paper available

[Christian Omlin]

Following the announcement of the paper by Wolfgang Maas on the 
computational power of networks consisting of neurons that communicate 
via spike trains, we thought the following paper may be of interest to 
the connectionist community.

It can be retrieved from the website

        http://www.neci.nj.nec.com/homepages/omlin/omlin.html

We welcome your comments.

 -Christian

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                   Training Recurrent Neural Networks  
                     with Temporal Input Encodings 

           C.W. Omlin (a,b), C.L. Giles (a,c), B.G. Horne (a)
                      L.R. Leerink (d), T. Lin (a)

                     (a)   NEC Research Institute
                             4 Independence Way 
                             Princeton, NJ 08540 

                     (b)      CS Department 
                                   RPI
                            Troy,  NY  12180  

                     (c)         UMIACS
                          University of Maryland
                          College Park, MD 20742

                     (d)      EE Department
                         The University of Sydney
                             Sydney, NSW 2006



                               Abstract

    We  investigate  the  learning  of   deterministic   finite-state
    automata  (DFA's)  with  recurrent  networks  with a single input
    neuron, where each input symbol  is  represented  as  a  temporal
    pattern  and  strings  as  sequences  of  temporal  patterns.  We
    empirically demonstrate that obvious temporal encodings can  make
    learning very difficult or even impossible.  Based on preliminary
    results, we formulate some  hypotheses  about  increase  training
    time  compared  to  training  of  networks  with  multiple  input
    neurons.