A compiler and simulator for partial recursive functions over neural networks

نویسندگان

  • João Pedro Neto
  • José Félix Costa
  • Paulo Carreira
  • Miguel Rosa
چکیده

Abstract. It was shown that Artificial Recurrent Neural Networks have the same computing power as Turing machines (cf. [6,8]). 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 these experiments provide clues to develop procedures for automatic synthesis of Neural Networks from high-level descriptions.

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تاریخ انتشار 2001