating the complete Lyapunov spectrum associated with its dynamics. We also provide conditions that prevent the emergence of computational chaos in such concurrently asynchronous neural networks |
