In this section the main principles and technical (programming) solutions are discussed.
The simulator is based on SystemC [2]. On one side, it is class library, otherwise purely C++. On the other side, it provides and OS independent engine, which is very useful in simulating processes happening on quite different timescales, from femtoseconds in physical processes to year millions in geology. The major point is that it separates the simulated time from the actual computer utilization time. The simulated time is defined in the program, and during the operation, the processes happen exactly at the expected simulation times. The processes are started by special signals (called events) and they send another signal when they finished the calulation, enabling synchronizing the processes to each other.
The time is considered as quasi-continuous: there are short time periods within which the events "happen az the same time". The simulator provides an engine which schedules the events in a proper way, and the takes the events in the order as they follow, maxbe several events "at the same time". At any time periods, is is a little falsification that the events happen "at the same time"; there are event that happen "at zero time later", just to keep logical dependency. This is hows time awareness can be implemented in the time-unaware paradigm.
The need for having events to occur "at the same time, but at zero time later" originates from that in reality, in contrast with the assumption of "instant interaction", some transfer time is needed from one computing object to the other. Our simulator extends this handling, and introduces an artificial delay. According to that, the results are send only at a later time (corresponding to the physical distance of the objects and signal's propagation speed). Given that signal propagation speed in biology is several millions lower than that in electronic technology [9], this delay leads to significant difference in the operation of neuronal objects, both inside neurons and especially in neuronal networks [8]. Among others, the temporal behavior explains how information is stored and how short-time and life-long learning works.
1.8.17 
