This feature is useful to represent systems where the delays can strongly modify the state, such as the memorization process.

Fourth IEEE Conference on Cognitive Informatics, 2005. (ICCI 2005).

In this paper we use the graphical formalism timed-arc Petri nets to specify cognitive systems. In addition to the usual characteristics of this formalism, this timed extension of PNs features the possibility of capturing the facts of enabling/disabling a transition as a consequence of the time elapsed. This feature is useful to represent systems where the delays can strongly modify the state, such as the memorization process. In order to illustrate the suitability of this model we formally represent a cognitive model of the memory. This process includes the appropriate characteristics to show the advantages of having a very natural conception of concurrency as well as, how time can elapse and sometimes go beyond the threshold which determines if a perception goes into the long-term memory or not. The memory structure/working assumed can be better studied in Squire et al. (1993), Solso (1999), and Wang and Wang (2002).

This feature is useful to represent systems where the delays can strongly modify the state, such as the memorization process.

How does the memory work? By timed-arc Petri nets