A task-oriented controller to manage learning robots was described in Callataÿ (1986). The main processing element is a phasic categorizer, the output of which is an instantiated symbol transmitted to many other categorizers. Categorizers are the nodes of a directed circular network processor system (DNPS). The impulses (instantiated symbols) concurrently flow within the DNPS loops. The system is radically different from conventional computers and parallel processors. Memory consists of relationships which are stored in the processing elements: these are "recording content addressable memories" (RCAM) which can do only one machine instruction type: "association for production rule processing". A well-designed DNPS can resolve complex problems, like some rule based or logic programs. Predicates do not appear in the compiled programs. They are replaced by instantiable keywords. Production rules are represented like documents described with keywords and are processed by an information retrieval algorithm. Processing may continue even when no rule matches because analogies may be discovered by partial matching and produce a relevant pseudo-logical solution from incomplete knowledge. The active working memory is matched in parallel with the whole rule base. The DNPS controller is suggested as the central part of a brain model. © 1988.