@ARTICLE{MR1136617,
   AUTHOR       = {H. T. Siegelmann and E.D. Sontag},
   JOURNAL      = {Appl. Math. Lett.},
   TITLE        = {Turing computability with neural nets},
   YEAR         = {1991},
   OPTMONTH     = {},
   OPTNOTE      = {},
   NUMBER       = {6},
   PAGES        = {77--80},
   VOLUME       = {4},
   KEYWORDS     = {neural networks, computational complexity, 
      recurrent neural networks},
   PDF          = {../../FTPDIR/aml-turing.pdf},
   ABSTRACT     = { This paper shows the existence of a finite neural 
      network, made up of sigmoidal neurons, which simulates a universal 
      Turing machine. It is composed of less than 100,000 synchronously 
      evolving processors, interconnected linearly. High-order connections 
      are not required. (Note: this paper was placed here by special 
      request. The results in this paper have been by now improved 
      considerably: see the JCSS pape which among other aspects provides a 
      polynomial time simulation. This paper, based on a unary encoding, 
      results in an exponential slowdown). }
}

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