The Computational Power of Continuous Time Asymmetric Neural Networks
نویسنده
چکیده
We investigate the computational power of continuous-time neural networks with Hoppeld-type units and asymmetric interconnections. We prove that polynomial-size networks with saturated-linear response functions are at least as powerful as polynomially space-bounded Turing machines .
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تاریخ انتشار 2007