Building a Neural Computer

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dc.contributor.author	Carreira, Paulo J.F.	por
dc.contributor.author	Rosa, Miguel A.	por
dc.contributor.author	Neto, João Pedro	por
dc.contributor.author	Costa, José Félix	por
dc.date.accessioned	2009-02-10T13:11:46Z	por
dc.date.accessioned	2014-11-14T16:24:10Z
dc.date.available	2009-02-10T13:11:46Z	por
dc.date.available	2014-11-14T16:24:10Z
dc.date.issued	1998-12	por
dc.description.abstract	In the work of [Siegelmann 95] it was showed that Artificial Recursive Neural Networks have the same computing power as Turing machines. A Turing machine can be programmed in a proper high-level language - the language of partial recursive functions. In this paper we present the implementation of a compiler that directly translates high-level Turing machine programs to Artificial Recursive Neural Networks. The application contains a simulator that can be used to test the resulting networks. We also argue that experiments like this compiler may give us clues on procedures for automatic synthesis of Artificial Recursive Neural Networks from high-level descriptions	por
dc.identifier.uri	http://hdl.handle.net/10451/14240	por
dc.identifier.uri	http://repositorio.ul.pt/handle/10455/2941	por
dc.language.iso	por	por
dc.publisher	Department of Informatics, University of Lisbon	por
dc.relation.ispartofseries	di-fcul-tr-98-8	por
dc.subject		por
dc.title	Building a Neural Computer	por
dc.type	report
dspace.entity.type	Publication
rcaap.rights	openAccess	por
rcaap.type	report	por

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