@inproceedings{a8a92db8c2eb4d6e824d9505cf89fb57,
title = "Some structural complexity aspects of neural computation",
abstract = "Recent work by Siegelmann and Sontag has demonstrated that polynomial time on linear saturated recurrent neural networks equals polynomial time on standard computational models: Turing machines if the weights of the net are rationals, and nonuniform circuits if the weights are reals. Here we develop further connections between the languages recognized by such neural nets and other complexity classes. We present connections to space-bounded classes, simulation of parallel computational models such as vector machines, and a discussion of the characterizations of various nonuniform classes in terms of Kolmogorov complexity.",
author = "Balcazar, {Jose L.} and Ricard Gavalda and Siegelmann, {Hava T.} and Sontag, {Eduardo D.}",
year = "1993",
language = "American English",
isbn = "0818640715",
series = "Proceedings of the Eighth Annual Structure in Complexity Theory Conference",
publisher = "Publ by IEEE",
pages = "253--265",
editor = "Anon",
booktitle = "Proceedings of the Eighth Annual Structure in Complexity Theory Conference",
note = "Proceedings of the Eighth Annual Structure in Complexity Theory Conference ; Conference date: 18-05-1993 Through 21-05-1993",
}