Merging Sub-symbolic and Symbolic Computation

In a recent short paper and a report (see [Neto et al. 98] and [Neto and Costa 99]) it was shown that programming languages can be translated efficiently on recurrent (analog, rational weighted) neural nets, using bounded resources. This fact was achieved by creating a neural programming language called NETDEF, such that each program corresponds to a modular neural net that computes it. This framework has some practical implications in recent efforts to merge symbolic and subsymbolic computation. Adding neuron-synapse connections (high-order neurons) to the neural network model allows us to integrate learning into the NETDEF computing paradigm. Some possible enhancements are presented using this framework, namely structure selfmodification, and integration of sub-symbolic learning into the NETDEF neuron architecture. The Hebb learning rule is used to provide an illustrative example.