Path: utzoo!utgpu!water!watmath!clyde!att!osu-cis!tut.cis.ohio-state.edu!accelerator.eng.ohio-state.edu!lepton.eng.ohio-state.edu!borgstrm From: borgstrm@lepton.eng.ohio-state.edu (Tom Borgstrom) Newsgroups: comp.ai.neural-nets Subject: Analog Vs. Digital Weights Keywords: Neural Nets, synaptic weights Message-ID: <646@accelerator.eng.ohio-state.edu> Date: 25 Sep 88 21:13:25 GMT Sender: news@accelerator.eng.ohio-state.edu Reply-To: borgstrm@icarus.eng.ohio-state.edu (Tom Borgstrom) Organization: The Ohio State University Dept of Electrical Engineering Lines: 20 I am interested in finding performance/capacity comparisons between neural networks that use discrete synaptic weights and those that use continuous valued weights. I have one reference: "The Capacity of the Hopfield Associative Memory", by R.J. McEliece, E.C. Posner, et al.; IEEE Transactions on Information Theory, Vol. IT-33, No. 4, July 1987. The authors claim to "only lose 19 percent of capacity by ... three level quantization." Is this true? Has anyone else done hardware/software simulations to verify this? Please reply by e-mail; I will post a summary if there is a large enough response. -=- Tom Borgstrom |borgstrm@icarus.eng.ohio-state.edu The Ohio State University|...!osu-cis!tut!icarus.eng.ohio-state.edu!borgstrm 2015 Neil Avenue | Columbus, Ohio 43210 |