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Computing Time Lower Bounds for Recurrent Sigmoidal Neural Networks

By Michael Schmitt

Abstract

Recurrent neural networks of analog units are computers for realvalued 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

Topics: mschmittlmi. ruhr-uni-bochum. de
Year: 2001
OAI identifier: oai:CiteSeerX.psu:10.1.1.19.7893
Provided by: CiteSeerX
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