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Recurrent neural networks of analog units are computers for
real-valued functions. We study the time complexity of real
computation in general recurrent neural networks. These have
sigmoidal, linear, and product units of unlimited order as nodes and
no restrictions on the weights. For networks operating in discrete
time, we exhibit a family of functions with arbitrarily high
complexity, and we derive almost tight bounds on the time required
to compute these functions. Thus, evidence is given of the
computational limitations that time-bounded analog recurrent neural
networks are subject to.
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