The relationship between symbolism and connectionism has been one of the major issues in recent artificial intelligence research. An increasing number of researchers from each side have tried to adopt the desirable characteristics of the approach. A major open question in this field is the extent to which a connectionist architecture can accommodate basic concepts of symbolic inference, such as a dynamic variable binding mechanism and a rule and fact encoding mechanism involving nary predicates. One of the current leaders in this area is the connectionist rule-based system proposed by Shastri and Ajjanagadde. The paper demonstrates that the mechanism for variable binding which they advocate is fundamentally limited, and it shows how a reinterpretation of the primitive components and corresponding modifications of their system can extend the range of inference which can be supported. Our extension hinges on the basic structural modification of the network components and further modifications of the rule and fact encoding mechanism. These modifications allow the extended model to have more expressive power in dealing with symbolic knowledge as in the unification of terms across many groups of unifying arguments.